I'm currently being legally abused and intimidated into silence by a former employer. This has been going on for a while now and it simply cannot live in darkness for another day. But I am asking for your help to fight back. I signed this separation agreement, an NDA, containing a strict and very broad non-disparagement clause many years ago. I voluntarily left my job after deciding I could no longer put myself or my growing family through the toxic and abusive work environment I had endured for years. This place was and is to this day a workplace rife with sexual misconduct, degeneracy, and aggression. The things I saw, the things done to me, and the things I witnessed my employer do to others were disgusting, shocking, and utterly indefensible. I have the receipts. This all took a serious toll on my personal health to the point near the end of my tenure. The work environment had become so toxic that I had to be admitted into a heart hospital. After many tests, I was ultimately put on anti-anxiety medication. This condition was so canoeing to me. I know history of it prior to my employment there and have never been treated for it since. You just heard from Jared Monroe, known as NotGay Jared on Steven Crowder's YouTube show Later With Crowder, and he has now set up a GoFundMe to help him cover the cost of legal fees that he accrued due to years and years of legal intimidation from his former employer, Steven Crowder. Now in a 15 minute long video, Jared alleges that he was coerced into signing a strict NDA and non-compete that he didn't want to sign once he announced that he was leaving the company.  Now he quickly learned that leaving wasn't so easy once he announced that he was resigning because he found himself in a perpetual state of law affair against Crowder and he claims that he's not the only ex-crowder employee who's been subjected to the same type of legal intimidation and he also alleges that Crowder is doing all of this to his ex-employees so that way they don't spill the tea on his double life in secrets. That's Jared's words, not mine, we'll get to the video in a moment. Now he makes it very clear that he has receipts to prove all of this, hence why Crowder is trying so hard to silence him. Now I do believe Jared because he's not the first ex-employee of Crowder's to speak out about the uncomfortable position that Crowder often put them in. For example, Owen Benjamin alleges that Crowder would make his employees dress up under the guise of making fun of transgender people and explains, quote, How was dressing like degenerate fruit cakes owning the left? I agreed so I'm no victim but I felt very uncomfortable saying no and I didn't want to let down the team. Over time I realized more and more that we weren't owning anyone and I just kept being asked to wear really degrading and sexualizing outfits. Now this has been Crowder's ammo for a while, I mean he'll find any excuse to dress and drag under the pretense of doing comedy or owning the libs or making fun of LGBTQ plus people especially trans people when it's painfully obvious that he just wants to dress in women's clothing because he feels more comfortable wearing women's clothing and of course there's nothing inherently wrong with him doing that aside from the transphobia that he's espousing but his employees very clearly didn't feel as comfortable as he did in drag as evidenced by what they've said after leaving the company and with respect to Jared the surf shared screenshots from a skit that he did with Crowder where Crowder made them both touch each other and even made out while both of them were in drag all under the pretense of making fun of trans people and not to mention there's also this video. Now Stephen Crowder chose to post that video to his public Twitter account on his own volition I'm not joking about this now I couldn't find the original so he might have deleted the tweet but that is a video of him with his own employee and it's funny how all of his comedy skits involve him and his employees dressing in a sexual manner right I mean it's a bit of a tell is it not now if that's the kind of stuff that Steven Crowder was sharing publicly we don't even have to imagine how much worse it probably was behind the scenes and I say we don't have to imagine that because we know how bad it was in fact in an expose published in the New York intelligence or journalist Matt Steve reported that Steven Crowder allegedly sexually harassed his own male employees writing in March of 2018 Crowder and his crew were driving in a van when a former producer he liked to call not gay Jared fell asleep in the back row Steven was in front and he was joking about what he was going to do a witness said he climbed over and dropped his junk on top of Jared's shoulder the same ex-staffer recall that Crowder had exposed himself to Jared in 2017 while they were filming a parody version of ghost and on a flight in 2018 a different employee claims that they saw Crowder put his testicles on his childhood friend and assistant John Goodman another employee remembered that Crowder had showed his gentles to Dave Landau a comedian and former co-host who called Crowder a bully last week now they say last week but this is an article from last year now they add Landau claimed that Crowder installed a Dave don't talk button on the show to get him to be quiet on air quote at first I took it as him trying to be friendly or one of the guys said an ex-staffer now I see it was a power play Crowder allegedly sent production assistants to do his laundry and could be an unreasonable micro manager who would make wild requests after hours to set people up for failure ex-staffers claim that he would regularly berate his team and threaten to fire people on the company's discord channel he even went after his own father Darren Crowder per one source who claimed Crowder would yell at his dad in front of employees when Darren was working as his son's booker so this man allegedly abused his employees and created a hostile work environment and now he's trying to silence them in order to keep a lid on the abuse that we don't yet know about but Stephen Crowder isn't just waging lawfare against his former employees because in response to Jared's video Candace Owens actually spoke out and accused him of doing the same thing to his wife writing wait till they find out what he's been doing to his wife I never edited my opinion on Stephen Crowder because I knew that everything would eventually be revealed this really is going to be a year of revelations now even though Candace Owens isn't necessarily the most reliable source it's very plausible that she's correct here and that he is indeed subjecting his wife to the same lawfare as his former employees because as mediaeid explains Monroe is in the only one who has been subjected to legal action from Crowder in October his company sued his estranged wife Hilary Crowder for torturous interference after her family accused him of mentally and emotionally abusive behavior in a statement to the journalist Yasha Ali and that emotionally abusive behavior that they were talking about was exposed in ring-camera footage where he was vocally abusing his very pregnant wife so he is using his wealth to crush anyone who dares to defy him and with respect to Jared it's evident that Crowder was maliciously just trying to destroy his life as evidenced by the following details shared by Jared all I simply wanted to do was peacefully leave it was clear by my employer though that this was not only unacceptable but that I needed to be punished for doing so being bullied on each of these terms and many more fighting for my basic freedoms to leave and provide for my family immediately sent me into legal debt I did not want to sign anything mind you but the negotiations with my former employer left no question that without a signed NDEA my guaranteed silence I would undoubtedly be harassed well into the future the primary reason I signed the NDEA was for a small carve-out in the non-compete clause which allowed me to freely seek employment using some of my skill sets at another specific company I'll use that to feed my family that provision however was a lie and upon starting my new job at the new said company I was giving another one of these a cease and assist and uh was promptly unlawfully terminated from that that position I did thousands more to my legal debt trying to fight back my wife was very pregnant by this time and without a financial safety net for groceries and a baby crib much less a legal fund to file a countersuit I simply had to let it go trusting God had a plan Crowder actually got him fired from his new job even though he had a baby on the way he was just trying to move on take care of his family sought other employment and Crowder intervened for no good reason to get him fired now Jared signed the non-compete but what trade secrets specifically is Jared going to share with other companies right I don't know what the job was but assuming he was working in media or had a media company like what is he going to expose how to do segments where you dress in drag and be overly racist like what exactly is the purpose of this non-compete and it's all about control that's what it feels like and Jared goes on to explain that Crowder is able to legally intimidate employees into silence because he has virtually unlimited funds to do so and even if the terms of these NDAs don't hold up in the court of law which I don't know but even if they don't simply challenging them in and of itself is very costly for people like Jared who don't have the power and wealth that Steven Crowder has but since Jared did challenge them and refused to comply with any of Crowder's legal threats well his life has basically been ruined and this was all because he wanted to just leave a job because his boss was terrible toxic abusive he wanted to leave but Crowder wouldn't let him and to make matters even worse Crowder made Jared turn over his own personal equipment and even made him turn over his personal Twitter account that he had before he started working for Crowder because he claimed that that was the intellectual property of the company so there is so much here so much that is really damaging for Crowder and it's evident why he didn't want his employees to speak out because this man was a tyrant and it just kind of goes to show you how emboldened he felt because he has millions of dollars and a team of lawyers on his side but I've got one more video of Jared while my supposed not compete expired after two years unfortunately the most egregious part of my NDA the part that silences my free speech has no expiration while NDAs are classically used to protect protect trade secrets unfortunately in the entertainment industry they are too often used to protect disgusting unlawful activity in many forms of abuse we see examples of this all the time powerful people and their attorneys routinely use NDAs to silence victims in order to remain powerful people let me be clear what I'm afraid of is legal harassment and perpetuity not the truth only one side of this ordeal is spending a fortune to hide truth and punish anyone who dares speak up for themselves or other victims I'm asking for donations for two reasons number one obviously to help with the financial burden I've already accrued with this legal matter right here that's taking care of that matter is priority priority number one as I'm going further and further into debt with each passing day the second reason I'm asking for our financial assistant though assistance is to proactively address my unconstitutional anti free speech NDA which is the basis for all of their claims against me the simplest resolution here would be for max employer to release me from the NDA give me and my fellow ex-employees the same free speech they publicly champion for every other American but I can almost guarantee you that will not happen I've already witnessed them spend unfathomable amounts of money in other cases to protect their double life and secrets I mean if that NDA is gone and Jared feels free to speak you know there's going to be some pretty damaging stuff to Crowder's credibility there I mean Jared literally said that Crowder has already spent unfathomable amounts of money in his case and in other cases to protect his double life and secrets so why would he let up now and in response to Jared's video the CEO of Crowder's company Gerald Morgan has basically said yeah we're not gonna let up anytime soon quote we will not be intimidated by media smear tactics mr. Monroe entered into an agreement with lotter with Crowder that he knowingly violated after discovering repeated violations lotter with Crowder was forced to take legal action the trial court has denied mr. Monroe's effort to avoid the legal proceedings which are moving forward pursuant to the court's order in other words they're not denying the fact that they've been putting Jared through hell all this time in fact they're defending it now keep in mind that Jared hasn't been with the company for more than five years at this point and the fact that he is still forced to go through all of this tells you that Stephen Crowder is willing to go to great lengths to protect the public from learning about whatever Jared knows and it shows you also how tyrannical Stephen Crowder is but i mean even without this information to be clear from his wife and his employees i think it was already abundantly clear that Crowder was a terrible human being based on despicable dehumanizing rhetoric that he used against marginalized people on his own show i mean before we learned about the alleged harassment from his own employees we saw him publicly harass queer creators like Carlos Mazza for years so i'm not surprised that someone who exclusively punches down in public is also punching down in private as well none of us should be so in conclusion when somebody tells you who they are you should believe them


Welcome back everyone! We'll start the second session. Our next speaker is Mr. Josh Bet. Josh is a research associate in the Biostatic Center at Johns Hopkins School of Public Health and the director of Analyst Corps in the Cochlear Center for Hearing and Public Health. Today, Josh will be discussing how the analytic approach to data from nationally represented surveys such as NHANES may differ from the approaches taught in Statistic Social. Josh? Thank you very much, Jungjoo, and thank you for the invitation and kind introduction. And thank you all to my previous panelists for setting up this talk. So I'm going to be talking about broadly about analyzing data from complex surveys using NHANES as a particular example. So first, what I'm going to do is review the statistical models that hopefully you all know and love before we go into what makes these complex survey designs different. We'll talk about what features might be included in the survey design and how they need to be adjusted for in the analysis of data. So we're going to talk about what these survey weights actually mean, when and how should we use weights. And for the most part, I'm going to stick to the concepts behind design-based inference or the analysis of survey data, focusing on the what and the why, not the how, because there's a lot of excellent resources I'll point you to for actually carrying out these analyses in SAS data or R. So as George Box famously said that, remember that all models are wrong. The practical question is how wrong do they have to be to not be useful? So whenever we're going beyond the data at hand and performing statistical inference, there's some statistical model in play. And statistical models are like other types of models that you may have encountered in practice. They're useful fictions where we strip away the complexities of real life in order to get a tractable solution that hopefully still allows us to do some important work. So you may have heard of in physics, there's like ideal gases and point masses and things like that. None of these things actually exist in reality, but that doesn't stop us from still using them to solve important problems. There's a joke that, you know, an engineer will assume that a horse is a perfect sphere if it makes the math easier. Well, statisticians are the same way, is that we use statistical models. They may have some kind of silly or unrealistic sounding of assumptions, but still in many cases they can give us useful information, but it's important to remember when those assumptions might be violated and give us results that are not useful. And the power of statistical models are based on the rules of probability. So what do I mean by probability? Here I'm going to be talking about frequency probability, which talks about the relative frequency of an outcome and a large number of experiments. This could be a toy example like taking a coin and tossing it 10 times and counting the number of times that the coin comes up heads. Not very useful scientifically, but this also covers things like taking a random sample and then, you know, measuring height, weight, blood pressure and environmental exposures. So frequency probability is something that we can empirically assess.  And it has its basis in, for example, the physical properties of things. Is this a fair coin? Is this a loaded die? Or the properties of measurements? And this is something distinct from something called belief probability, which is based on the degree of belief based on evidence. This is connected to Bayesian statistics. And we won't be talking further about that, but there are a lot of confusions about statistics that come from this frequency probability versus belief probability distinction. So that reference at the bottom, the Ian Hackings book is an excellent book for learning about probability.  So if somebody talks about the probability that a team is going to win a football game or something, they're not imagining multiple replications of a football game and counting how many of those times a particular team won. So here's an example of a probability model in practice, the binomial model. So in a statistical model, we're trying to learn about some feature of a population that we're going to call a parameter. And for a binomial model, we're looking at a binary outcome. So the parameter or the feature of interest is the proportion or the probability or prevalence, whatever you want to call it. So if we know the population prevalence is, let's say, 20% like this binomial model on the right, and we take a sample size of 100 individuals and look at the sample prevalence. But even though the population prevalence is 20%, you may see a sample prevalence as high as 30%, or potentially as low as 10%. So the binomial model tells us the probability of getting a particular sample proportion based on the population proportion. So this is useful whenever we've got a fixed sample size that's set in advance. So collecting data on 100 individuals, that the outcomes are binary, that they can be, let's say, heads or tails on a coin flip. The coin can't land on its side. And that the outcomes are independent, that the outcome of one coin toss doesn't influence the outcome on another coin toss. And that they all have the same probability or prevalence in the population. But the beauty of these models is that they can be applied whenever these situations are met. So it could be we're sampling from a population, we're looking at the prevalence of a particular phenomenon. You may see this as one event referred to as a success, but that largely comes from kind of the games of chance origin of probability. So success is just the event of interest. It could be a good thing like survive versus not, or it could be a bad thing like malignant versus benign. And so you can see as the sample size gets larger and larger, the probability of getting a particular sample prevalence that's, let's say, 30% when the true population is prevalence is 20%, gets less and less as the sample size becomes large. But usually we're in the world of statistics that we have, let's say, 100 observations and we've observed 28 prevalent cases in those 100 observations. And we wanna know, well, did this come from a population where the prevalence is 19% and maybe it's an overestimate of the population prevalence? Or maybe it came from a population where the prevalence is 38% and maybe it's an underestimate of the true population prevalence. Or maybe it's actually came from a population where the true prevalence was 28%. So this gets at the idea of maximum likelihood that you've probably encountered in practice. So the binomial model tells us if we've seen a certain number of successes out of a certain number of trials or a certain number of prevalent cases out of a certain number of individuals, we can get a point estimate, which tells us the best estimate of what's the prevalence in the population. As well as its standard error is how precisely do we know this? Is this within a margin of error of 10% or 5% or 1%? And we need this because there's inherently going to be sampling variability that we're taking a random sample from a population and the sample is going to imperfectly reflect that population. So we need to take into account the precision of the measurement itself. The thing is we only get one of those samples from the population, typically. And so we use the statistical model of the sampling distribution, like the binomial distribution, to infer from a particular experiment to a particular population. So here's an excellent reference that gives a visual and interactive approach to concepts and probability and statistics. So in confidence intervals are something that's often misunderstood but are incredibly useful because it tells us what are values of the population feature, such as the population prevalence, that are most consistent with the sample we've observed taking into account uncertainty due to sampling variability. So over on the right, you see 100 studies, each of whom involves sampling 100 individuals from a population where the prevalence is 20%. And you can see the population prevalence is indicated by that vertical line. And because each sample is going to vary from the population proportion, we want to know what is our values that are consistent, values of the population prevalence consistent with the data we have. And you can see that in the long run of studies, if we were to consider a large number of them, when applied correctly, confidence intervals, for example, a 95% confidence interval will correctly cover the population prevalence 95% of the time. But this is 95% confidence, not 95% probability. Because once we actually have calculated that interval, it's no longer random in the frequency sense. Remember, thinking about sampling repeatedly from a population. And many people find this counterintuitive and not very satisfying. Because once we calculate it, either the interval covers the parameter or it doesn't. We just don't know which is which. And in order to kind of clarify this, think about like a lottery ticket. If you go to the store and buy a scratch off ticket and it advertises one in a thousand will win. What they mean by that is if you repeatedly buy many, many tickets on average, one in a thousand of those will be winners. But it's only random until you actually choose a scratch off. Once you've bought it, you either won or lost. You don't know it yet. So that's the difference between probability and confidence. So in the binomial statistical model, we're assuming that data are generated from some random process. And by using statistics to infer about the features of that population or the features that govern that process, we can generalize to wherever that process is in play. But there's many sorts of questions that these sorts of models can't give really a satisfying answer to, such as how many people in a particular population or subgroup have a risk factor or disease. You know, these are infinite population models that doesn't necessarily give us a satisfying answer to those questions. So we need a different framework for official administrative or vital statistics. And this is called design based inference, as opposed to what you may have been taught about that the model based inference approach. So in design based statistics, we're sampling from an instead of sampling from an infinite population, we're sampling from a finite population, such as non institutionalized individuals in the US. And in this setting, we consider our data are fixed. Everybody in the US has a height, a weight, a BMI, and so on and so forth. But the selection and participation and observation of that data are where randomness comes into play. So the goal of design based inference is to infer about counts, means and proportions in this finite population. We're not trying to, you know, use the US to infer about some other population. And in design based inference, we're trying to learn about what would the population parameter values be, means, proportions, totals and so on and so forth. If we were to observe everybody in the population. So features of these designs include probability samples where individuals have a known non zero probability of selection. We may divide up a region into different clusters and randomly sample these clusters. And that makes it more feasible to carry out these surveys and then do stratification where we divide up the population into more homogenous groups and then sub sample those, those strata. And this allows us to do as others have talked about over sampling so we can ensure adequate representation and studies in order to give stable or precise estimates. And importantly, if we ignore that the data were generated in this way, this can result in serious bias in our estimates and our standard errors. So not only can it give us the wrong answer, it often makes us more confident in those wrong answers. So a simple random sampling is usually what's assumed in the world of model based statistics where any sample size of a given, any sample of size and is equally likely. But think about it, if we were to try to do that in the US would be going back and forth coast to coast to try and cover a simple random sample. It's just not feasible. So that's why we have multi-stage sampling designs. So where we sample counties and then segments of counties then households within these segments and then individuals within households. And stratification, the reason we do that is it gives us greater precision of estimates, especially when the variability between the strata are large and the variability within the strata is small. So that translates to what might be called a high intra-class correlation coefficient. And when the strata membership is correlated with the outcome, this can actually give us a big boost in the precision of our estimates. So survey weights, this is something that always comes up in discussion. Well, what do these actually mean? So I talked about how everybody's got a non-zero selection probability. And that means that each person represents a certain fragment of the population. So for example, if we've got 35 million people in our population and we do a simple random sample of 3,500 individuals, everybody has an equal probability of selection and that probability of selection is one in 10,000. So that means that each individual in the sample represents 10,000 people in the population. So if we have 400 of those 3,500 individuals have hypertension in our sample, we'd estimate that 4 million cases of hypertension in our particular population. So this is called the Horvitz-Thompson estimator of the population totals. And we can get the mean by dividing by the sample size. But we only know the probability of selection at the beginning. What if people don't respond, or they respond but they refuse parts of the data collection, or let's say there's a loss of data once it's collected. We need to estimate what is the propensity of responding, and that's not something that we can know in advance. And all of the estimates that we make are going to make some assumptions about the nature of missing data. So if we want nationally representative estimates, we need to incorporate these weights because they tell us about over-sampling and non-response. And weighted estimates are typically going to give us higher standard errors, which translates to lower precision. But there's kind of a tug of war with the effect of stratification. So stratification can allow us to gain precision. And cluster sampling, we actually lose a little bit of precision because of spatial correlation. So two people within the same zip code are going to be more similar to each other than two random people selected in the same state. And they're going to be more similar to each other than two random people selected from the entire country. And this leads to the design effect, which tells us about how precise our estimates are in this current design relative to the simple random sampling design. And design effects typically are much greater than one, indicating that if we assume simple random sampling, we're overestimating the precision. And so this relates to the concept of the effective sample size so that even though we have, let's say, 10,000 individuals in our sample, that may only be the equivalent information of, let's say, 8,000 individuals who were sampled at random. So Andrew Gelman opened a paper with the quote, Survey weighting is a mess. So what's actually in these weights? Well, it's not just the probability of selection. It's actually includes non-response adjustments. So what's the probability that people respond, given that they were selected, and then post stratification factors. So because of non-response and the random nature of sampling, we need to adjust these things so that things line up with population totals from, for example, census data. And that's what post stratification is about, to make sure things are representative. So you need to know which weights to use. Read the documentation. It varies by study question and by the study you're looking at. In some studies, you may have multi-level data, such as, let's say, states. And then within states, you sample counties. And within counties, you sampled schools. In schools, you sampled students. There may be weights at multiple levels of the data. And there may be separate weights for, for example, longitudinal studies versus cross-sectional studies within the same survey. And other kinds of weights exist in software. So just because there's a weight argument in your software, doesn't mean that it handles survey weights. So read the documentation carefully, because those weights may be frequency weights, which basically tell the software to make multiple copies of that observation. Or there may be precision weights, which are related to the variance of the observation for those that are familiar with, like, weight of least squares. So rates of participation vary within the population, so we need to adjust for non-response. And the greater the degree of that non-response, the greater the potential for bias, and the more important the assumptions are in our analyses. So when talking about missing data, we talk about missing data mechanisms. So missing completely at random means that the missingness is unrelated to both the observed and unobserved factors about the individual. So this may occur in a random sub-sample. Let's say something is too costly or invasive to do in everybody. We may only do it in a sub-sample, but because that sub-sample now needs to represent everybody in the population, we need to give them higher weights in order to accomplish that. So make sure that you know which weights to use for a particular outcome. So missing at random means it may be related to things that we observe, but not things that we don't observe. So let's say you're surveying an area and one person has a device failure. So that may be related to the area in which you're actually collecting the data, but because the failure of the device doesn't care about the characteristics of the people or their health status, that might be a missing at random outcome. Then there's not missing at random, which is the sticky situation where maybe people aren't participating or responding because of some characteristic that you don't observe. Like let's say they've got a health condition and are afraid of disclosing it due to stigma. So when they use weights, you should definitely use them if you want nationally representative estimates of totals and means. But as other people hinted at that, this is more complicated for regression models because when you do a model-based approach to survey data, you're assuming that selection is ignorable, that participation is independent of the outcome given what's included in the model. So if you've got in your model all of the features that were included in the sampling design you might still get estimates that are okay. So the problem is it not all data going into the weights is publicly available due to disclosure risk. There also may be interactions and other features that are modeled in the survey weights. So you have to think carefully about this. But the thing is weighted estimates give us lower precision, so it's higher standard errors. So it may be advantageous sometimes not to use weights. But this is, at the very least, you should be comparing the weighted and unweighted estimates for qualitative differences in the estimates and the standard errors. There's an excellent example of this in the literature. But even if you're not using the weights, you still need to account for the geographical features like clustering because of correlation. Again, otherwise you're going to underestimate your standard errors and overstate your precision. But the intra-class correlation is going to depend on the outcome. And the, have a larger effect when things are more spatially correlated. So just to kind of sum up, the model-based approach is when you're, you know, sampling from an infinite hypothetical population and you want to learn about some data-generating mechanism and generalize that to wherever that data-generating process might similarly hold. And the randomness comes from this random process. But design-based inference, we're sampling from a fixed finite population that we want to characterize. And data exists but are randomly observed. Okay? So just to kind of sum things up, design-based inference is something that's very different than what you may have encountered. It's got different aims, different languages and different tools. So be aware of them. Know the survey and the instruments inside and out. The documentation is very comprehensive and very useful. So read it not just once, but twice before beginning your analysis. I would advise people to stick to recommendations that a lot of thought have gone into those by methodologists. So I'd follow those unless you've got a really good reason not to. Make a use of the tutorials generously provided by NCHS and others. And then for all of you who are reviewing papers, I have some homework for you. I'd like you to review those papers very critically and pay attention to the methods. Because if they don't mention anything about the complex survey design, chances are they didn't properly account for that. It's not a mistake that those who know better would have, I have made. So hopefully I have kept us to time. I'm happy to take any questions if you have them. Let me leave up my references just for a second. All right. Are there any questions I can take? So I don't see any questions in the queue. I wanted to open it up to any of the panelists who might have questions. So some common questions that I get as a statistician is like, what if my software package of choice doesn't handle survey weights or the routine in my statistical package doesn't handle survey weights? I would, number one, advise collaborating with a statistician. We're generally friendly people. We are very helpful when it comes to analyzing and interpreting data. We can help point you to the right statistical package. And for complex things like, let's say, structural equation modeling and multiple imputation, other things like that, if you thought these were complicated in and of themselves when you bring surveys into the mix, they become even more complicated. So if, for example, you're analyzing longitudinal data and you can't find a routine for analyzing longitudinal survey data, I would highly recommend collaborating with a statistician who can advise you on the appropriate software and ways to use that software, because there's a lot of ways in which things can go wrong. So a couple of questions have come in from participants, which I think kind of get a little more into the nuts and bolts of analysis. So one relates to, I'm struggling to find survey procedures to test correlations, particularly spearman correlations in SAS. Could you recommend any resources or software? So you'll see in the further reading and resources, there's a couple of links there. The first is the website that goes with the first book on the references that, Hearing a West in Burglund. There's an excellent examples in all sorts of statistical packages there. Hopefully that should answer your question. I am not a SAS expert per se, but SAS data in R, for the most part, they can all handle the types of analysis you usually do for things that are more complex, like let's say longitudinal data, structural equation modeling, or more fancy stuff, there you have less choices. And for that one, I would check out the Lumley and Scott reference, which is an excellent overview in terms of software and then modeling. And then the UCLA site is highly recommended. They've got a lot of information on not just model-based statistical approaches, but design-based statistical approaches with worked examples as well. So here's another question, which is sort of scenario-based, although it sort of is both general and then has reference to a specific scenario. So question states, I understand the most accurate way to work with survey data is to use the entire data set available. And then specify your subpopulation of interest using the subpop command so as to get correct standard errors. But the question is, are there exceptions to this? And then this sort of goes into a specific scenario. For example, if we want to limit to people who had Medicare coverage for a certain time period, say the first six months of 2016, wouldn't we want the standard errors to only be estimated within the subsample? Or will we still want to use the whole sample? So this is where you use the subpopulation command, because this is the special area of what's called domain estimation, so estimating within subsets of the population. And you might sometimes get appropriate estimates, but if you don't account for how the subpopulation, you're not going to get appropriate standard errors. So as to actually getting into specifics, I think that's a little bit beyond the scope, but I would recommend looking into the, the Haringale West and Berglund book. It's a fantastic resource that it covers a broad range of statistical packages and particular applications, including domain estimation. Okay. And then we have a series of questions all related to weighting. So after conducting both weighted and unweighted analyses, how can we decide which set of results to report? So that's an excellent question. So number one is that there's a lot of people confuse statistics with a toolbox of tests. So make sure that when you're comparing results, the most useful thing is not usually a statistical test. It's usually a more qualitative look at what are the estimates and what are the standard errors and what are the differences and pay particularly close attention to things that are related to the way in which the data was collected. So since for example, NHANES samples differently according to race, ethnicity and age and other factors, you want to pay close attention particularly to those factors in your model when you're actually comparing weighted and unweighted analyses. So the Lumley and Scott example has an excellent kind of side-by-side of a case in which the unweighted approach may be okay versus where a unweighted approach probably is going to lead to bias. Thank you. And so somewhat related. So the question is if we have the waiting for the whole survey, and then they applied inclusion exclusion criteria to change and resulting in a change sample size, should we use the same strata cluster and weight as in the original whole survey? So I would highly recommend following the analysis guidelines and NCHS has done a lot of work in putting all these things together and they've got worked examples of how to do this in all major statistical packages. So it'll show you how to appropriately estimate, let's say sub-population estimates or sub-samples of the survey. So Josh, I'm sorry to interrupt. Josh, why are you talking to me?


In this video I want to do some more computations of inverse functions, algebraically speaking. Consider the function f of x is given as 2 over x minus 3 and we add to that 4. The basic strategy is going to be the same, we start off with the relationship that gives the function f. We're going to replace the f of x with a y here, y equals 2 over x minus 3 plus 4 and then we proceed to swap the roles of y and x in that formula and this will give us a formula for f inverse. We're going to get x equals 2 over y minus 3 plus 4 and now we're going to proceed to do inverse operations until we can successfully solve for the y all by itself. So the first thing we do kind of reversing of order operations we're going to minus 4 from both sides that cancels the 4 on the left hand side and that would give us 2 over y minus 3 is equal to x minus 4. Now the left hand side is just a fraction right? We want to get rid of the y from the denominator. We have to kind of free from its prison, it's trapped inside of the fraction right there. One can accomplish that by a couple different techniques. In this situation I'm going to kind of treat the x minus 4 as a fraction x minus 4 over 1. That way I can cross multiply. You multiply the 1 by the 2 and you're going to multiply the y minus 3 by the x minus 4 and if we do that that will then give us 2 times 1 is equal to x minus 4 times y minus 3 like so and so 2 times 1 is pretty simple here. Now one has to be kind of careful on the on the next part right? What we don't want to do what we don't want to do is start spreading out the y's too far right? So it might be tempting to like foil out the the right hand side so you take x times y, x times negative 3, 4 times y, 4 times 3 there.  This is problematic for us because now there are two y's inside of the equation. We want one y right? We want it y equals so spreading out the y's is not going to help us. We want to keep them gathered in so instead of foiling the right hand side because our goal is to solve for this y right here instead look at the expression x minus 4 and pretend like that's the coefficient of this expression right here y minus 3. Divide both sides by x minus 4 and you have to do that to the other side as well x minus 4. Then you'll see that the the y you'll get a y minus 3 from the from the right hand side then you get 2 over x minus 4. Whoops minus 4 like so and now in this situation you'll notice we still have y all by it's I mean y is all connected together we still got to give rid of this stuff that's attached to the y that's pretty simple just add three to both sides and we end up with in the final form y equals 2 over x minus 4 plus 3 and so now that we solve for y we're going to get rid of the y and put back in the name of the function this is f inverse of x which we have this thing right here and you might be tempted like well should I add the fractions together like 2 over x minus 4 plus 3 you could but I mean we should do it for a reason we shouldn't just do something because we think we ought to there should be a reason motivating why we do it and so for example like we had that choice between foiling this side or dividing by x minus 4 and we actually took a moment and stopped to think about what will happen if we do that there are consequences to doing things to the equation foiling actually caused us to get farther away from solving for y as opposed to division so we don't do things just to do them we do them to make things better right to make to make things more to better but we'll just we'll just keep without look make things better so I'm actually perfectly happy with writing the function this form because in this form I can very well see the transformations you shifted up by three you've vertically stretched it by two and you shifted it to the right by four if I would start adding the fractions together the graph transformations would be concealed let's take a look at another algebraic function this these things right here you have a fraction of a polynomial of so you have a polynomial on top polynomial on bottom this is what we call a rational function this type of rational function where you have a linear a linear polynomial on top and a linear polynomial on bottom this is often what we call a linear fractional and it turns out that linear fractionals are one-to-one functions and we can solve for this thing algebraically and then there's kind of two ways one could do it one you could actually do a little bit of polynomial division like you could take this 2x plus one divided by sorry 2x plus one and you divide it by x minus one if you do that you'll actually get something that looks very much like this function right here in which case then you could find its inverse the way we did just a moment ago and that's a little bit extra flavor you know sometimes a little bit more spicy than we want it to be and we haven't really talked about division so we're not going to do that right now but instead let's let's just try the approach we've done so far replace the f of x with a y you get 2x plus one over x minus one and so then when you switch to the inverse function you're going to every y becomes an x and every x becomes a y and something different happens this time we end up with actually multiple y's in the equation there's a y in the numerator and a y in the denominator this is actually the situation we were trying to avoid in the previous example this might be one advantage of doing polynomial division we we actually would only have by doing the division you're actually only going to get one x to start with as opposed to two but like I said there's a price to paying division and the price isn't necessarily worth it considering what we have to do now so our goals we want to solve for y but first to solve for y we have to combine the y's we want to combine the y's together and what we're going to do is we have to free the y's from prison it's like we have two members of the family that are separated by war a wall has now kept one in one land and the other in the other so they have to break free in order to do that we need to liberate y from its prison which we call the denominator for which we're going to multiply the right hand side by y minus one because multiplying by y minus one will cancel the division by my y minus one but what's good for the goose is good for the gander we have to do the same thing to the left hand side so that equality is preserved this then will give us two y plus one equals x times y minus one now what a slightly different predicament than we were a moment ago in this situation we have a y imprisoned right inside of these parentheses here uh the the guard keeper of course is the multiplication by x previously i told you not to multiply it out because that would spread out the y's on the other hand though since the y's are already spread out in order to combine them together we need to liberate them and that's not only going to happen if we distribute the x it almost seems like i'm giving you different advice in one situation i told you not to multiply it out but in a different situation i told you to multiply it out why this the important difference well again the point is on how we do things not because we're told to because there's a reason for it we didn't do it earlier because it's spread out the y's in this situation since the y's are already spread out in order to gather them together we have to free all of them so i would actually advise we multiply out the x you get x y minus x is equal to two y plus one you'll now notice that with all of the walls gone our y's are free to be reunited they can go back to their their missing family member right and so we have a x y on the right hand side what if we subtract two y from both sides that'll put all of the y's back together and what if we add x to both sides that way we put all of the multiples of y on the right hand side and everyone who's not a multiple of y will be on the left hand side uh the two y's will cancel the x's will cancel on the right hand side this then gives x plus one on the left hand side and this will give x y minus two y on the right hand side now if i was giving you something like five x minus x what would you do with something like that well you'd be like i'll combine like terms in which case you would get four x but why is it four x um why isn't it five x five times one why do you or why isn't it uh you know why isn't it seven you know why why'd you get a four right there and when you're probably like well okay you have a five right here you have a negative one right here you add together the coefficients and let me give you some explanation of why that's what we do you'll notice with five x and with x there's a common divisor we call it the gcd the greatest common divisor there's a common divisor of x and so we can factor out that common x which would then leave behind a five minus one times x notice if i redistribute the x through um you'll get five x minus x factoring is just the opposite of distribution you just pull out the thing that was multiplied beforehand and then you can see that once you have five minus one times x that'll then become a four x why am i mentioning that right now well because we're actually in the same situation when you look at this right here there's a y there's a y if you have x y minus two y how do you combine those together add together their coefficients which is the same thing as just factoring out the y uh factoring out the y you're going to end up with an x minus two times y now look at your equation the left hand side is currently x plus one the right hand side is currently x minus two times y you'll notice that there's only one y in the equation now hooray we have now solved for why we combined the y's together that's our first objective uh the next part is then once you have the y's together now you're going to isolate the y that is you want to get everything away from the y now the thing attached to the y is is an x minus two and it's attached to y via multiplication so we perform the inverse operation which is division here so the x minus twos will cancel and then you're going to get y equals x plus one over x minus two and this would be the inverse the inverse function of this linear fraction right here that might seem quite intense right it seems like there might be a lot going on there but it turns out um it turns out that we have all of all of the skills in front of us to do these type of calculations uh like if we go on to if we look at the next example here we have another function f of x equals four x minus one over two x plus three i can promise you that this calculation is actually quite methodical you do all the same steps each and every time although you know the numbers change a little bit so compare this example to the previous one the relationship for y is going to equal y equals four x minus one over two x plus three switch the rules of x and y as you transition to the inverse relationship you get x equals four y minus one over two y plus three multiply both sides of the equation by two y whoops two y plus three what's good for the goose is good for the gander so that this cancels on the left hand side you then get x times two y plus three is equal to four y minus one so step one switch x and y step two clear the denominator step three distribute the x uh that is on the left hand side there you get two x y plus three x equals four y minus one now you're going to gather all of the y's on the left hand side you're going to put everyone who's not a multiple y on the right hand side you end up with two two x y minus four y on the left and on the right you have a negative one minus three x on the left hand side factor out the y since you put every multiple of y on the left hand side there will be a common factor of y factor it out you get y times two x minus four this equals the right hand side negative one minus three x and whatever was left over when you factor out the y's divide by it two x minus four divided by two x minus four that will cancel and now you will have the inverse function f inverse of x is equal to uh negative one minus three x over two x minus four see we got we got all of the things we needed to do just right then and there um the steps are the same every single time for these linear fractals they seem a little involved but if we're intentional if we focus on the algebraic steps and more importantly why we are doing the algebraic steps we can change the numbers and follow the same process this is sort of the beauty of algebra if we can focus on what's essential and we can ignore what's non-essential what's irrelevant then we can repeat the process over and over and over again and that's really what algebra is all about focusing on the important things and ignoring the irrelevant things and that's exactly how we can solve for an inverse function for one of these linear fractionals


Income tax 2022-2023. Marginal and average tax rates. Let's do some wealth preservation with tax preparation.  And as the income goes up, the rates go up, which could lead to having multiple rates basically being applied when we calculate the tax based on one taxable income amount. A calculation which is clearly more complex than a flat tax, where we would only be multiplying times one rate. And it also leads us to want to be able to summarize those rates. Support Accounting Instruction by clicking the link below, giving you a free membership to all of the content on our website, broken out by category, further broken out by course. Each course then organized in a logical, reasonable fashion, making it much more easy to find what you need than can be done on a YouTube page. We also include added resources such as Excel practice problems, PDF files, and more like QuickBooks backup files when applicable. So once again, click the link below for a free month membership to our website and all the content on it. And one number which leads to the concepts we're talking about now, which is the average tax and the marginal tax. These are important terms we would like to be able to calculate for a client and provide to the client and tell them the relevance of those terms. To understand that, let's recap what the progressive tax system is and how that leads to the complexity and the problems that lead us to what these numbers, an average tax and a marginal tax. So this is the 2023 single file or tax brackets. We'll talk more about the tax bracket, basically different calculations for statuses such as buried filing joint or single or head of household later. But this is just to get us an idea of the progressive tax system. So we have these tiered levels. So if they have 11,000, we're taxed at the 10%. Now remember, this is the complexity of just calculating the actual tax now. That means we're calculating it based on the taxable income. We'll talk more about the tax formula, which gets into what should we include in income, what should be included in the deductions and how do we get to the taxable income. We'll talk more, we'll dig into that later. But assuming we get there, then we're going to be just calculating the tax. We want to understand what that tax calculation is. We also have complexity with regards to credits that we have to deal with as well. So all of these, of course, put further complexity into the tax code. So then if you earn over 11,000, but not over 44,725, now you're applying the 12% tax not to the entire amount. Let's imagine you earn 44,725, you wouldn't suddenly go from 10% to just a flat rate of 12%. That would be easier to calculate actually, but it would be kind of unfair because you can imagine then if someone jumped from 11,000 to 11,000 and $1 that you would suddenly tax them from 10% to 12% doesn't seem quite right. So what should happen is you're taxing the first layer at the 10% and that's calculated here with the 1100 for us in this table. And then the rest of the tax will be taxed at 12%. And if you made over 44,725, but not over 95,375, this 5,147 is basically summarizing the 10% at the 11,000, the difference between the 11 and the 44,725 at the 12% and then the difference between whatever you earned up to 95,375 and the 44,725 which is now taxed at 25% and so on and so forth as we increase in the layers. So the tax calculation gets more and more complex as we add the layers. Now in practice, this isn't that complex for us to do the tax return because we just use the tax software which applies the table calculation or something or a calculation like this in order to spit out the answer. So we're like, no worries to do that. However, it's still kind of an issue when you're explaining this to a client. They're like, well, how much tax did I actually pay and what you don't want to have to tell them?  But then you had credits actually that were applied after that that brought it down and so it gets quite muddied up. So what you would like to be able to do is say, well, there's a progressive tax system, multiple tax were applied, but this is one number that summarizes those taxes. Usually when we think of one number that summarizes a group of numbers, we think about an average. It would be an average. This is your average tax rate. Now, the average tax rate will work. It'll be a good tax rate to use to give someone, but it's not the best rate to use for planning into the future because if they're trying to say, hey, look, next year, I'm going to earn more money, how much tax am I going to owe? You can't base that on the average tax rate because that's pulling it's going to be lower because they're going to be taxed at the margin. That's an economic term at the margin. So that's why the marginal tax rate, their top tax bracket is important for planning. So let's just do a look at that in a little bit more detail. If I was to just take their total tax, so let's say that we did their tax return and they and we got their total tax. If I take their total tax divided by the taxable income, which again is a complicated number in and of itself because the taxable income took into consideration the gross income minus the deductions. And then you also have credits that complicate this, the idea of these conceptually. But tax income divided, the total tax divided by the taxable income, meaning the software calculated the tax using the complicated multiple rate table divided by the taxable income gives us the average tax. So I'm going to say, yeah, it's a progressive tax system. But this is basically one number that encapsulates that list of numbers that you dealt with. And that's that's a good number to give someone for that average tax. So a calculation like if you had 20,000 divided by 100,000 total tax divided by 100,000, that would be 20%. Right. And you can this is just an example, which is that's that's going to be it. That's your average tax, even though we know in actuality, it was multiple tax, it was multiple rates. So and then the marginal tax, though, the problem with this average tax, again, it's kind of like you're trying to give someone number like average what they're paying on the taxes. But it's not really going to help you for the budgeting into the future, because if they say, I'm going to earn more money next year, or I'm going to earn less money next year, then we're not diving back into this 10%. They're probably not going to be dipping back into this 10%. We're talking about the margin. So if they were, if they were in this category, if they were in the 24% category and they earned more money that was going to be somewhere within this category, if I gave them the average, it would be too low, right? And let's just take a quick look at that. If I know this isn't the way you generally calculate the average because you'd be taking a quick look at the taxable income once again. I'm sorry, the tax divided by the taxable income. But if I just, I did the average this way by just looking at like, okay, I'm just going to take those four rates divided by four and say, okay, that's going to be 0.1, 0.12, plus 0.22, plus 0.24 divided by four. Then I could say, ah, your average rate is like 17, right? I took those four numbers added them together and divided by four to get the average. But again, in practice, what you would be doing is taking the taxable income, which was calculated by the software divided by, I'm sorry, the tax that was calculated by the software divided by the taxable income. But just as an example, if I was to say, if that was your average and you think you're going to make another 50,000 next year, I can't just say, well, yeah, take that 17% times 50,000, it's going to be too low because you're working at the margin. So from an economic standpoint, the next step you make, you have to think about what's going to be the impact from this point going forward. And if you are currently in the 24% tax bracket, then the next dollar you're going to be taxed on isn't going to be at the average, it's going to be at the max, it's going to be at the high end, it's going to be at what we call the marginal tax rate. So when you deal with tax planning, you got to think, okay, if they have more, if they earn more now that I have this progressive tax system, you would think that that would be impacted at their highest tax rate unless they go earn enough to get into the next tier up. So now we've got to worry about, well, when are they going to hit the next tier up? We're not worried about it because we're going to say, hey, look, if you go over 95, if you go over 181-100, if you earn 182-100 and $1, you're going to suddenly be taxed at 32%. That's not the concern, no. But the concern is that you're going to be paying taxes at 24 to 32 on the added dollars, not at 17%, the average rate.  So it's really important two terms to give them. Most tax salts, where as we'll see in a following presentation, will give you these two rates, the marginal tax rate and the average tax rate. And those are two things that you might want to emphasize with a client as you're talking to them. If you're doing bookkeeping, telling them what the average is, you can say, yeah, that's the average. That's what you average pay. But that's not really the most useful term when you're trying to make decisions about changes to your taxes in the future because the changes are made, the decisions are happening at the margin. And that means you're making decisions at the highest rate.


have been calculated properly. So then we can take the percent of the total. So for example, what's the percent of times that we found nine potholes in our 500 counts of 100 mile spans, right? So I could say that would be, we found two times that that happened. So two over 500 is gonna give us our point 4%. And if I scroll down, we could say, what's the percent likelihood that we had 20 potholes and the 100 mile span? Well, apparently that happened 56 times out of 500. It seems like, so that's 11.2% that likelihood that we had the 56 potholes. So that's gonna be our data set, which we can represent then in terms of the frequency, the count, as well as the percent of the total, which is there. So then if I took some standard calculations, if I took the mean of the data set, for example, and the mean is just simply taking the average of this data set, so the Excel formula would be equals the average of this data set, we have a mean number of 20.14. And if I take the variance of the data set, so now I'm looking, this is the var.p and then dot as for a sample and a population, just to practice both of those of this entire data set, we get to 20.49. So one of the characteristics that's interesting with a Poisson distribution is that the variance, if it was a perfect Poisson situation, would be equal to the mean. So if it's approximating the mean, the variance and the mean are pretty close, then we're going, oh wait, this might be like a Poisson distribution situation, in which case we might be able to use a line in order to represent the data. So then, so we had the mean potholes are, before we go there, no, we can also plot the data. So if I plot this data, so this is a plot of the frequency, so we're taking the frequency here and we're counting, so how many times did we have the 16 or the 20 potholes, the highest one is 50 something, 55 or something like that. So that's the count that's happening here. So we had, again, the frequency represents the number of times it happened and the 500 tests that we ran. And if we're looking at 20 potholes, we had the 56, right? That's that one that's going way out there. It looks a little bit kind of like an outlier a bit. And if I look at, I can also do it on a percent basis. So if I take the percent, the percent of the total, notice you get, in essence, the same shape here, but now we're looking at it as a percent of the total. So that's gonna, so now we're saying, hey, that kind of, that doesn't look perfectly like a Poisson distribution, but it looks like it might be a little skewed to the right, right? It might be looking kind of normal. Poisson sometimes looks like a bell shape, but it's slightly skewed to the right, generally, as the general idea. So we're thinking maybe it would be a Poisson. So now we could say, what if I took that mean, so now I'm taking the mean that we got here and the miles of 100 and I did an actual curve using the Poisson distribution. So this would be like the perfect representation as opposed to our approximation with our data set if it wasn't a perfect line, right? So I can take my data set here as if we're seeing how many times the potholes would show up representing zero potholes in 100 miles, one pothole, two potholes in 100 miles. And then if I do a Poisson calculation, which is this formula, Poisson.dist, the x is representing the zero, the one, the two, and so forth, the mean is now gonna be the 20.14 we got from our example. And then do I want it to be cumulative? No, I don't want it to be cumulative. I want you to give me the percent likelihood of each of these numbers. What's the percent likelihood that we get nine potholes in the 100 miles, 0.27. And then I can compare this to what we actually got over here. Now remember the Poisson distribution isn't giving us an actual frequency, the number of potholes, it's giving us basically the likelihood of the number of potholes, right? Which is this column kind of represents this column. So I can compare this column then, if I subtract this column to this column, here's our differences. So I can look at those differences and say, does that look pretty close? It looks like it's pretty close to a Poisson distribution, an exact Poisson distribution as the data that we generated. And so then I could say, all right, we could then of course ask questions if we had our Poisson distribution such as what's the likelihood of having zero to five? So if I said zero to five, let's pull out the trusty calculator and we can say that would be, now I would have a cumulative from here to here. It's still pretty low. I've got 0.006, I can't count it here because I needed an actual other decimal to pull it up. But the idea is you could generally sum it up if I had another decimal to do it to get to the point 0.06, which would be the sum this way, or we can use the Poisson.dist formula this way. I could say Poisson.dist, the x is now going to be the five, the mean is still the mean over here, 20.14. But now I do want it to be cumulative, so I put a one instead of a zero and that'll basically take sum it up. So you got that nice Poisson distribution to give you the cumulative up to that point, which would be a common kind of question. What if I want seven to 14? So if I go from seven to 14, here's seven and then down to 14. So I could add those up, I could sum them up being careful to say, well, are we including seven or are we not including seven in a practice problem or in any situation you have to be pretty careful and say are we including those two ends or are we really only saying that we want to include the eight to 13? But I could then add those up here. I won't actually do it, but you get the concept of it here. And then if I did this with a Poisson distribution, it's a little tricky because what I have to do then is I have to say, well, the cumulative function will take me up to the upper limit, which I'm saying is 14, which I'm gonna assume we're including the 14 here. So that would go up to the 14 and then I'd have to subtract out up to the lower limit, which we have six. So I'm assuming we're including the seven, so the lower limit would be six. So it'd be Poisson dot dist, X would be 14, the upper limit, which I'm including the 14 in this case, and then comma and the mean would be the same mean over here and it's gonna be cumulative with a one minus Poisson dot dist up to six. So I'm not including the seven because I don't wanna subtract out the seven. I want the seven to be included in the range we're talking about comma. And then the mean is gonna be this one again and it's gonna be cumulative. So we can do the calculation that way as well. Now, this is a histogram plotting the, and we're doing it with a bar chart by the way in Excel, but it's a histogram kind of format. We're plotting the P of X, which is the perfect Poisson distribution curve, histogram versus this graph over here, which was our actual data. And you could see they line up pretty close. You got that weird outlier right there, but they line up pretty close. This is the same thing with a line chart as opposed to like a histogram type of chart. And so now if I'm looking at my data and we're saying, okay, these potholes, can we get some predictions about these potholes? Let's count all the potholes that come up in every 100 mile and see if we can analyze that data and see if there's any trends to this that might help us with our maintenance policy or something like that. And then we counted the potholes. We noticed that the mean is close to the variance. So we're saying, hey, that looks like it might be a Poisson distribution. It looks kinda like it might be a Poisson distribution too. And then we actually plot out the Poisson distribution, compare it, and it's like, yeah, the difference on their data versus our data is pretty close. So maybe we could use the Poisson distribution. Let's plot them together on top of each other. And we could say, yeah, it looks pretty much like the Poisson is approximating our data. Therefore, maybe going forward when we make decisions about how many potholes might be showing up in any 100 mile radius or something, we can use the Poisson distribution to make some approximations about that and plan accordingly. Notice that if the Poisson distribution did not correlate well, couldn't approximate the actual data, then we're left with a problem. Cause then we have to say, well, how am I gonna figure out how many potholes we have to do something different to kind of extrapolate what the data is gonna mean going out into the future.


Hey, hey there, do squatters look and here back again with another reaction video and today we're gonna be reacting to Chandy's birthday video So happy birthday, Chandy if you guys don't know who Chandy is he is a member from easy if not my favorite group out there He has an amazing artist. He raps amazing. He's a funny dude. He's just awesome and journal and I'm pretty sure he turned 18 today So I'm is that Korean age or normal age. I you know, I don't know which one it is Whatever one it is congratulations. You've got one year older You're getting closer to being adults so you can go to your travel on America, which you want to do how yeah I would like to ask you or not America I would like to travel Asia instead of America because I'm a bit but I don't know I just like Asia more than America So, you know, and when I turn it into adult which is next year I want to try and travel to Asia mainly Korea But yeah, other than that this gets straight into this I'm freaking excited I hope you guys are too Let's do this Yo, is that heaven here? Yeah. Oh, oh look at that handsome fella.  He's a savage mechanism as well, which is cool I can't sleep during the day. It's impossible for me to do so. I only sleep. I will at night I can't don't join the day Sorry Yes Whoa, that looks like good drink. I Don't have a drink, but I want that And I drink from shorts as well Shorts are amazing. Oh Yo, what's that dude? Oh, man. That was a good video There's so many moments that I can just remember from this video. This is so good But yet again, happy birthday Channy I hope you had an amazing day. If you ever have something across this video, which you probably won't but happy birthday, my dude Happy birthday. You're getting closer to being adults. So yeah That's what I want to say, but yeah I think that this place is gonna be all for this video. Hope you guys did enjoy and if you guys did, then definitely show some support by leaving a like And also comment down below your favorite Channy memory and also subscribe guys are new to channel because there's been more Reactions coming to you guys in the future. So stay tuned for that and I'm in that I hope you guys have a great day and just keep on smiling and Adios amigos


There have been many mentions up to this point of the wonderful Perry Hewitt who is about to come up and tell us you can find her Not just in the flesh here speaking to you But also in every possible form of social media in her own and on behalf of Harvard And I think she's going to tell you about some of the resources available within the university to do much of this and hopefully Lead us also into some demos after this and Perry. Thank you Good. Well, thank you very much for having me here. It's a privilege to be here Especially since I don't know John if you know this I went to the first ever Berkman Internet and Society Conference I was working for hard court now Elsevier. I'm ashamed to say And they wanted to send me to Gartner to learn about the Internet and I said I don't know somehow this enterprise IT approach seems like It might not be the right way So I'm going to talk a little bit about social at Harvard, you know How we think about balancing influencing control how we're doing it and how we can be helpful to you the faculty So in February of this year a piece ran in the New Yorker I think it was Adam Gopnik that talked about the impact of the information deluge that is the Internet today And what are the ramifications to that gentleman's question early on our collective intelligence? What does it mean for all of us? And this article divided the world into three categories, which I loved It was the never-betters like clay shirky. It's utopia life has never been better We've never had more access to informations fabulous all social divisions have now broken down the better never's the sky is falling We can no longer think in more than 140 characters at a time. It's a disaster What are we going to do and the ever wasters which is sure the tools are changing and the effects are wide ranging But isn't that the very essence of modernity that we expect these tools to change and how we communicate to change? So I think the same division could be made about this brave new world of social media We live in today the world in which we're intimately and constantly to dr. Boyd's point Connected to those of us, you know our friends and our family. We carry our social graph with us wherever we go You know what from our most intimate people? We know the closest to the way the acquaintances we might meet at a conference who somehow show up on my Facebook page And I have no memory whatsoever of who they are so for this social web transformation I'm going to go with the camp of the ever wasters and talk about why how the tools have changed the game Significantly people remain people with the same positive and negative Attributes they had going into this brave new world and as you pointed out as well, you know Teenagers are now using code on Facebook to deceive their parents How different and how the same the world really is? So Harvard's a social place, right? This is a party in 1935. I found this in the archives a party I really wish I'd gone to the costumes look fabulous and this Professor Lewis remarked that he was here for the beginning of Microsoft in the here of the beginning of Facebook He was not here for the beginning of Twitter, which I believe this is the first instance of Twitter Which is actually in 1910 a bunch of Harvard students had a typewriter Sitting with a with a roll of paper that just rolled out and had what is essentially status messages, right? That's exactly what this if you read the middle one. It says where the deuce are you? I thought that you were we were going skating with me this afternoon M furious So that's pretty much like any tweet. I've ever read so the more things plus a change people This is what Harvard looks like today. It's what a lot of campuses look like It's how you know whether as Sherry Turkle would say are these students alone together? Are they socializing? There's a lot to be defined about what they're doing But this is what they were doing the first sunny day in the yard in March 2010 And really it's changed the way, you know the fabric of even how alumni Relate so if you think of the most physical and traditional institutions that make up Harvard Even those start to look different with these new mobile social digital behaviors that are pervading all age groups And what does it mean to be at commencement anyway, right in an age in which you can share information in which you know Mr. IT and I collaborate on a live stream and you know, there's all this information going out there, you know about commencement You know, what does it mean to be physically present and how much are you experiencing it versus capturing it to share with that social graph? You now take with you everywhere So I think the big challenge now is as we think about this is how are we balancing influence and control in this brave new world? So as a communication shop, so social technologies have emerged giving every Harvard community member a potential megaphone This is a David Mellon CS 50 where he just basically pulls in and aggregates all the tweets I was here about two days before my Twitter account was pulled in here And you can see there's a real balance of control and influence in terms of thing that you know I didn't ask for them to be pulled in all the sudden I'm part of this bigger Harvard thing is what I'm saying now More Harvard than not but what does it mean that this democratized access to media has changed the game So as a university communication shop, right? We're not academics where the people talk about the university You know, we really need to think about a different balance of influencing control. There's no letting first of all There's no letting faculty do anything. I'm married to one. I know how it goes But there's no letting you know a thousand flowers bloom This is this is the world we live in and it presents a lot of opportunity as well as a lot of adjustment We can put some rules in place about you know use of name and provide some sensible best practices Which my office does but our strategy should really recognize that every member of our community now has access to global Communications channels that were unthinkable even five or ten years ago and we should acknowledge that while their risks, you know Inherent to this approach. There are also huge opportunities to what this enables us to do You may not know but Harvard's a slightly decentralized place and there's an opportunity through these social channels to tear down some of those walls I'm not going to say hey business school. Why don't we build one website with the divinity school? And then anyone can get everything one place that's not going to fly real Well, but there are ways through social media where you can pull content thematically to deliver a more holistic and experience of Harvard So similarly to being unable to you know control the voices around Harvard You know students are going to think of innovative ways to use and manage, you know their social web presences I love this as I saw you Harvard. It's essentially Craigslist. Miss connections. It's one of the most traffic sites at Harvard And they'll use it for pre professional reasons too, right? HBS's startup drive was in the New York Times yesterday It's frequently, you know mentioned in the media is a way that people interested in innovation around the university Not only this is HBS startup drive, but they've been very welcoming to undergraduates and people around the university interested in new programs and ideas So, you know for a communication shop and in a university influence versus control has been a balance But this democratized access to media makes us really seriously rebalance in favor of influence So what does this mean? It still means like we think and we care a lot about what we put on Harvard dot edu every day But we also care a lot about what gets over a million impressions a day on Facebook. We're no Dr. Pepper We have about 375,000 fans on Facebook But it does mean when articles are picked up through our what we post ourselves and what others post that we can point to about A million places that our content appears every single day So our ability to Professor Lewis's point earlier to write edit publish and deliver the canonical story of Harvard You know a hardbound edition is decreasing to deliver that one picture book that one story that one volume that says here Here's what Harvard is today. Here's the definitive official communications blessed version So we were a lot more about creating shareable content getting shareable content from all of you aggregating content from around the university and Sending it out syndicating it out both through platforms We own and through platforms where we have outposts so platform We own would be the new Harvard dot edu launching this summer a platform That's an outpost might be our Facebook play the presence or our YouTube presence So what's really changed is it's not a world in which we can sit and wait for people to come to our home page? To your point earlier of you know, what does it mean? You know how much to rear view mirrored view does Harvard have into what? Other universities during other knowledge sharing institutions are doing how much distance we have between us and the next guy Today we can no longer say Harvard will build a home page and people will come to it People will form impressions based on what they gleaned directly, you know from the great teaching research coming out of Harvard You know all its websites all its Facebook pages all its YouTube channels We can report on some small fraction of it with the Harvard Gazette, which is great But the bulk of the information will come to you So we're got to swim with that current and build systems to support that reality of all the content coming out from all over the university So some more granular slides about how we're doing it So we recognize that people want to share so we help them a couple years ago We relaunched the Gazette the traffic's up about 350 percent. So a lot of people come to it use it share the content That's one of my favorite stories that was big this year thinking like an octopus that was picked up on a lot of social web channels about how octopus is think and One of the things we did there are some negatives You know as was pointed out earlier of bringing your social graph wherever you go Sometimes they're real positives to bring your social graph wherever you go So when we put Facebook recent activity on the Gazette pages Usage and travel around the Gazette per unique visitor, you know really spiked because people would come and say Oh, I read this article, but John read this other article Maybe that article will be of interest to me and we also, you know Help people share and devices that are most intimate to them right these mobile devices that are now never more than 12 inches from our hands And so we built an app that delivers news via that device and we enable people to share and recently we enabled a custom URL So again, we can't be all one Harvard and all our web presences or all our you know faculty Nor should we but we can be all one Harvard through our domain or URL or Harvard me So we participate because social pores of vacuum. This is one of my favorite stories when I first came We had this fabulous admissions video and I said, hey, can I put it on YouTube?  So what were you thinking? I said, I know one's very sorry I'll get it up there immediately But it's just super interesting that these idea of you know, I think you said earlier Writing yourself into being on the social web and while there were all these representations of Harvard as a 375-year-old institution with all this stuff going on of course, you know There was all this representation of Harvard, but Harvard wasn't representing itself and Nature abhors a vacuum and social abhors a vacuum So another way we've tried to sort of enliven or bring social into the experience is but with live streams So president Faust has been incredibly forward-thinking on this because people can post whatever they post and people always say Do you moderate beforehand? We say no, you know, we just let people post what they post of its hate speech We take it down, but that's pretty much it this economic panel stinks can stay that's fine That's one person's opinion But we've really been able to open up using a variety of tools You know the live stream experience of Harvard and people in very supportive of that and then you know I was just Astonished on any number of levels to see president Obama on the stool at Facebook. It was really interesting to see how many Governmental sort of stayed offices or you know emulating that trend or taking it to an even greater degree That banner looks very much like 1999 web. Isn't that like the president in comic sans? I couldn't quite get over it Another thing we do which we hope benefits schools and faculty is the idea of providing platforms and tools So to the point of when do you when do you have core principles? And you know exactly what you're going to do when you run a pilot and you you move forward very Plansfully and when do you steam forward and experiment? This has been a little bit more on the steam forward and experiment side Because I think we knew that social board of acumen and we had this great content And we there were really pressing reasons to get it out there Professor Sandel's justice content was one of the reasons But there was a lot of other stuff that was just it was on the internet But it was sort of lying fallow in websites that were poorly trafficked or embedded in players that no longer worked So we sort of wanted to sort of use that opportunity to find content that was already Existing that professors had already created that was already blessed for public and yet It was just sort of marooned where it was so we've had some good successes here We've been able to you know with the itunes particularly, you know rising tide floats all boats So, you know little known feeds like the Memorial Church feed for example We pulled in they did a I think it's a podcast or a video cast that we've had for ages And the fact that we had it available through the itunes you channel at the regrettable passing of Reverend Gomes Was a huge asset because people were already coming there and they thought oh, I read about this man the New York Times I'd like to understand more about him and his church and his mission and that content was there So just aggregating that content syndicating it out really amplifies the content for everyone So we also done, you know some foray into location based with Foursquare that hasn't been heavily promoted or heavily pushed We care deeply about student privacy and student privacy issues So all the content that we post to these channels are content that already exists on whole closed Harvard sites or and you know Harvard domain sites this is a way to to spread and amplify the content not the only means to get to this content and You know reaping the amplification benefits is really real So on the left is JFK 50 which is series of videos put together for the 50th anniversary of the inaugural and on the right is Science and cooking that very very popular gen ed class These are not the classes themselves that the public lectures, but it was a way to say You know, we really want you know, this content to live beyond you know This immensely popular, you know was sold out in Sanders right away You know how do we make this more accessible and then the New York Times called which I think is a currency faculty recognize You know YouTube whatever but you know New York Times New York Times calls most people even here will pick up the phone And they called and said we want to do a feature on your science and cooking But can you make sure all your videos are on YouTube so we can link to them? And I thought you know sort of a light bulb went off a little bit at cease who's definitely sees the value but saying hmm You know there it there is benefit even to traditional media So the real focus today is how can we be helpful as a communication shop to what all of you are trying to do Which is the core of what universities do do with the teaching learning or research? So, you know, there's some new data coming out. This is from actually Pearson learning group About you know how faculty are beginning to use social media a lot of it seems to be structured around consumption on YouTube and less Around posting but increasingly, you know faculty report using at least one social media device And we know that faculty are using social media for you know professional and non-class purposes particularly latter faculty and You know to my mind, there's a tremendous opportunity You know, there's a way to refine the rough cut without sending out the bad draft Different people feel very differently about that but there's a tremendous opportunity there There's a medium for testing the waters cross-organizational collaboration a way to discover as I did with communications Found content, you know comments from conferences for example these come think of all the smart comments You've ever heard at a conference that just vanished into the ether Increasingly social tools like Twitter like you are gonna be capturing and storing and saving and allowing people to categorize those comments to get sort of found content or found knowledge and A greater ability for impact. I guess this was the theme of the day beyond journal circulation If you don't have access to JSTOR how do those great ideas get out there? So what's our piece in all this? You know you guys are involved with the teaching learning and research which is the core of what the university does So our piece is a couple of things It's to develop best practices for social participation for you in your schools and to manage the platforms Which are both third-party like YouTube and iTunes and a new Harvard edu where Harvard content is aggregated for greatest reach Thank you Well, thank everybody for participating in this session. I'm absolutely I think I'm over stimulated It's probably a way of saying it but outside we've arranged to have some computer set up So if people are interested in learning how to set up a Twitter account Or how to set up a blog or other kinds of things we have some people outside We'll actually just introduce you to the ideas. So with that one, I thank our panelists again Their effort and


Would you rather give up bacon for the rest of your life or sports betting for the next year?  Oh, bacon. I don't even eat bacon, so. Would you rather give up bacon for the rest of your life? Whatever the second thing is. Yeah, gotta stick with the bacon. Bacon. Of course, betting for the next year. I love bacon. It's good mac and cheese. I love breakfast sandwiches. I need bacon.