There are, there are nine million bicycles in Beijing, I think so anyway. That's Russell Loo, this is Asia in Review with Russell and me. Russell is a lawyer who practices in Beijing and also in Honolulu, and he is fascinated as we all are with the new prospect of bicycles everywhere, bike share, bike share. They got bike share in Honolulu, but come to find, they got bike share in Beijing. There's three million and rising fast bicycles in Beijing, that's why this song is appropriate somehow. Hi Russell. Hi Jay, I'm glad to be back here in Hawaii. Great to have you here. So you're all excited about the bicycles in Beijing. Let's talk about it. What do they got going? Now here it's called Biki, right? They just installed it on July 1st. There are a thousand bicycles out there in the community and a lot of bike stands everywhere you look. What's happening in Beijing with bike share? Well Jay, actually bike sharing in Beijing has been ongoing for the last two years. It's been going, working really well. It's a big phenomenon and I kind of wanted to share that phenomenon. Maybe people here in Hawaii would catch on to it. We need to get a little educated. We need to look at the platform in which a lot of capital raise. People are buying into this. We need people buying into this and the bikes are used by people to really get around man versus car on the bicycle. Well I remember a time when there were nine million bicycles in Beijing and everybody rode on a bicycle in Beijing. There were hardly any cars but then we went through this kind of cycle where the middle class arose, everybody could buy a car and then there were way more than nine million cars in Beijing and much fewer bicycles. But now we're back. The sine curve turns around. Now we're back to bicycles, old fashioned push-battle bicycles in Beijing. That's right Jay. It's back to the 90s. When bikes were the king, it's the bicycle kingdom. You raised an important point because when people started to have a lot of money, having a car meant a lot of face, a lot of respect. I have a big black Audi. Government would buy these big black Audi's. It was a sign of prestige, it's a sign of what I've made in this world. But today, people have money and now they're moving up that curve realizing, hey wait a minute, there's a lot of pollution. We need to do something. And there's a phenomenon because some young people, and we'll talk about the Ophold that company, 26-year-old founder of this company who's rigged in billions of dollars starting a great idea. Okay. So first, how does it work? How does, what do you call it, O-F-O, Ophold? How does Ophold work in China? Sure. There are several bike-sharing companies. The two largest are MoBike and Ophold. And Ophold, I like Ophold because it's the yellow bike, it's comfortable. Big seats, it's very comfortable. And even though it's 90 degrees, I was there last week, just got back from Beijing, 97 degrees, I was riding my bike and I would feel the wind blow and it was a great way to get around. I wasn't sitting in a taxi. But how does it work? Okay. Well, let's take a look at Ophold. Started by a 26-year-old entrepreneur, he started with $20,000, $20,000, two years, just in two years. It's now valued at $2 billion. Oh my God. Yes. Yes. And... This is like Jack Ma. Well, in fact, Jack Ma of Alibaba recently helped raise $700 million in latest capital offering. It's fantastic. And you know this young entrepreneur, he was interviewed by CCN TV. His name is Mr. Dye. And what he said is, it doesn't matter about what the value of my company is. What's important to me, he said, is what Ophold can do for the consumers. We're thinking really capitalism, consumers, he's thinking like a marketing person. And he said, quote, when you set up a company, you have to solve the problem. You provide a service for the users. He says, we have to figure out what is the main problem we have to solve. That's more important than valuation. And I think he's caught on to a point, Jay. So what he's done is he's realized that there are millions of people, cars are clogging the road, lots of pollution. So why not run the subway, but you take the bike from the subway or the bus stop to your last destination to your office or to a place for entertainment. And you can ride it back. And he said, we're going to make it affordable. It costs 15 cents for an hour, 15 cents for an hour. And it's fantastic because it's changing the lives of people there. The Chinese dream. I'm living at a J. What does it mean? I may take an hour on the subway to the far part of the city. But when I get off, I get on my bike. There's a bike stand right there. Bike stand right there. How do you do the transaction when you take the bike? Very easy. I take my iPhone. E-commerce. E-commerce. I have an application. It's called WayChat. WayChat allows me to talk to people, send video files, send word files. But it also allows me to pay for things. E-commerce is really big in China. I don't carry cash. There's no credit card to put in a slot. There are no coins. This is not credit cards. It's not credit cards. And so I just have my iPhone and I take a scan. The scan looks like this. You see the scan moving along there? Oh, maybe you can't see it. You scan a QR code. I scan it. The QR code will be at the side of the vendor in this transaction. That's right. You take your phone and you scan that. What happens then? Then I will hear a click. And that click means that I've registered onto the system. And the lock opens up magically. It starts taking away. So the money now is gone from your phone, your account, to the vendor who had the QR code. And that's when I close the application. When I get off the bike, I lock it, put the kickstand on, and I press and it reads it again. It closes. Automatically it's read my QR code. And what it does is automatically it logs me off. And it tells, at that point, your amount to pay is $1.15. And it says, do you approve? Pay now. And I hit the pay button. It's gone. And you know what's amazing? One of the admirers of this whole bike sharing program is Tim Cook of Apple. He recently went to the OFO headquarters to see this young 20-year-old. It's phenomenal. Why? Because do you know it's a half a million downloads of this app through the App Store, iPhone. People are loading this app so that they can use the bike sharing. And that's just one company called OFO. Incredible. Incredible. OK, so that is an interesting comparison to what we have here now as of July 1st in Bike Share. But let me show you. I think we've got a video that we can show you. Take our viewers how it looks like on the streets of Beijing. OK, let's see that. And you can tell us what we're looking at.  There's got to be about 1,000 bikes lining up the whole block all the way to the subway station. These are all bike share bikes. These are all bike share bikes. See the colors? Blue, yellow, orange, all the competitors. OK, you'll hear this guy. There he goes. He's on a bike. OK, you can't hear it, but you heard the click. And there's another person. They get off the subway and take on and ride this bike to their workplace. This is an early morning shot in Beijing very early. But you can see how big this is. It's a revolution. People are riding. It wasn't this way a year ago. It was not. Well, it started about a year ago, but now I think it's really riding its drive. It's going viral, yeah. But you see how many bikes? That means that's corresponding to how many bikes are on the road. We're looking at thousands of bikes here. Thousands of bikes, right. That tells you it's also eco-friendly. People are taking the streets. Look how empty the streets are cars. They're on bikes. They're going to go ride the bikes to the workplace. They're going to unpollute the environment. So it's a great idea because people can find an economic, efficient, and a clean way, a clean way to get around the city. So this is a real game changer, at least in Beijing. This is not only in Beijing, but in the second tier cities like Tianjin. It's all over China then. It's all over China. That app works all over China. So again, Jay, this is what we will see all over in China. But that's just one shot. But do you want to see how it works, Jay? Yeah. Let's show our readers how it works. We've got a video of that of how we opened the app. Yeah, let's see what it looks like. Good afternoon. We're back live here on King Tech Global. I'm Russell Liu, your co-host, directly from Beijing. And I have here, I read Lee, who is a local from Beijing. And we're going to show you how easy it is to rent a bicycle for $0.15 an hour. And the user's smart phone, there's a bike application we use that's called Mobile. And believe it or not, it is in English. It shows me the location of all these bikes on the map here. It's tied into the GPS system. So it tells us which bike is available. And I read, and you see there's a button here that says Unlock. Underneath, all you have to do is click the Unlock. Unlock, and it will only take a few seconds. Then it will be Unlocked. We heard that sound. That wonderful sound. We heard a buzz, and we heard Unlocked. So the bike now is Unlocked. And we're free to use it. We want Unlock to go. OK, we're back. And we'll show you now just in a few seconds how we close the ride. Our next video. OK, next video shows how we close the ride. We're back again. It's 95 degrees here. We finished our ride. So when we're all done, we're going to show what we do at the end of the ride. Irene, we're going to lock this bike. Can you show what we do? So this thing here is the lock. All you have to do is just put it downwards. Oops, I can't do it. It's locked. And you just heard us how. It's locked. That means my charge is all over. I spent $0.15 for a beautiful ride on this bike. And I can take it anywhere in the city. Oh, that is so easy, Russell. It's brilliant. And the great thing about it is I don't have to take it back to the bike stand. Some companies, you have to take the bike stand. What a waste of time. I just drop it anywhere in the city. Why? Because they have a GPS. They will pick up the bikes if it's in a remote place. They know where the bike is. But what they also do is, because it's a remote place, if you take it back to a convenient place, they will give you a bonus, maybe another free ride. So the scent of works. And it really gets people. That's brilliant. I just want to go out there. And I look forward to it, Jay. I really do. I will get to the subway. And I'm always thinking about this is my 15-minute exercise. I get my Garmin watch. And I actually carry it with me. I read my pulse. And I time my ride to see what my workout is like. Who needs a health club, Jay? It's a wonderful thing. It's a great idea. And Beijing is flat. Beijing is flat. Well, there are hills in Beijing. No hills, for the most part. It's a bike-friendly city. You know, the mayor of Honolulu has a vision. And he was smart. He's a bike rider, I believe. But we already have the bike lanes. But what do we need to do? Well, again, we need to think this carefully. If you're going to have mass transit, if you're going to have the train station, you need to get the bikes near by the train station. Encourage people to take the 15, 20 minutes. And get companies that Garmin all the Fitbits. And have them through applications for the city of Honolulu. This is your routes. We give you the maps. And how to tie in health awareness. You know, all of these are wonderful. Well, you know, I'd like to take a minute break, Russell, and come back and talk about what you learned, what, you know, what everyone knows in OFO and the other bike share companies in China, which we could import to the US, to Hawaii, for example. Let's take a short break. We'll come right back. We all play a role in keeping our community safe. Every day, we move in and out of each other's busy lives. It's easy to take for granted all the little moments that make up our every day. Some are good, others not so much. But that's life. It's when something doesn't seem quite right that it's time to pay attention. Because only you know what's not supposed to be in your every day. So protect your every day. If you see something suspicious, say something to local authorities. Aloha. I'm Tim Appachaw, host for Moving Hawaii Forward, a show dedicated to transportation issues and traffic. We identify those areas where we do have problems in the state, but also the show is dedicated to trying to find solutions, not just detail our problems. So join me every other Tuesday on Moving Hawaii Forward. I'm Tim Appachella. Thank you. We're back. We're live here on Asia In Review with Russell Lu. He's a lawyer who practices in Beijing and in Hawaii. And he has discovered, truly discovered, OFO and other bike share organizations which are spreading throughout China. And he's here to tell us how great that is. And I asked him during the break, what happens when you leave the bike for a few seconds and deliver the package? By the way, all the bikes come with baskets. I noticed that, which is really good. Some bikes here don't have that. So you leave it for a second. Somebody could rip it off. How do you avoid that? Well, Ray, it's important that when you get out the bike and you're going to walk away, you want to close the lock because somebody will get on the bike. They may not steal it. They may ride it for free.  On your nickel, right? On your nickel. So you close it. One Kwi, nobody's going to worry about that. You lock it up and you come out. You get the same bike or another bike. It doesn't matter what bike. It doesn't matter what bike. Pick a bike. That's the beauty. Well, that's a benefit of why it's successful. Because in the old days, I'd be afraid to take my bike, leave it there, lock it. Somebody may steal it. This way, I don't care. Because every bike is tracked by GPS. So they can find that bike. They will find it. Even if it's in a remote part. And if somebody stole it, they're going to find him. Yeah, exactly. But you want to try to at least get the bike. But I found a culture in China. People respect these things. They don't steal bikes. These kind of bikes. They know it's everybody uses them. Everybody. So there's enough of a group awareness that we don't steal these bikes. Maybe they'll steal some private bike, but not one of these. This is kind of a public property. Yeah, so in that device that fits, the lock thing that fits there, that has the unlock mechanism that you showed us. And it has the lock you showed us, which you want to when you stop riding. It has the GPS mechanism in it. And it's the security and identity of that bike. So that's the key to the whole thing, isn't it? That's a key. The other key to it is that the technology. This is a think-tact. So technology, that's really important. We've come to the age where I feel in the US, we are not using the technology where we should. China is leaps ahead of us. They use e-commerce. And for example, the question of how do you secure the bike? Well, before, when you sign up for the program, whether it's a full bicycle or a full bike. It has to be a member. Well, not a member. You register, but also you put a deposit, maybe $200 RMMB for offsetting the lock. So what does that come through in American money? $30. $30. But one thing good about this is that people will know that if you took the bike last, they know that you had it last. So they can go back to you and check where it is at Hock and Stolen or whatever. But again, these things do happen. But I think that there's a public awareness that this bike is for us all to benefit. The bike's in good shape. What happens if they break? What happens if these crews that come along on these special motorized bikes throw them on top, take the bikes away to get them repaired? And they did a good job of it. Actually, repairing the bikes, I rarely will come across a bike that doesn't work. My bikes are all well-fit. And the good thing about it is that the seats of certain bike companies are adjustable. I fit the switch. I like to reach it smaller. Because then the ride is easier. My legs don't cramp. Extremely comfortable. But again, that's the human side. That's the mechanical side. So that's important. Bikes have to be comfortable, clean, working order. They have to have GPS. How can they make billions of dollars doing with one RMB, one QUI for an hour? That seems like it doesn't compute. Well, that's the model that's being criticized by the West. I was looking up some research about it. It's not going to work because they're not going to make money. But I believe that at a certain point, because of the amount of the ridership and the frequency of the bikes, they will come out and make money ahead. Plus, right now, I think the movement to make a cleaner Beijing, cleaner city in China, it's making a difference, I believe. And people are getting healthier. All of these are good things. I found that I have this bike withdrawal. So I'm back in Hollywood. I want to get a bike. I want to ride down the street. You can take the Viki bike right around the corner. Well, it's too expensive for me. It's three-something for half an hour. And I think you've got to return it to one of their stations. I don't think you'd just leave it there. Otherwise, you're going to be socked for a loss of a bike. But again, it's the platform. It's making sure that the consumers are aware of what they're using it for. The way they designed China is for people to use it every day, living. So, OK, let's take what you have described, what you know about how it works in Beijing. What can we learn here? What can we do here to make it better? What we can do to make it here better is to have the technology to have e-commerce. We need to wrap up our technology. And our technology is important because that's the key to making it work. It's the technology. You need to have the apps on the phone. You need to encourage people to use it. And you need to make it convenient for people. Are they doing that in China? Are they encouraging people to use it? Is there an advertising program going on to let everybody know how it works and why they should use it? It's because with all the bikes all over, the free instructors are free advertising. It's on advertisements through the application. And it's convenient because you're using your smartphone. You need to carry cash. You need to carry a credit card. You don't have those kind of problems. You don't have that kind of problem now. It's the masses can use them. So there's a big difference. Yeah. Well, it sounds like we're a long way from what you described is happening in Beijing. And they're kind of ahead of us in a way because it's much more affordable, because it's easier, because of the smartphone. And in general, it sounds like it's more well thought out as against the kind of people who would be the market. It fits right in. So instead of a few tourists coming at random here in Beijing and to use it here in Honolulu, everyone uses it. And that's what we have to achieve. We have to achieve a community here in Honolulu anyway, where everyone is riding a bike. Everyone is physically able to riding a bike. And even if it's not, give them a way to do it. And so I think there's less than we can learn about the 15 cents, about that lock design that we looked at a little while ago, about the notion of leaving them anywhere and tracking them with GPS. A lot of those things, we don't have that. We don't have the GPS tracking, I don't believe. And we have to use credit cards, and we have to go from station to station instead of anywhere. So there are limitations in the system that we just installed on July 1. That could be improved somehow by referring. I think that people in Biky ought to go to Ofo and take a look. That's right. I think the idea is that it's a community-based project. It's not just a company only. The community has to buy in. Maybe if you franchise this in the different communities in the city here, where there's a private franchisees who make it work within a community and tie it to a bigger part, you make it work close with the public transit, put it by the bus stations, you make incentives so people will ride it. And especially with the mass transit, you want to get it by those stations where people get off and ride that bike. Yeah, but all you have to do is have a bike rack. You don't have to have a fancy. I mean, the Biky down the block has this huge steel plate that fits on. And then each bike has a special kind of holder for it. And then there's a solar. This is creative. There's a solar device, a solar cell that feeds the electricity to that particular station. That's all very creative. But it's not mobile. It's not mobile. We can't move it around. It's a fixed platform. We're talking about a world in China where it's not fixed. We have the smartphone. Put them anywhere. We don't put them anywhere. We've got the smartphone with track you. You put a deposit. You go anywhere. That's what the beauty is. You make it work. You make it work not for the tourist, but for the community. Right. And everyone and anyone would be riding all the time. And there's an old rule is the more people that ride the safer it gets to be to ride. You were telling me before the show that it's quite safe to ride a bike in Beijing. Even though there's gobs of traffic and all that, people respect bikes.  There's this kind of side road, isn't there, where bikes can ride? There's a side road. And you know the latest thing that came up? There's so many bikes that now, even a bike laid, the traffic guides, the major restrictions, have a bar with the red light to hold the bikes back. And then when the light says go, the bar rises. And everybody in a group hoards on their rented bikes across the city. But it's amazing. But again, getting back to it, we need a community base. Second, technology through the apps. Third, we need to have e-vendors. We need to stop looking at the old platforms of credit card. We need to have the banks change. We need to think digital. This is what's all about, Jay. And you know what? We're not going to be in the global platform, because the Chinese are moving their stuff all across the world. So we need to get with it. We need to start to change our models. And the old traditional models monopolize industries. There's no chance for people to take risks and invest, such as this. So is this something that an American entrepreneur could do? Sounds like he could buy this technology. It's probably available. And he could rent bikes. And then his friends could compete with him. And we'd have bikes all over the place for $0.15 an hour or something like that. That's right. But here's the other clinker that we get back to the title of the show, leadership. We need good government leadership here to support it, who's going to work hand in hand to create incentives. And I'm sure in Honolulu, that's what happened to make the new bike sharing program. And that's wonderful to see the government progressive. But we need to get the banks also involved in the street. We need to get the finance industry. We need to sit down together and figure this out. If we're going to be truly a great place, we have to do great things. We have to let the entrepreneur take that risk. But we're not going to happen unless we don't have that leadership to say, OK, banks, we're going to have to sit and make it work. We want to have it so that let's call Tim Cook at Apple. We want to get your ideas how this fits in the American model. And maybe this model can roll out all the cities across the US. We need to have that happen. We need a people's revolution to the bikes. Yeah. You know, I'm just reminded that we only have a minute to go. But I'm only reminded recently, I forget who it was, it was the trust that runs Coppulani Park. There's a charitable trust that runs Coppulani Park. They said, no, don't put the bike share here. We don't want bike share. It's a commercial activity. We don't want bike share in Coppulani or around. It was actually on the periphery of Coppulani Park. I said, gee whiz, somebody should talk to them. We need a leader to step up and say, no, no. We need it everywhere. We need bikes everywhere. This could be a much easier place to ride than Beijing. Well, definitely it's beautiful. But isn't that what the park is for? A preview of the joy? You're not running like a motorbike across the park to injure people. It's clean. And you're letting people have access. So who should do this, the mayor? Well, again, your guess is as good as mine, but I think we need leadership to say, you know what? It's for the people. That's what's all about in Beijing. The government there sees it as for the people. And it's quirky. And believe me, I'm an American. I'm living a Chinese dream because you don't know how wonderful it is to avoid taking a taxi, getting stuck in traffic, having pollution. I go down deep into the subways. I do my 45-minute subway ride. I get to the other side. I'm happy because why? I've got my mobile bike. I've got my oval bike. And I'm going to ride for 20 minutes down to location B. And the air blowing into my face, it's been clean these days. Look, yeah, the air is clean. You look good, Russell.  This has changed your lifestyle, hasn't it? Well, it definitely changed your lifestyle on the outlook. Because you know what? And now it says that I can do anything what I want. I have freedom. And I have mobility. And this is what it's all about. I think that's what's happening in China. People are so enthused. If you go and Google that, you read the reports of what people say, you'll be amazed. And I feel the same way. It's wonderful. Even my American friends, they're riding bikes. When you visit each other by getting a subway and then get off and getting the bikes, taking a ride, we get our Garmin watches. And we record our rides. And we do an exercise. And we felt like, I've done something today. I've done something productive. I went to work. I got my bike exercise today. I got my carded basket. And I paid one quai for a few minutes. That's Russell Liu. He's a lawyer in Beijing and in Honolulu. Then he's had a biking epiphany. And it's really remarkable. And I think if you don't know about this, you certainly want to learn about it, just to see on a comparative basis how the Chinese have moved ahead. They've done an amazing job here. Look up OFO on Google and you'll find out. Anyway, thank you so much, Russell. Great to talk with you. Great to find out about this. And you do look good. Thank you, Jay. Say, Shay, say, Jen. Say, Shay, say, Jen.


Econometrikset, tekstit on panel data analysis and multilever modeling, typically focus on two different approaches, the random effects approach and the fixed effects approach. Both of these approaches belong to the family of generalized least squares or GLS estimators because they involve applying normal recursion analysis on a transform data set. So what is the problem that these techniques try to address? In a normal recursion model we have the predictors and the observed variables from the fixed part and then we have the random part that only contains the error term. In a recursion model for cluster data we also have unobserved heterogeneity. We have the unobserved term which here is called AJ. The key question that these when we start modeling this data is what do we do with the AJ? Should it go into the fixed part or into the random part? And the answer to this question determines whether we go for fixed effects modeling or random effects modeling. In the fixed effects model we estimate a specific value for each cluster. So AJ, the unobserved heterogeneity is in the fixed part and only the error term belongs to the random part. This is sometimes called the dummy variable model because the easiest way to implement this approach is not the most efficient way but easiest to understand is by adding a dummy variable for each cluster in the data. So if we have 20 observations consisting of five companies each observed five times, four companies each observed five times then we would add three dummies to the model and we would estimate a specific intercept for each company. So the first company is used as a reference and the intercept in the model is for that company and the intercept for the other companies would be obtained by using summing the intercept and the dummy for that company. So estimate the specific value for the intercept for each company that's the fixed effects strategy. The random effects model on the other hand puts the unobserved term into the random part and here we refer to the unobserved term as UJ because it's a random effect. We no longer estimate the specific value for each company instead we just estimate how much the companies vary from one another without estimating any specific values for any particular company. The random effects assumption importantly, approach importantly makes the random effects assumption that both UJ and Uij are uncorrelated with all X's. In the fixed effects approach we just assume that this normal error term Uij is uncorrelated with the X's but the Aij can be correlated with the X's here. So let's take a look at how these techniques differ and how the techniques are actually calculated and when you should be using one over the other. There are beyond these two techniques there are also other techniques that you could apply but these are two basic techniques that are fairly easy to understand. So the idea of random effects model is that we estimate one regression line. The fixed part gives the overall mean of the data and then we allow the observations to be clustered around the line. So for example this cluster of green observations is always above the regression line this cluster of blue observations is always below the regression line. In the fixed effects approach we estimate the unique regression line for each cluster. So here we have three different regression lines they have the same slope but they have a different intercept because we again estimate a specific intercept for each case and we can see that the lines are parallel because they are constrained to be so but they are varying in where they are drawn. In this case the random effects assumption holds and the regression coefficient from this model and that model the slope is about the same. When the random effects assumption doesn't hold then there are random effects approach produces inconsistent and biased estimates for the within effect. But the fixed effects approach which gives a unique regression line will estimate the within effect consistently. So how does fixed effects estimation approach actually work? It's fairly easy to understand. The idea of a fixed effects approach is that we mean center we group mean center all variables. So we subtract the group mean of each x from that x we subtract the cluster mean or group mean of y from y and then we run a normal OLS regression model to the transform data. Let's take a look at an example of how this is done. So our data consists of a few companies each observed for four years and we have two variables we have male CEO indicating whether the CEO is a man or not and then we have ROA which is return on assets our dependent variable and we want to know whether the CO gender predicts the ROA or explains ROA. So that's our original data and to implement the GLS fixed effects approach we first calculate the cluster mean for the color of cluster means for all these variables. So for the first case all CEOs are women so this is a zero male CEO. ROA 12% is the mean of these variables these values so this is the cluster mean for the ROA for the first case this is the cluster mean of ROA of these four observations that belong in the next case and so on. Then we run we cluster mean or group mean center the data by subtracting these group or cluster means from the original data and we get this kind of data. So now the ROA here has a mean of zero for each case male CEO has a mean of zero for each cluster as well. This demonstration also shows one important feature of GLS fixed effects approach. The GLS fixed effects approach assumes that all of these variables that we have male CEO and ROA vary within cluster and now we don't have any variation in the CEO gender within company and when we cluster mean center the data then all values for male CEO will be zeros and this is a violation of the third assumption of Richardson analysis and Richardson model cannot be estimated. So importantly the GLS fixed effects approach can't include variables that don't vary within groups. To include such variables we can go for the GLS random effects approach. So let's take a look at what GLS random effects estimation does. Again we have the fixed part and the random part and now we assume that the error term that the unobstructed heterogeneity term UJ is in the random part and it is uncorrelated with the predictors. This approach is similar to the fixed effects approach except that instead of cluster mean centering all variables we quasi mean center all variables and that means that we don't fully cluster mean centered variables but we do so only partly and then we apply normal OLS regression to the data. So let's take a look at what this quasi mean centering means and I'm going to use a text by Woolridge as an example. So here is the equation for the GLS random effects estimator and instead of subtracting the Y bar which is the cluster mean from Y we multiply the Y bar with lambda before we subtract from the data and the lambda varies between 0 and 1 which means that sometimes we fully cluster mean centered the data sometimes we don't cluster mean centered the data at all if lambda goes to 0. This equation also if we play around it a bit shows us that the GLS random effects estimator is the weighted average of the between regression and the within regression. The within regression is estimating regression using cluster mean centered data which is what the GLS fixed effects approach does and the between regression is the regression of cluster means on each other. When the random effects assumption holds the within and between effects are the same so that's one way to understand the random effects assumption and this is the key idea behind GLS random effects estimate. So the idea is that when the between effect and the within effect are the same then we can estimate the within effect more precisely by borrowing information from the between effects regression. In the fixed effects approach we eliminate all these between effects from the data by using the cluster mean centering in GLS random effects modeling we assume that the between effect is informative for estimating within effect and we use that information to get more precise estimates. So the idea is that the weighted average of the within regression and the between regression is more efficient than the within regression only and this more efficiency or precision is one of the advantages of GLS RE over GLS FE. Let's take a look at the lambda term a bit more and the different variance components. So in the lambda term we have the variance of the error term sigma u, then we have the variance of unobsord effect or unobsord heterogeneity sigma a and then we have t which is the number of time points and this is the within variation divided by within variation plus t times between variation and then we take a square root. So this kind of looks like an intro class correlation except that there is the extra t. So when we look at these different components that go into the equation we can make a couple of observations. So first if there is no clustering at all GLS RE equals OLS. So when the lambda is estimated to be 0 sorry when there is no within cluster variation then GLS RE will equal normal OLS regression. When there is no between cluster variation sigma a is 0 then lambda goes to 1 and GLS RE equals GLS FE. So the GLS random effects model is always somewhere between normal OLS regression model, ignores clustering and the GLS within effect model, GLS fixed effects model. Importantly when the number of observations or number of time points increases then lambda also approaches 1 which means that GLS random effects approach approaches GLS FE. So when there is a large number of observations then the efficiency difference between GLS RE and GLS FE starts to diminish because GLS RE approaches GLS FE and that's fairly intuitive because if you think that the GLS FE also uses only the within information but GLS RE uses between information. There are a number of between cluster differences that we have doesn't depend on the number of time points. But the number of time points determines how much information we have on how the cases vary over time. So we get more information from the varying time points that means that GLS FE becomes better and GLS RE then approaches GLS FE. So which one of these techniques should you apply? Before I go into this I must say that there are also other techniques that I explained in other videos that address the same problem that you could also consider. But normally in econometric books this is explained as a decision of between GLS FE and GLS RE. There are advantages of GLS FE is basically that it doesn't make the random effects assumption. But when the random effects assumption holds it is less efficient than the random effects assumption and also another major disadvantage is that the GLS fixed effects approach can be used with any variables that don't vary with inquisters. The GLS random effects assumption has two advantages. First it can include variables that are constant within groups so we can for example use individual's gender as a predictor when we study something that varies over time. GLS FE RE is also efficient when random effects assumption holds so the estimates are more precise. The major disadvantage of GLS random effects assumption approach is that it makes the random effects assumption and it is inconsistent and biased if the random effects assumption doesn't hold. Fortunately there are a couple of empirical tests that can be used for testing the random effects assumption and in practice people choose between these two approaches by performing one of these tests and then going with GLS RE if the random effects assumption holds and going with the GLS FE if it doesn't. But there are also other alternatives that you could consider that I'll talk in other videos.


So how would you rate the preparedness of Delta State as they host the country's hotlits? Yes, I would give the states a certain sense of what they've done in terms of logistic they've tried because this is the first national sport festival that will be having almost 36 states of the Federation in attendance. We have four governors in attendance and it has been superlative in terms of the logistics and in terms of the facilities, standard facilities, special indoor sport and wonderful tracks for the athletes so far so good. Delta State, who is the founding champion in this tournament who won the first medal in the mixed relay in cycling three days ago and they've also won some gold again in CIS actually. We have Emo State won first medal in that. We also have Edo State winning medal so the time for counting of medals has really arrived but as tournaments, there's some events that will start on Friday and Saturday. Tabetani started today and we've seen all your winning silver medal in the women's team so it's time for us to start counting the medals and everything has been going on well. Alright, so what's the update on the participation of startlets like Tobi Amusso, Ezebrumi and the likes? Yes, it's an authority that Tobi is not the representing of the states because during this period she'll be having the World Athletic Award because she's part of the five short-listed athletes across the globe to win the World Athletic Award of 2022 so she will not be there. There's possibility Ezebrumi will be representing Delta State but that has not been confirmed because we're expecting top-notch athletes to represent the state so it's hope for some good that more athletes will come to the event.  Yes, the medal table is between Delta States having two medals on the table so other states are also coming up but we hope that the states should talk the medal table but I cannot give you the finalist of what the medal table looks like but tomorrow we'll be giving the updates of the medal table after a collection by the collection centre. So much Mudashiru for joining us tonight, we hope to get more updates as the event continues. Hello, hope you enjoyed the news. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


Well, I'm gonna try to be a punctual person for once and We will get this thing started and I get to introduce myself, but two things Before we get started first off I'm gonna imagine that some of you have taken a class from me before or taking the class right now Are any of you enrolled in my class right now? Just one I have 430 students in my class right now one of them showed up you get an a Any of you take my class previously? Oh I'm so flattered. Thank you So I'm gonna introduce myself a little more thoroughly in a moment here, but most I want to explain to you what this Series is what the speaker series is. It's the degrees to anywhere Am I supposed to explain why we have that particular phrase? It's because one of our crappy state senators called a degree in the liberal arts a degree to know where um And so we're trying to say which was funny because he had one And anyhow But the idea degrees to anywhere. Yeah, it's true that if you're in the humanities the social sciences the liberal arts very often That degree in and of itself will not give you a direct professional qualification for something But really the whole point of coming to college is not to get your professional credential I mean, that's what that's what a technical school is for. It's to become educated and When you get your degree in the humanities the social sciences the liberal arts What it does is it prepares you for a huge number of things and so The point of the speaker series is to learn from people who have a background in one of these fields And have gone on to something that maybe you wouldn't have expected at first To see that it does prepare you for a lot of things now I know it's a little weird hearing that from a psychology professor, but I will explain why I'm Why I am here talking today So this is a Stephen, this is a monthly event, correct? Three times each semester. Okay So If nothing else it's free lunch three times a semester. So just remember this is where all the food is All right, it's like my daughter all three of my kids are students here and But I have one daughter who has said like they give out free fresh Fruit and vegetables on Fridays. All right, so she's like goes and stalks up and she knows where all the free food is I mean, she's not a starving student. We support her But there's a lot of free food to be had on campus and we've got a lot in this room So I will not be offended if you get up and go back and get some food while I'm talking Okay, also just a quick note this talk will is being recorded So you can share it with all of your friends and watch it again and again. I actually um I make videos and I had somebody who was from Pakistan tell me that She puts my data videos on repeat to help her child fall asleep I take it as a compliment um anyhow Very soothing very soothing um, you know I'm a psychology professor, but I teach statistics and um Some of the comments I've gotten on my videos are that I am either the mr. Rogers of data science or the bob ross of data science Very calm Anyhow, let me talk about this I'm going to talk about data work and liberal arts and my little secret clue here that this is a Data presentation is the double ampersand, which is the logical and in most programming languages So both of these things are true at the same time Let me start however by saying who is bart aside from me A few things number one. I have a phd in social psychology. I also have an mville and an ma and a bs In the field. I was in college for a long time um I've been a uv u full-time professor for over 20 years I've been teaching for about 28 years And uh, I have I have a one-class rotation. I teach psychology 31 10 statistics for the behavioral sciences Which I have now taught 116 times And the other 18 courses I've taught are 118 which means that in the spring I will have taught stats more than everything else put together um A little more is I also And this is why I'm here today why I'm a speaker here I also am a frequent author for linkedin learning now That's a service that all of you guys have free access to because there's a university site license And I make courses on them for statistics and data science. I've been doing this for about 13 years um And I talk about programming. I talk about how data science is used in healthcare and finance and sports and A lot of that stuff. So I do a lot of professional work there One of the interesting things about that is I also am very active on linkedin. I post lots of short videos on there My daughter got me doing just tiktok videos, you know holding up my phone and making short data videos and um including while I was riding my bike and didn't crash but um I also do twice a month. I do what are called live office hours on linkedin.  I also have a company called data lab dot cc where similar to the video courses I make for linkedin which are on Uh statistics and data science. I do the same thing at data lab, but with linkedin's blessing Um, I don't sell them. I don't make any money through data lab dot cc I give that stuff away for free But there's also a way that I connect with a huge number of people and has led to a lot of paid work for what I do also Um, there are courses there that well If you don't know how to use a spreadsheet because I am going to talk about spreadsheets You can learn how to use it there and they're the downloadable files. It's it's all there I also sometimes work as a data consultant. I have worked for Tiny nonprofits. I have worked for local business consultants. I have worked for Several global corporations and I've worked for the u.s federal government Um doing data for them two other things about me I actually studied design Um from the time I was a tiny little kid until I was a senior in college I was going to be a designer And I did an informal internship at general motors which served one of the important purposes of internships and tell you what you don't want to do And so I kind of ran away screaming after that switched over to psychology and still graduated in you know, five years total, um Design is still very important to me. I did have a drafting table and I did use a french curve Also dance That's unexpected, but my wife is here. She's a modern dance choreographer and several years ago. I took a sabbatical And I spent a year at the u of u to study Some of the elements of data visualization. It's an important part of data work And I had most of it already, but there were some things that I still needed But while I was there I was enrolled in the Certificate program in arts technology in the department of art and art history And I had to do a capstone project and I just got a microsoft connect I hooked it up to my computer and I had dancers do short improv's which we did uh motion capture On them and they turned that into this giant three projector gallery piece turned it into some printed pieces to show it another gallery And then that led to My wife and me getting commissioned by repertory dance theater one of the major modern dance companies in salt lake to produce a piece for them This is a still shot from that performance And then I got a presidential fellowship for faculty scholarship here at uvu And I spent two years working on live video looping and manipulation for modern dance performances on stage Fabulous work and it is because of my interest in data that I learned how to do those things um So But why am I talking about data work? And what does it have to do with you? Well a few things Number one when we talk about data You by the way data is the british pronunciation In america we say data Telling you that also data is technically a plural noun Because it's the it's the latin plural of datum But it is in english it's a mass noun like air or water and so you can say the data is Anyhow when we talk about data the first thing it comes up for a lot of people is statistical analysis And that's something I do a lot. It suggests number crunching. I do that. I know how to do it I teach people how to do some of it and it also just like random factoids Here are some statistics about you know this year's graduating class boom boom boom boom That's all useful. It's good, but there's much more to it than that Now there's a closely related field of data analysis And it's broader than statistics It includes visualization, which is a huge part of I mean just and truthfully just simple graphs bar charts line graphs um Are gonna get you maybe 90 of what you need And then it also includes the interpretation of the data which it turns out is the hard part And that's something I'm going to talk about a fair amount today And then if you want to go a step beyond that there's data science Now data science is a new field really Came into its own about 10 years ago And data science adds computer programming because it works with a lot of Data that doesn't fit into the rows and columns of a spreadsheet And it also has a heavy emphasis on an applied context. So in a business setting And so the people for instance who develop the what are called recommendation engines if you're on netflix or if you're on amazon It says you might like this That is a data scientist who created that Based on your browsing history to make these other recommendations now These days those people are more often referred to as machine learning engineers But it is still the same general field as data science But why am I talking to you about this? Well for a couple of reasons number one is You know you're in college, but you're gonna not be in college one of these days We hope and you're gonna be looking for a job. And one thing is there are a surprising number of people with degrees in the humanity social sciences and liberal arts who have ended up being full-time data specialists Degrees in data science are very recent I'd say only within the last five years is it possible to get a degree in data science Previous to that everybody came from somewhere else And often you would think it would be computer science or mathematics. It wasn't Some of the most influential people in data science came from political science and came from A shocking number came from astrophysics um But they came from many many different fields And it is possible to be a specialist in this now obviously if you're going to be a full-time Specialist in the field like this you're going to need some additional training need to go get a master's degree In the field But you may also end up working in a field that is adjacent to data. So you might become a researcher And so Data isn't your full-time job, but it is an important element of what you do So for instance me I I am not a full-time data scientist what I do however is a lot of work that connects with it And I can and also I I spend a lot of time talking with data scientists about things And so you may end up in an adjacent field and truthfully There are massively more opportunities in adjacent fields if you want to be the development director for a non-profit That's the person who raises money You're in spreadsheets all day every day if you want to be A human resources director for a company same thing if you want to manage a Uh an online social media business same thing Those are ones that you have to use the data in order to do the job well So I think of those as data adjacent fields You got to have it And then this last one MYO be mind your own business a lot of you Um if you're for instance Most of the students in my class Because my class is in psychology and it's mostly students from I'd say the single most common goal of my students is to become a therapist either a licensed clinical social worker or a Marriage and family therapist or a clinical mental health counselor You're going to be a therapist and for a lot of these people the moment you become a therapist you also become a small business owner And it turns out That smart people who have specific professional training in their field don't necessarily know how to run a business This is true of lawyers and dentists and doctors and accountants as well And so the ability to work with data Enough to take care of your own business is going to make a huge difference for you. I have made Important business decisions based off A single bar chart When I finally took the time to get some data together put it all together So like this activity pays really well. This one doesn't so we'll do more of this and less of that But the real reason I want to talk to you guys about this is because data is human data is a human activity Now I have an event. We call the data charrette. I'll say more about it later But this is our we had stickers for this and it says data is love and community Driven data for good Now there's a little bit of hyperbole in that it makes a cute sticker. But um But here's the deal Data is not something that is naturally occurring in the world Data doesn't exist on its own the same way that like the height or the density of a tree exists data Requires humans humans design the methods to find data to store it and to analyze it and it doesn't exist until that happens And it happens because data is used by humans to reach human goals. Okay, so ready Again data is not something that exists naturally in the world. It is created by humans for human tasks Even if it's machine I mean most of the data gathered these days is like in warehouses As a box goes by and it gets scanned automatically and it goes on but you know what yeah machines doing that But somebody created that machine. They created that technology. They created it to serve their purposes And so it is fundamentally a human activity And it gets us to the idea You know the little saying if a tree falls in the forest and there's no one there to hear it Does it make a sound well as a psychologist who can teach you the difference between sensation and perception? I will say you there is a correct answer to that question falling trees do not make sounds Falling trees make waves of air pressure But for that to become a sound you have to have a sense organ that is designed to receive it You have to have a transduction process that converts it into a neural impulse and you have to have a brain That is adapted to perceive and interpret sound So unless you have those three things around there is no sound. There's just variations in the air pressure The same thing is true with data. You can say if a tree falls in the forest and there's no one there to measure it Does it make data? No, it doesn't because data It it'll do stuff that could be interpreted as data But there has to be a process somebody has to be there to measure it one way or another I mean you could just go in to say like how many trees fell down So the trees are there, but it wasn't data until it got counted And so that's the important thing. It's not something that exists out there. It is the result of a human activity Now there is a need Data needs a story so it can take form and fulfill its purpose It is nothing until a human looks at it And interprets it Okay, it's nothing until it is perceived and interpreted And so the fulfillment of that need Is we need humans who are story makers and the humans who study stories do it best And I believe that people and the humanities the social sciences and liberal arts Are actually best situated to do that Not just people in my field psychology Or the broader social sciences anthropology and social work But people who write fiction People who create films People who study history All of these things are important. I actually have more books of poetry in my office than I have on statistics Um, and it's because I have gotten some of the best insights in my life into human nature from poetry Um or from movies or from I remember I have a I have a I have a poach. I have a poem on my door Um, I suddenly can't remember the poet's name But it's called um having it out with melancholy And it's about a person writing first person about their experience with um A bipolar that manifests itself mostly as a soul crushing depression And I think it is the best example. I know of How it feels and how people should try to understand it. I remember seeing an opera A few years ago called the long walk, which was about a soldier from the war in the mid-east suffering from um post-traumatic stress disorder I felt that opera was the best illustration of the principle I had ever seen and so People who know how to tell stories people who know how to take information and find the story in it Are truly the ones who are in the best situation to make sense of data And to fulfill its potential um, I'm gonna have a little brief interlude here on this to talk about um dumpster fires Or really horrible research that I have seen and I had to be selective Okay, because I've seen a lot of really crappy research Let's talk about dumpster fire number one the ai gaydar So a few years ago some people got the brilliant idea that they would develop a machine learning algorithm That could predict a person's sexuality from a single profile photo on a dating site Why they thought that was a good idea is not clear to me and whether they ever thought that this could potentially be used to harm people Again never seemed to cross their mind. They thought we're doing something. We're building something By the way, this was the same people behind the Cambridge Analytica scandal Where they were scraping huge amounts of data from facebook for political candidates many years ago. Yeah, whoops And yeah, so we have some people who don't consider the implications of their actions But predicting sexuality from a photo. Okay ready? They did in fact develop a method that was able to predict above chance Whether a person was gay or straight And so they saw that as success And they claimed if for instance in the case of men They said well the reason we can do that is because gay men have been subject to in utero feminization And they've caused their faces to have different features gave them smaller chins larger eyes a bigger forehead And that's what our algorithm is picking up and they're inventing this very elaborate Theory about and said and that's what makes them gay well, um, some other people came by and Looked at the same data and they didn't actually say hey dumbass, but what they did say It's the angle of the selfie. It's how high are they holding their camera? Many women know If you hold your phone here when you take the picture you're getting like the double chin Or in the in your nostrils, but if you hold it up here, it looks better Well, when you hold it up here, you have a bigger forehead. You have a smaller chin And so they invented this entire theory about hormonal Hormones changing the structure of the face like It's where you held your camera. You guys are familiar with Occam's razor The simplest explanation is usually the correct one and holding your phone a little higher is a much simpler explanation than This whole thing about changing the structure of the face They also concluded that they had all these things about you know predicting Lesbian women and they they also came back saying like it appears to be whether they are wearing glasses in the photo And have short hair and they're not wearing makeup Yeah, surprise. So They were the shockingly easy things and you say like you did not need an algorithm to do any of this Okay, so let's talk about dumpster fire number two low birth weight and I got this from a textbook on machine learning And again, I I do not cite the textbook because I'm trying to protect this person But they wanted to run through this whole giant example of how to use machine learning to predict um When a fetus was at risk of being born with a low birth weight Okay, that's a really important thing to know Because if you can predict that ahead of time if there's anything you can do about it that can I mean that's an actual life and death situation That's important And after their very long extended conclusion, they said if they're twins or triplets, they're going to be smaller Once again you go But yes, you simultaneously wasted, you know half an hour of my time gave me a completely obvious solution And it's useless because there's nothing you can do about it. Okay ready here. I go don't have triplets Now that's true. There is something you can do if you're doing fertility if you're doing in vitro Yeah, you have a higher risk of twins or triplets, but There's nothing you can do about it and I'm going to come back to that theme your analysis When you get your conclusion, there should be something that you can do about it Simply stating the obvious if twins and triplets are smaller than singletons is not helpful Okay dumpster fire number three The goal here was to develop an algorithm to recommend sentences for people who have been convicted of crimes um Again by people who didn't necessarily take the time to think whether that was even a good idea in the first place Well, they claimed that their super secret proprietary advanced, you know Method was ideal by what criterion we don't know um It was predicting risk of recidivism, maybe but it it was it was really problematic anyhow, um The public service agency pro-publica came out and said hey, guess what we got the same dataset and we could recreate the Accuracy of that thing with five sentences of if this then that if this then that Five lines as opposed to this super secret proprietary machine learning mojo um also While i'm a big fan of ai and machine learning and data science in general One of the most successful courses i have on linkedin is the one that talks about all the things that can go wrong And how social biases tend to just get replicated and exaggerated One of the big problems that happens and again, this is why I think that people in the The liberal arts the humanities and social sciences are important here Is because when people develop an algorithm other people just go along with it going to go Oh, the computer said so the computer knows the computer's full of crap If you gave it biased data to start with and all it does is reproduce the bias just In a much faster more scalable manner. You have simply taken the problem and made it bigger And that is true for so much um I have a news feed and I get two or three stories every single day About machine learning going haywire um You know they The most amusing one it's not machine learning per se, but it is technology going bad is uh There's a company that makes electric bicycles called van move And they're very fancy And you use your phone to unlock them and it's it's all very cool. Well van move went bankrupt and suddenly The servers that unlocked the bicycles were not available And so everybody who owned a van move could not ride their bike And so they were calling the police to say basically that van move had stolen their bikes from them Even though it's right here. I can't ride it Um Van move got bought out by somebody else. I think they have maybe fixed that problem But the idea is tech is not always the solution and you have to think about some of the consequences of your actions And again people and this is one especially with people with a background in history political science are gonna say We've been through this before there's a problem. You need to be aware of it Okay, and then let's do dumpster fires ad nauseam Where somebody gives you a data sense and says just analyze the data find something And so you go through it and you find random significant findings and I put I put significant in quotes there Because you may those of you who have statistics know that that word has a very special meaning in statistics It means a finding Where the probability is less than five percent of getting that finding if the null hypothesis is true And you have to say that whole thing Um But people don't pay attention to that they get a significant finding They think that means that it's big or it's important or it's meaningful None of that is necessarily the case So they say well, I got the data and I found like four correlations that are have a p less than o five here They are And then the crash is nobody knows what to do with it and it ends up just being junked You know this happens, right? I have Recently meaning in the last month Read a report by a professional researcher that fell exactly into this category Where I don't know how many thousands of dollars of resources has been used in gathering the data and doing the analysis for that report But when it was all said and done It was completely useless They kind of highlighted. Well, here's this and here's that but yeah, okay, there you go And you say what what what do I do with it? So this gets to the point why this is relevant to you and that's because You can work in data and you can build on your strengths Here's here's a few things. I mean I'm going to show you three activities ready to from not so hard. It's a harder to hardest Not so hard is the technical skills. Anybody can learn to program in python It's the simplest language in the world. Anybody can do it Okay, so when people say that they're part python programmers sort of like saying hey, man I know how to write a complete sentence, you know, it's not that hard. You can learn how to write python in a week You can learn how to use a spreadsheet in three hours The technical skills are not hard now I understand that there is a craft to this and there are people who take pride in it But I would again I would say 90 of the work You can learn how to do it in a few weeks It's really not that hard what is harder Is understanding the organizational culture of the group that this came from the and what sorts of answers work well for them and what's hardest of all is developing critical thinking so you can avoid the dumpster fires The people who developed the ai gator were technically extremely competent people but functionally they were idiots And they did not take the time to realize that they had gone straight off the deep end and their conclusions Develop the algorithm great. That's wonderful. You know what? It's really easy for you to get online and hire somebody to develop the algorithm for you But interpreting it and using it responsibly It requires the kind of critical thinking that I believe that the liberal arts the humanities and social sciences That's their specialty That's the most important part um There is a saying and I have to break this up into four sections because it's a long saying But this is a well-known truism in the data world It is easier to teach technical skills to someone who understands your organization your business model Then it is to integrate a person with technical skills into your organizational culture Take somebody who understands how the world works teach them how to program Much easier than taking somebody who knows how to program and teach them how the world works okay You guys are in a situation where you have special training in how the world works That's going to be the critical part the technical skills are not difficult It took me years of graduate school to develop the way of thinking That has stayed with me ever since then I picked up the programming on my own Because it's not the hard part Understanding the culture the organization you're working in and understanding human nature and where things fit And what makes a useful response and what makes a useless response? That's the hard part And that's why being educated is often much more important than having the technical skills you can acquire the technical skills Okay The next thing is In terms of what your skills should specifically be focusing on when you're working with data is number one What are the goals of the project? So often people get started on a project or somebody hire somebody to do a project without ever specifying what their goals are and what this leads to is weeks or months of Back and forth or waiting or wasted effort One of the most important things you can do before you get started on a project is to know exactly what the goals are So you know when you've reached them Um and to avoid extra effort The other one is can you interpret it? Can you make sense of it? Can you tell a story about the data now? I'm not saying like once upon a time But simply being able to draw a thread We want to know this and so we got this data and that connects with here And it means this except for when it breaks down this way and this is what we do as a result It's a simple story, but the ability to thread it through I recently Was paid a huge amount of money to develop a course for practicing business analysts And I taught the course three times with people who worked for one of the largest corporations in the world And they were professional analysts in that company I would say that probably Three out of the 50 people I worked with knew how to walk through the data So that you knew what they were saying And you knew what to do as a result of it Many of the others eventually got it some of them. It was just it was just bouncing off And it never got there. These are people who are employed professionally as data analysts And the last one here is when you work with data, you're trying to give people a roadmap You were trying to say do this the phrase is actionable insights And I know that that's a very jargony phrase, but it's an important one. In fact, I will say more about that Actionable insights is the key term put that on your resume your letter app Letter of application if you say, you know how to design An analysis for actionable insights You're good. You got the job. Here's what we mean What is somebody actually going to start doing stop doing or continue doing but perhaps with more confidence now As a result of your analysis if you can't tell what changes as a result of your analysis You have not accomplished your goal What are they going to do? That's why it's called actionable insights and insight is something that you find in the data Oh, we found a pattern here or we found a difference between two groups. Fine. That's nice But what are people going to do? That's the actionable part and until you can say what people will do as a result of your insight you have not Done your job Okay Now let's answer the obvious question You know doesn't chat gpt do that Can't we hire chat gpt for free to do everything? Can ai do it? I'm a big fan of ai. I have the paid professional account to chat gpt and I use it frequently I like it, but there are issues Actually, let me just back up You guys are aware of the hallucination issue with ai Are you aware of the lawsuits Where loy first the best one is the is the law firm that submitted a brief that they had chat gpt right and it totally made up non-existent non-existent cases and created citations for those they submitted to the judge and Not only did the judge call bs on them. They got severely reprimanded for Submitting a work of fiction As their brief Chat gpt is very useful except for when it isn't And the problem is you usually have to know well enough You don't have to know what the right answer should look like So for instance, I I have people who have asked for chat gpt to write some code in c++ And that it was doing fine until it stuck some javascript in there, which is a different programming language Um, I have had it I've had it Try to come up with some statistical examples and it uses the wrong terms and it gives impossible calculations You always have to check chat gpt doesn't do math It does something that sounds like math it resembles math If you have the pre if you have the paid version you can hook it up to wolfram alpha And wolfram alpha can do math But you're gonna have to pay 20 bucks a month to get that um Otherwise it makes stuff that looks it chat gpt is great at generating plausible answers That they look that they're correct. Unfortunately, it is not ready to take over the world um this right here is a report from linkedin is it's uh They released a frequent one on the future of work report And this one was specifically about ai and this one was released in august of 2003, but it was all done in response to the november 2022 Is when the ai world changed because that's when chat gpt came out ai existed before that I mean ai goes back to the fifties Um, but the modern version of ai which is based on deep neural networks and large language models That's been around for a few years, but it wasn't till chat gpt got um Released in november, which by the way was the exact same day That linkedin published my most recent course on ai accountability. So like oh great now. It's all immediately obsolete but um The future of work report by the way That'll get you the pdf um Let me tell you a few things 92 92 of us executives agree that people skills are more important than ever And this is a survey done right after This is subsequent to chat gpt coming out. They're saying we still need people skills And then here is linkedin reporting on the growth in demand for various job skills since november of 22 Which again is when chat gpt came out These are the four areas that have shown the most growth in job ads on linkedin Look at this flexibility Professional ethics social perceptiveness and self management. None of which a computer can do These are the things that people in the humanities the social sciences and the literal arts Specifically develop skills in these are the most in These are the ones that have shown the biggest growth and if you want to know In the us this again, this is according to the same report by linkedin communication is the Number one top skills sought across all job postings Any of you communications majors way to go you win But the ability to communicate clearly with other people Is going to be the single most important thing. I have a sister who is um Who actually has a degree in communication and She has worked in public relations For years and years and years and But she has worked in the tech industry For the last 30 years And It started out with her doing legal pr for tech lawsuits But now she has many clients in silicon valley. She lives in san francisco And she she is training them how to talk on stage They have to be able to explain their product or their service or their company to other people and again These are all very very technically smart people Um, and she is making something along the lines of half a million dollars a year teaching them how to communicate So Good job All right, so now what do I do? What do you do now? Okay, here's a few things. I'm gonna recommend number one Think about it. Think about the possibilities that are out there. You might like it When I started teaching in graduate school Um, I had this fellowship that was paying for grad school But I I was I was sort of a useless Fourth member of a research team And I just kind of sit there and say like I I think you've got the period in the wrong place You know eventually my my advisor said, you know bart, I think you should go do something To earn your fellowship And she said maybe teaching now I was at a grad school that had no undergrads the city university of new york graduate center And so I had to shotgun Paper letters out because this was like pre email I had to shotgun out paper letters to all the undergraduate campuses that That were part of the city university of new york system And I said, okay, I can teach like the following eight courses and I can do it for free because I'm already paid And I was crossing my fingers and saying dear lord Let it not be statistics or research methods, which of course is exactly what I was offered And I was panicked And when I was teaching statistics, I was barely ahead of the students for the first semester The strangest thing is I have come to love it Keep in mind. I'm a person with a background in design. I spent more time taking classes in color theory and drawing And 2d design And I ended up loving statistics Even the calculation part of it. You never know until you're actually there You have to try it So think about it. You might like it think about some of the possibilities.  Just look around you and see places where maybe a little bit of data would help Where things look like they're not going very smoothly. Things look like they're kind of silly See places where you think data might help them make decisions. This is They're all over the place Maybe they could do something with the parking lot. I don't know But there's a lot of things you could do also a little bit of statistics is nice I keep saying that I tell my students I will keep saying that You don't have to have an enormous amount But if you know what a correlation is If you understand a basic regression model If you know the effect of outliers You've got a great beginning The statistics again is not the hard part, but you have to have at least some to go with it And then spreadsheets. I'm going to give you a tool Microsoft Excel or Google sheets. No seriously really If you can work a spreadsheet a couple of things happen number one you suddenly have an employable job skill There are people out there doesn't need to be you but there are people out there Whose jobs consist of working in spreadsheets all day every day And if you end up one of those people you can learn how to do what's called vba Which is visual basic for applications That's a programming language that works within excel And then you can get your eight hours of excel work done in one hour and go watch movies for the rest of the day I know people who have done this They specifically got a job that where they were in the building by themselves at night They would launch their vba script and literally go down the street and watch movies But they had they got all the work done Um Any a little bit of spreadsheets. In fact, I'm going to quote the wall street journal on this one now This is from 2015, but I do not think that this is changed appreciably The key to a good paying job is Microsoft excel to which the article says the answer is yes A little bit of Spreadsheets can make a big difference. The nice thing about spreadsheets though is they're really easy to learn And you use them all the time. I had four spreadsheets open while I was preparing this presentation I use spreadsheets every single day And My favorite one I got one of my kids is here Several years ago my wife and I um, we took our family on a trip around the world And involved 16 airplane flights And our kids were young and we were going to places that were important to them But also because kids are kids. I didn't want them arguing about who got to sit at the window seat I created a spreadsheet That calculated how much time each kid would have At the window and making sure that they were there when we landed in a place It was important to them and I showed it to them. I did my best to balance the time We had some other constraints on it too But you know, it really was sort of thing like I've thought about this and this is the best I can do So please don't argue Um I use them all the time Um also I have an event we call it the data charrette now charrette Which literally means a small cart in french But it's a term that's used to revert to creative activity under extreme time pressure And this is something that I've done several times in the past And what it is is in the past we would do this over two days We would partner with two local nonprofits who had data and they had a question But they didn't know how to use the data to get their answer And um Over two days we would take their data. We would get it all cleaned up which usually took a day and a half And then we would go through the data and come up with an answer for them As often as not it reduced to making what's called a pivot table in in a spreadsheet A pivot table is a very employable skill It's a way of collapsing the data in a way that makes it very easy to understand what's going on We're going to be relaunching the charrette In november so that is in about six weeks And we're going to have two local nonprofits. We're going to be here on campus It'll be a one day event just a saturday And we'll have food and it will be an opportunity for you to come and work with some data again The data that we're working with is not going to be complicated It's the making sense of things and telling a story about it. That's the part that we really need help with And so when we do this event on saturday november 4th, and then again in the spring on saturday march 2nd I would love to have you there. You can put it on your resume One of the conditions i'm giving to the nonprofits is that they will also give a linkedin endorsement for anybody who contributes to their project This is a great thing to do. So if you don't have a linkedin profile now's the time to get one And so november 4th and march 2nd We're doing these free events on saturday. You get to work with data. It's it's one of the most gratifying projects i've ever worked on And with that I am done and here's my little Link tree that has the links to everything else, but folks. Thank you very much Now I realize that some of you have to go to class But I I am here for a few minutes if you have some specific questions If you have questions, just raise your hand and we'll bring you the mic so that we can get it on the the recording Oh, yes What do you study like? Where do you learn all the knowledge to know now? Say it again. What do you study? Like to know everything you know now um So my degrees are in psychology. I have four degrees in psychology. I taught myself statistics um, and one of the things I think has actually been the best in terms of my professional ability the reason I'm Is I have 30 years of experience teaching statistics to scared undergraduates who don't want to take my class And so I'm able to find a way to make it approachable And that has made a very very big difference in my In the work that I do I said Candida What would be some advice you would give to students who are looking to make a jump from something like Psychology into learning more about statistics Okay ready know how to work a spreadsheet I mean seriously start with a spreadsheet if you can make a bar chart a line chart a scatter plot and a pivot table You literally have 90 of what you need You can you can then use to learn you can then learn to use a um an application like spss Or I actually prefer jamovie, which is a free open source program that resembles spss And you know you can learn to use a programming language like r or python, but You don't need those to work with data Again start with the spreadsheet Again and just creating some data in your own life. I mean seriously count how often you have like, you know How often you have to feed the cat how often you know you've got to change the temperature Just start with data sets that you can get any of them just get started again If you can make if you can sort the data if you can make a bar chart a line chart and a pivot table You're great Okay, um over I'm in my mid 40s over the course of my life. I've taken the Myers-Briggs personality assessment three different times And something that kept kept coming up was counselor some kind of counselor counseling though So that's why I'm here in humanities. Um, but I've heard criticisms of that as like a pseudoscientific kind of like the botched AI Like how how valid or credible or viable is that? You asked me about the mbti. Yeah I'm gonna give you my take on most tests. Okay. They're great for starting a conversation Okay And that's where most tests should end. Okay. Thank you. They're great for starting a conversation One one add on to that real quick though and it does ties in with what you talked about earlier being like the complementary to the tech world like People need us this people skills. Um, I had career counselor Interpret the results for me That's all like community college. I think they scrapped the program now. They don't offer that anymore, but She gave me some really good insight and that was You could go into a company with your people skills and be like the missing little link that they don't have And do just great even though you're not like profoundly, you know data driven or tech driven They need you so like if you can get in there you could you could have that niche kind of job No, I believe that's true and I believe if you have just a small amount of technical skills It it works wonderfully, you know There are so many examples of people who have who have risen to prominence In fields that require, you know specific skills. It turns out that that person doesn't really have that much But they are often I don't want to even say his name, but Elon Musk I don't know how smart that guy actually is I don't know how much of this stuff. He actually does and I do think he's a total psychopath Um, and so I don't want to hold him up as a model But I think what it is is He makes things happen And he connects things and makes them happen again I don't know what his actual contribution beyond being the motivator is to any of these things But there are lots of others one of the great american architects philip johnson was never a very great architect in and of his own He's graded like two buildings that are well known But he was the mentor to An entire generation of america's most influential architects. He made those things happen. That was his contribution Okay, we have time for one more question anybody Why did you become so passionate and moving from psychology to data? It was it was a totally accidental change um, not something that I anticipated and It's it's hard to explain. You know, what's funny is my career has actually changed several different times Even while I've been in psychology the whole time I spent several years studying, uh, the legal system and restorative justice Then I spent several years working on creative coding and art Then I've been doing all the research and this is all well employed here As a psychology professor. That's a nice one of the nice things about being a data person There's a there's a there's a great quote from one of the guy named john tookie And he says the great thing about being a statistician is you get to play in everybody else's backyard Uh, you get to do all this other stuff. Um and see whatever other people are working on And I love doing that and truthfully it's because I actually get a lot of gratification from being helpful And a lot of people get very panicky bad statistics And I feel that You know, I sometimes joke that teaching statistics is like, you know, given colonoscopies and the worst, you know The best you can hope for is that people go, okay, it wasn't as horrifying as I thought it would be But I like being in a situation where I can help people make sense of something And and I know for instance a lot of people they're worked They're just working on their academic papers and they go I got data I don't know what I'm doing and be able to help them out and walk them through it It's enormously gratifying All right, thank you so much And we hope y'all enjoyed today's session and that you'll join us next month For our next degrees daniela speaker. So watch your emails for that