Can you hear me? Checking the mic. Checking the mic. Good morning, everybody. All right, so I'm going to talk to you today about coupling natural human systems at the decision-making scale. And I'm going to do two things. I'm going to talk about coupling models in the first half. And then in the second half, I'm going to say why we're trying to do this at the scale of the decision-maker. And the second half is really in collaboration with my student Benjamin Minin, who's up here at the front. So I flew here on Monday from Southern Ontario, University of Waterloo, which is about 100 kilometers southwest of Toronto. And this landscape in Southern Ontario is incredibly human-dominated. There's about 13 million people in that area. There's about 52,000 farms. And competition for land is really intense. Housing market is really expensive. And there's a lot of drive to increase and sustain profitable farming for small family farms in the area. And this isn't just true for Southern Ontario. This is also true around the Great Lakes, and in particular around Lake Erie in Michigan, Ohio, Pennsylvania, New York, and the US. Now the basins around these lakes are shown in this slide here. But Lake Erie in particular, shown in yellow, has this basin which incorporates the majority of those farms in Southern Ontario. And the runoff from those farms is leading to massive eutrophication of Lake Erie. So we're getting massive algal blooms, which is creating a lot of toxicity.  City of Toledo has had to shut down the pipes from extracting water from Lake Erie on multiple occasions. And part of my research program is to try and mitigate these issues, like many people in the room today. And so through that program, we're trying to quantify the impacts of best management practices. These are things that farmers are doing in their activities, try and reduce the amount of runoff, the amount of nutrient flow to the hydrological system. And we're trying to identify critical locations for applications of these BMPs, learn how to better incentivize farm adoption of the BMPs to generate more sustainable land management practices. But of course, all of this really requires decision-maker buy-in, requires data, and of course it requires models to prove that mitigation practices work. It's like a good modeler. I went back to my modeling process and I thought, okay, we're gonna formalize what system we're working in, use a conceptual model to foster discussion, operationalize that in code, create a computational laboratory, work through some results and keep iterating, improving the system that we're working in. But what we see is we got two camps of work that are ongoing. Got a group of people in the human systems. We've got a group of people in the natural systems. Some cases we get interaction between those groups or at least in terms of the modeling that's represented. Okay, so we'll have a lot of human system modelers that will do a really crude representation in the natural system, maybe using an inventory approach or benefits transfer approach, where they'll say, we have, for example, land cover change, then we'll have average value of carbon storage associated with that land cover change. So if we lose forests, then maybe we're losing temperate system, 9.8 kilograms of carbon per meter squared, something like that. It's a really crude representation of what's happening. On the natural system, there's a lot of prescription going on. We're prescribing how human activities are changing that system, but there isn't this dynamic feedback between the systems and interaction that's happening. There's very few people that are doing that. And some of the separation is put in there, partly because of the funding initiatives that are out there. Okay, so in Canada, we've got the social sciences and humanities research council. They provide 388 million towards social sciences and 1.1 billion to the natural sciences. So social sciences is only getting about 26% of that funding. We're seeing similar outcomes in the US and these are just numbers that I'm pulling off the internet so don't hold me to them exactly. But in the social sciences, we're getting about 250 million. And in the natural sciences, we're getting one and a half billion to math and physical sciences, 906 million to geosciences and engineering. And so the social sciences really getting about 8% of that. Now there is those initiatives coupled with natural human systems, which are coming out in there about 18 million this year in funding, but that's only going to fund six projects, maybe seven. So there's this embedded disparity between social sciences and natural sciences. And then there's this focus on these different funding agencies which are kind of keeping us separated. And so one of the ways that we can start to harness this is that we've got specialists in the human systems, specialists in the natural systems. And instead of having these crude representations of interaction between them, we can harness those specialists and their work through coupling models together. When I talk about model coupling, what I mean is a coordinated communication between models. And so this figure on the right is showing on the x-axis, the frequency of communication and the degree of coordination on the y-axis. And the majority of this coupling that's taking place is in the center row, which is obviously hard to see from the back of the room, but it's a unidirectional flow from one model to another, kind of like our prescription that's happening in the natural sciences. Okay, and this is true for most of us in many cases. This is my research, I do this all the time. Here I've got a human dominated landscape in Ohio with forest cover and ecosystem models that wanna represent ecosystem function like carbon storage or point-based. We multiply, it represents the vertical system very well. We will run those models and then we'll multiply them out against the area that those models represent, kind of like in a hydrological model, we'd use HRU and apply that to the landscape to estimate evapotranspiration, carbon storage, run off those sorts of things. But we know that's not the case. We've got patterns that affect how ecosystems behave. And in this case, I measured air temperature treatments and humidity from the edge of the forest, the interior and reparameterized the models along different swaths of edge effects to show that we get massive amounts of carbon storage differentials depending on the size and shape of the patches. We can transfer this to the human system as well. So here's some work that we did where we were coupling an agent-based model to BioNBGC ecosystem process model to investigate how land management practices on residential landscapes affected carbon storage when we're adding to the landscape through irrigation of fertilization or removing coarse woody degree, grass clippings and those sorts of things. And these are giving us massively different carbon trajectories. So many of us are doing these types of unidirectional impacts. But what we want to get to is the upper right where we're having bi-directional interaction between our models and feedbacks between our models. And this inclusion of the feedbacks and how we represent them really increases the non-linearity and the variability in our results and gets us closer to understanding and getting insights about the systems that we're actually interested in. And so this was the focus of a CSDMS workshop back in 2016 where there was about 30 of us here that were working in this area, putting our minds together and sharing our experiences. We published this paper in our system dynamics last year, making comparisons about the different types and styles of coupling that we did in our research programs along with how the feedbacks were represented in those different initiatives. And then we also identified a number of lessons that we learned as a community. The first lesson was about remembering modeling as an iterative process. And so I highlighted that already, but in this case, when we're starting to couple models together that haven't been coupled before, there's data that we haven't typically collected. And so one example is that when we combined the agent-based model with BioNBGC in the residential landscape, we realized that residential landscapes are really void of any data about carbon storage and the soil and the vegetation on those properties. So that fostered new proposals and new papers that we're exploring in quantifying carbon storage and residential landscapes that we're now bringing back into the modeling process. Part of that is also leveraging the sensitivity analysis of our models. So when we couple them together, we don't always know what parameters or variables are gonna be really important. What processes are really gonna affect the feedbacks and interactions. So we need to use sensitivity analysis to identify those pieces to foster where those additional research efforts are gonna go. We also noted creating a common language, as you've all heard the standard names convention that CSDMS is putting forward, that's incredibly important, not just for communicating between human and natural scientists, get on the same conceptual page, but it also feeds into the next one in terms of making code open access. If we can interpret that code easier because we have the standard naming convention, it's gonna help us out a lot more in making progress. Making the code open access here is about making sure that we provide hooks and leverage points between our systems. So for example, in ecosystem models, there's often mass balance equations that are provided, but those are just inherent in the natural system. We don't provide hooks and leverage points for us to add fertilization and manipulate some of the nutrient cycling activities that are going on in there. And so if we do that, then we can foster the greater collaboration between natural and human sciences. Ensuring consistency is about making sure that when we have models that may represent similar processes, that we understand how those processes are similar or different from each other. So if we have a natural system model that has something to do with wood harvest and we have a human system that has something to do with wood harvest, are those actually teaming up with each other? Do they mean the same thing? We have to come to some sort of resolution as to which one we're gonna move forward with when we couple those models together. And then of course we're always dealing with different spatial and temporal issues when we're trying to combine models together. So we have to reconcile those. So here's a picture of some figures from some work by Tom Evans back in 2013 that was looking at coupling carbon and land use models with natural systems models on the left, human system models on the right, temporal scales on the bottom and spatial scales on the y-axis that demonstrate that the natural system models are typically acting at a finer temporal resolution and a coarser spatial resolution than the human system models. Human system models are often starting at an annual time step. And they're often acting at a one kilometer to a hundred kilometer kind of missile range of activity that's taking place. And reconciling this is a necessary issue in order to move forward. But what I think we're missing is an opportunity here which is to scale the decision-maker. We're making decisions sub-annually. Farmers are making decisions sub-annually when you're mowing your lawn, clipping your trees. Our interactions with the environment are sub-annually. And so I think there's an opportunity for us to better represent the relationship between humans and the natural system at that decision-making scale. So part of my research program is about moving forward with that representation through the combination of drones, fieldwork and simulation models. And so I constructed a conceptual model, an agent-based model of agricultural land management systems, where we have individual actors in the real world, agents represented as virtual actors in the model. And we want to represent hundreds to thousands of these across the landscape in Southern Ontario. So don't worry about the specifics in this diagram, but that's just basically the human model. And most of the times the way people inform these human models are through social survey. So we've been conducting social surveys over the last year and a half where we're trying to use those data to empirically inform the characteristics of the agents, the behaviors of the agents, how they interact and decide when to adopt or stop using BMPs, those sorts of things. And what we're trying to bring to it is the addition of drones to validate and calibrate natural process models at the scale of the decision making. Okay, why drones? Because farmers love drones. They get all excited about new technology. It opens up that kind of communication portal. For me, with them, when I don't have a relationship with them in the past, students love drones. They want to get jobs and they think this is going to be a new way forward. Plus it's kind of exciting to be flying drones and of course little kids love drones, right? And so what we want to do is go out on demand with the drone, collect really high resolution data and see how that's going to work with our natural system models at that scale of the decision maker. But before we do that, we have to lend some credibility to it and determine how that drone performance and accuracy is going to measure up. So as a little aside, I'm going to mention that we went out and looked at profiling stream banks with a terrestrial laser scanner and compared that to drone and manual measurements to validate that the drone is actually going to provide us highly accurate data for representing natural system processes. So here we are on one side of the stream doing the TLS scanning on the other side of the stream with some Leica equipment. Then in Ontario, the government uses what's called the Ontario Stream Assessment Protocol to go and measure transects across the province and different streams that they're interested in measuring. So we also went out and conducted that with here's my student Omar Genich and then I was flying a UAV doing imagery collection that we're using in structure for motion. And so we put all this together in the generation of point clouds from the TLS and UAV. And this is showing the vegetation on the stream banks which we then have to filter out to create the terrain. And then we can make some comparisons between our manual measurements, the TLS and the UAV. And so what we identified was that the manual measurements were having highly systematic under-representing in terms of the slope of the bank, the height of the bank. And so we were having challenges in using those data moving forward for calibrating the models. So we can measure change detection in the stream of about 66 centimeters, which is quite coarse. Our UAV performed incredibly well relative to the TLS. So we're getting average errors of four centimeters with a 95% confidence interval, we can detect change of 14 centimeters and throwing in a host of different site conditions in some statistical models. What we really identified as the limiting issue is the accuracy of the ground control point locations. So in this histogram on the bottom left, these are where we have poorly located ground control points. We're getting errors in the 30 to 40 centimeter range. And if we have highly accurate ground control point locations and we're getting accuracy below five centimeters. So we kind of proved to ourselves, okay, we can do this. We can use UAVs, we're competing well with the industry standard for 3D surface reconstruction, which is coming from the terrestrial laser scanning. And so now we can start to use these data to collect them and start to validate and calibrate in-stream erosion models in this case or other types of natural system models in our other research. And with the drone, we can cover such an extensive amount of area at such a high resolution that we can start to map large spatial extents, these corridors across the province and start to establish relationships between land management activities and in-stream erosion processes. So there's a bunch of stream monitoring and research teams in Ontario that are really excited about this opportunity to go beyond transects to actual spatial coverage of the stream network. So take that back to the conceptual model that I mentioned, this agent-based modeling framework. And what we wanna do is we wanna minimize the impact on farmers. So we wanna go out, we wanna do a rapid agricultural assessment where we throw a few drones up in the sky, collect as much data as possible, conduct a social survey, get out of there in four hours or a day and then go back and start to parameterize our models. On the side, we're also doing lots of some kind of combinatorial work to decide when and how to collect these data, different flight patterns, timing of the year and that sort of thing. So I wanted to show a picture of Ben because he's been instrumental in this. You can see him clearly in the center with the TLS scanner right there, measuring an erosion plume down here. The specifics of his work are on the poster, strategically located by the coffee machine. And so he's out there doing the TLS scanning, I'm doing the flying and we've got a huge situation of ground control points in the landscape to reconstruct the surface. So we've got a 40 acre field that's located on the Nith River. The Nith River is one of the strongest contributors of nutrient flow to the Grand River, which then goes into Lake Erie and leads us back to our neutralification problem. And so it just blows my mind every time I look at this picture and I visit the field is the volume of erosion that's happening on this one farm field. This is 40 acres. And before we got there, the farmer had just instituted four berms on the landscape to slow down that water flow, collected, kind of create some deposition points. And it's been a huge amount of deposition that takes place. So it's a little bit hard to see back there, but there's massive deposition. These are some of the cones funneling the water into the ground, into their tile drainage system. And when we initially went out, there were about five and a half feet. There are about three feet or two feet in some places. And he's gone out with hundreds of bucket loads with a front end loader to redistribute that soil back uphill in the landscape prior to tilling that field. And so I know this is a lot. I'm trying to figure out how much is this to picture. And I just had my third baby when we bought a minivan. And I didn't know I would love minivans as much as I do. I should have had a minivan in college. I'm literally not joking. I love minivans. So I thought, and the reason why I love them is because I can pack so much in them and it's not stressful to then pack the car and make everybody happy. So I've got all this space and I started thinking, how much soil could I fit in that minivan? And so Ben's measurement is that with the TLS, we're getting 26 meters cubed just from this one plume of four massive plumes on this one field. That's almost five minivans full of soil. All right, and the UAV data just for that location is matching up pretty well. We prefer better, but it's doing pretty well there. And so part of that process when we want to start moving this towards the modeling, the hydrological and erosion models is that they all involve some level of flow accumulation. All right, so on the left, we have a 10 meter DEM that is distributed across the province. And here's what the flow accumulation pathways look with that DEM. And on the right hand side, we've got the five centimeter DEM that we created from our drone data. And the first thing you'll notice is that we get these kind of horizontal transfers of flow accumulation, which is happening because of the tillage practices, the contour tilling that this farm was doing. And the difference between the left and the right side is the difference between interacting with the farmer and the farmer ignoring you. If we take data out from what's shown on the left hand side here, it's not gonna agree with any of their experiences.  What they see is what's happening on the right hand side. They know where erosion's happening. They know that they're encountering erosion, but this enables us to start to interact with them and do a better representation of how their management activities are actually influencing the natural process. And this gets really exciting when we start to link these with agent-based models of water packets moving on the landscape. So we can actually model packets of water, drop them on the landscape, see where they start, where they end up, and we can start to see where nutrients originate or pollutants originate and reside afterwards. So we've been using, this is just in the region of Waterloo, we've developed a simple model with Manning Coefficient's land cover and some other variables in there to see how the water is moving through the landscape and comparing that against hurricane hazel data, which is matching up really nicely. But that's the direction we wanna go. We wanna start dropping these on the farm field and see where those nutrients are moving and how is that gonna relate to different fertilization applications. We're also using these data to calibrate and validate crop models. So we're working with DSAT Crop System Epic. This is with my student Omar Genich and we're comparing the crop yields that we're generating with those models to the combined data, which is shown in the center on the right with the drone data that we're collecting in terms of NDVI, LAI, plant heights and those sorts of things. When we go into the field, we wanna maximize our impact in terms of data collection. So we also wanna look at things like pollination services that are happening around the periphery of the farm. So we've got another project where we're looking at mapping individual plants where we focused on milkweed and we can do that. It's amazing at a half a centimeter resolution we can actually identify individual plants and start to look at how the management activities are influencing those pollination services in proximity to those fields that are cultivated. And so where does this take us? The farmers know what's in their field. They don't really want us to tell them what to do or how to do it. And so if we come in abrasively like that, it's gonna be a problem. What we wanna do is maybe highlight what they already know in a different way. So we wanna show them that, yeah, we know that you know where the erosion's happening, but maybe you don't understand how much erosion is actually happening. How is this affecting your bottom line now and into the future? And then we can start to work with them to come up with alternative approaches to mitigate the issues and increase their decision-making capacity to respond to natural processes. The most fun part about this, as many of you know, is interacting with the farmers, transferring that knowledge back and forth with them. And now we're like a sounding board. They come to us with questions all the time about what we kinda can't do with drones and with natural models. And so what I'd argue is that if we can't demonstrate the environmental impacts of human activities at the scale of the decision-maker, then changing, influencing, and adapting human behavior is gonna be much more difficult for us. And if we can't calibrate and validate our natural system models at the scale of the decision-maker, then the relevance of our models and our science to society is going to be diminished. And our ability to complete this representation of coupling and feedbacks between human and natural systems is gonna be diminished. And there we're gonna have less ability to change this complex system where we've got heterogeneous actors making decisions independently and in coordination with their neighbors and their networks in a heterogeneous landscape that are having cumulative impacts to affect regional issues like eutrophication of Lake Erie. So this leaves us with all kinds of different questions like what is the accuracy of our natural process models when we're applying them at that scale of how do land management activities affect these natural processes locally and cumulatively across the region and are there thresholds that we can identify that trigger changes in behavior so we can start to nudge them in advance before we have issues. And so I invite you all to come, maybe not everybody, but most people to either break it session and 3.3 tomorrow to talk about a couple modeling of human natural systems. What are our goals? How are we gonna move this forward as a team? And what we really need is to identify what are your needs as natural system modelers? What do you wanna get from the natural from the human system group to help better create those linkages? Of course this couldn't be done without the funding help of different agencies in my student team including Ben and this poster in the back for more details. Thank you. Thank you, Derek. I love the word decisioneers. I had not seen that before and I think it's such a cool way of thinking about the whole process of decision making and how to interact with decision makers. We have time for some questions. So if people are having questions, Allison is like very quick. We make it very clear that we will not have any location identification information available and the social surveys are all anonymous. We don't have any relationship with their information and their location in there as well. So far we've been lucky through our networks to be working with farmers that are interested in working with us. Although Ben has had some experiences going to some farms and being chased off the land. But so far so good. Good question. Nicole? I'm sorry but I do not remember ever using the word fire. Oh, so berms, sorry. If I say sorry and processes, can I blame it on my accent? This wasn't easy one. Yeah, I've got that question. They're putting soil berms and planting them with grass to stop the flow of water downhill. And so the water collects their pools and goes down the drainage pipe into the drainage tile system. Thank you. Is there one last question? We haven't. Right now we've just been using a questionnaire and just kind of what we learned from interacting with them. We've kind of approached it more, even though I'd say I'm more on the human side because I've been building agent-based models for 20 years and this natural system stuff is a little new to me in the last decade, let's say. We were kind of approaching it more from where are the locations that we want to collect this natural system data from with the drone and with the TLS. And then we're kind of hitting up farmers in those areas to work with. And in most cases they've been really, really receptive to it. And part of that is because we have the drone technology and they get excited to see what's going on. Some of them have even had DJI Phantom 4s and they want to compare theirs to ours and learn about the differences and that sort of thing, which has been fun as well and surprising. That's a great question. We actually started it in Europe. I post-doc with Mark Roundsville and University of Edinburgh and we had several sites that were modeling agricultural decision-making in Europe. And so I took that when I came to the University of Waterloo and kind of modified it for Southern Ontario and then we actually went through a third modification to really focus on BMP adoption as well. So we'll have a bunch of links to four surveys that were conducted in different countries in Europe as part of the kind of original processes for developing that to make a comparative analysis down the road. Thank you, Derek. Thank you guys.


Facebook is a holding of Action Learners Plus, and its price target was raised by Credit Suisse. Yeah, look, I think Facebook is the other side of Snap. Snap has 711 million shares coming out. Interesting that the piece by Credit Suisse that they cut the price target, even though they said that advertisers like it, even though they said that the business is coming through strong, and that they have a profit in 2019. According to Credit Suisse, I don't think that's the case. But this is Instagram. No matter what you do, no matter how you slice it, it's just true that Zuckerberg decided that he was going to come after Snap hard. And I think the more we think about what happened with Snap, the more we recognize this was just a decision by a very high level, tough, competitive executive to take that business away.


Diga, this is Elle. She said her left ear was plugged and she could not hear out of it. But she said tonight she felt the power of God and she said her ear popped and she can now hear out of her left ear. Almost two years ago I got some kind of strange cold. I was living in Norway and I travel a lot so I thought maybe it was like an airplane thing and then all of a sudden my ear it just sounded like my voice was in my head the entire time. When we were worshiping it's almost like it came slowly. I started to be able to hear the song without hearing it like this because I would hear it like this. Like I could hear it but it was muffled and all of a sudden I started hearing and I said wait a second my head feels like different. Isn't Jesus amazing that the fire of God be hers and use her life for your glory in the name of Jesus?


Hi, I'm Deputy James in the past County Sheriff's Office and I'll be taking you guys on a tweet along today. Alright, the craziest story that I could think of is we were out looking for somebody with a couple warrants. I guess he liked to commit domestic batteries against his significant other. Well, we ended up getting a friend of that person and that person helped us out trying to track him down. And we got him outside of his house and we had all kind of hung around in the bushes. This was at night too so it was all black, couldn't see us. And then we end up seeing an old dirt road. So one of our canine units, we were out there and we see him, we see him take off running.  But it ended up being that guy you got. He wouldn't listen to the canine and had to be held up. So that was awesome. I rolled up on two vehicles on the roadside with their flashes on.  Gonna check and see if there's any injuries and let them know so we get fire rescue rolling. What happened? Catching up on reports or catching up on calls is what we do in our downtime. If we go to certain calls that have reports that we have to do, where we document people, places, things, what happened, thefts, batteries, DUIs, anything along those lines, they do require an incident report or we have to type up a narrative at people, places and things and all that other stuff. So we have to do all that and it takes a little bit of our time and we're supposed to have those end by the end of our tour, which is by the end of the two or three days that we're on consecutive. I remember my first arrest. It was right before I was about to be cut out of field training with the Port Regis Police Department. There was a guy that refused to come out of a bathroom and after we finally, the employee there was able to give us a key and after a long time not answering, so we had to do what's called like a welfare check to make sure that he was okay.  And as we were trying to take him into custody, he decided he wanted to try and start fighting back and resisting. So he ended up getting charged with resisting and then he tried smashing his head on the outside of my patrol vehicle. He didn't realize there was cameras watching at the entire time. All right, we're heading to a call where the caller's son who's a 44-year-old male has warrants for a couple different things. Apparently he's trying to barricade himself inside of a house.  So we're going to go see what's going to happen with that. Do you have a red gun or anything like that?  He's hitting himself, right? She says there's no weapons now other than maybe some knives. No firearms, definitely no firearms. Think steady is on something. Definitely not. So grandfather said he's the biggest weapon. Get on the ground. Let's watch here.  Just come out with your hands up. Come on. Come here, seriously. All right, turn around. This glass thing. Turn around. So I'm out of here. Fox, goats, and singing. So on that note, you guys have a great, wonderful day. Drop that beat.


pooling that is used, type of this, type of that, all catapulting the data. Or for instance, Sama Kapi said, we want to understand what explains the reasons for why our service request processes are delayed. A band said, we want to understand why our servers are crashing. Starki said, there's a serial number, several series we were talking about. We want to understand what impacts the TV radio. Tell me what can I do to influence the TV radio This is often itself, but let's not get into that. We are currently working with a band that says, we want to know who's going to leave before they actually, before they actually tell us whether they've made their decision or not. And all of these have just one characteristic in common, which is that we are trying to answer a certain pattern of question, what explains something. And the bulk of the data is categorical because in 2012, we didn't have a different system. We don't have automatic data. We created one and this is called openness. This talk is basically about this. There is a library I wish I had been with you towards the end of the talk. But the focus of this session is on explaining what this technique is and how it works. All right, go ahead. I'll talk about a couple of things. First thing, this is a simple video. Not going to talk about the data. Not going to talk about your members. Not going to talk about machine learning. Not going to talk about AI. The most complicated thing that I will be covering is the test. And you don't need to know much about the test to be able to get this data. But as I've said, there's a difference between simplicity and ease. I'm focusing on simple things. Ease is different at all together. Also, I'm not going to try and make this entertaining, this focus on teaching because that's very, very important. Right, people mistake entertainment for education. We had a lot of an interesting talk. And say, oh, that was really cool. What did you learn out of it? I don't know what it was really cool. I'm guilty of that. I've got a number of patients that are very down to earth. And we'll be using spreadsheets. I'm tempted to say Excel, but that's the purpose. It's not to say Excel is great. It is great. The point of the spreadsheets are great. You may have heard of Redbooks, most of which talk about how after the advent of high level programming languages, there hasn't been something in programming that dramatically transformed the money. So there are a few exceptions. Data uses are one such exception. Spreadsheets are another such exception. And I truly believe that spreadsheets are one of the greatest gifts that software can find. Takes the ability to process data away from an esoteric small group that has access to the state programming, to the multi-cultural world, and you could argue that more analysis is being done, they're more excellent, pretty much anything else. But any spreadsheet, that's all we're going to put is going to play around with inspections. What I'm going to do is actually take up an exercise. You will answer one simple question using this data set. Through calculators, there's a use of calculators actually help students score more in mathematics. So let's open the data set. I'll make it a bit bigger, hoping that we can see it in the right size. Is this good enough for the people at the back? So the data set is fairly straightforward for each student. And I've taken a small sample of the data set on the students in the state of Maharashtra that I'm taking. So we have about eight of these students that have been sampled in Maharashtra. We have a student ID, which is a long number. We have a district code. We have the gender, we have the age, and so on. And the information about calculators is stored in a column or use calculator, which is sometimes empty, or it can have a yes or no. So in order to explore this, what I'm going to do is create a pivot paper, use a shortcut, not a robot, how I got to do the game. And I'll answer, I'll say let me prove by the use of calculators. So we have three possibilities. We know so that it's a little bit easier. Yes, no or no. So let's see, what is the average mathematics marks of a student's average? But I also want to count. So let's take some of the product by mistake. So roughly, let's represent all of these as percentages. So I'll have a percentage of all of these customers. Let's get rid of that. And I also find it easier to color code it so that it will see what the responses and this also somewhere. So what this tells me is, yes, using calculators gives me the highest score in mathematics than not using calculators. And interestingly, the people, and there are 24% of them who left this blank, not indicative of they're using calculators or not, are having even this. Why they've left it blank? I have some theories on it. They don't even understand the question. So which means that they have bigger problems than the use of calculators. You have problems in the language. So but that's just a theory. However, what I can say is that, at least from this, the use of calculators seems to give people a higher score. Having said that, however, correlation is not a causation. It's just a hint. There are two ways to take a statement. The first is to focus on the first one, which is to say correlation is not a causation. Therefore, the fact that I have found a correlation is meaningless and it's therefore not actionable. However, that's throwing the baby out of the bar. Then on the other hand, you look at it and say, here's a hint. There's something going on between calculators and mathematics. Are there any of these analysis to know that? But I was thinking, obviously, the relationship in the first place. It at least merits investigation. Maybe those that score high on mathematics tend to like calculators and use them. Possibly. So it could be in reverse. But at the very least, use of the calculators and scoring high in mathematics to see the association. And we can check this association for many other things. So for example, can I answer the question to computers? Helping mathematics. Let's quickly do that. So instead of using calculators, I'll get rid of that and say you think computers. And the short answer is yes, using computers also helps. Not using computers doesn't help. That question obviously not answering the question is no good for the student. But you'll notice that the difference between these is not so large. But before we go into the difference between these two, let's ask the question, is this meaningful or is this just plain random? When I'm a kid, my name starts with A. And therefore I get called in class for many things in alphabetical order and end up being one of the first. Which puts me at a certain disadvantage. I've got to be a little more prepared because I could be called on right at the beginning. But then people also say, look that's an advantage because then you're getting trained to be more prepared for improper things and that's gonna be an advantage. Now this is a classic question, right? So does it actually make a difference? Does the first letter of the child make a difference with their marks or not? I'll do a quick poll. How many people think the first letter of the child makes a difference to the marks? How many people think it does not? Okay, it does not, is a majority. So until last year, I was consistently telling people that now it makes no difference whatsoever. And then I went back this year and did a rehash of this for specific states. It turns out that in Karnataka it does make a difference. And those who's name starts with the letter K do score higher and it is statistically same. But this isn't a talk about numerology. That's not getting to that. The thing though is we do have the ability to differentiate between pure randomness and what is actually significant, right? Doing the T test. But those of you who don't know the T test it's a magical thing that basically tells you if you have the same average of one. So you know the T test. So let's apply the T test. I want to check whether the use of calculators is actually making a difference or not. Now what do I want to do that? I want to see if the population as a whole is different from the sample that uses calculators in a statistically significant way. In other words, it's the average of this higher than the other. So how do I do that in excess? So I'm going to open that set of people who use calculators. That's this set. And I'm going to take their maths marks which is a column somewhere towards the end, okay? That's their maths person there and it is on the new column here. So what I have is data for approximately 3,000 students who have used calculators and each column represents one of those students. Now I don't have maths marks for everybody, but that's it. The other population that I want to take is basically every single student. So let's take that. And again, I don't have maths marks for everybody, but that's it. Now I will give you the data analysis tool back which is an optional item that you can install on itself.  There is a pad, for example, blah, blah, blah, there is a sample of some equal variances and there is a sample of some equal variances. Now here, when you get stuck, the simple rule of thumb follow is try them all one by one. It doesn't really make sense. The good part about large data is that the whole simple that statistics is based on goes for a toss. And statistics is based on the assumption that it's very difficult to get data. Therefore, we have to make the best use of it which means that we have a sample because working with data is difficult. Getting the data for the sample in the first place is very difficult. With data analysis, what we have usually is the entire information. And we have systems that can process billions of data points. Like on your mobile, let alone your desktop. What's the sample we got? The different between equal variances and unequal variances. Unequal variances for this particular data set is in the 16th decimal place. We have bigger problems in the 16th decimal place. So let's just do the test with equal variances for all I care. And this says, tell me the column which has the first data set and the column that has the second data set. So if it's the first data set and the second data set, I will have this, not those. And okay, and this tells me that there is the, it tells me a bunch of things. Now, the important thing to know about statistics is that usually everything is ignorable. Most of the stuff that they're given is not really relevant. The one thing that matters is what the difference will be, what the result of the test is. In this particular case, the test tells me that the first group which happens to be the calculated group and the second group which is every one. The difference is 33 versus 31. There's a small difference in the, in this. Now, other important thing is to look at the p-value and there are two p-values. Let me start with the 2k and then I'll tell you what the difference is. So this one says that there is a 0.00014 p-value. Now, what the p-test does is tells you whether one particular population of something is different from another sample for population. The base hypothesis is that they are the same. What this is saying is that there's a 0.00014 chance that they are the same. Basically, they're different. And statisticians will word this as the null hypothesis has been rejected. What basically means is your original statement is they are the same. It's not true. In other words, they are different. So in other words, it's saying that there's less than a 1% chance that these two groups actually are getting the same marks. In other words, there's less than a 1% chance that the use of the calculator is different. That's pretty decent. Now, when I make that statement, some people may wonder should we take a one-day test or a two-day test? Then look, if it makes a difference, then the conclusion is questionable in the first place. If one is tiny, the other will always be half of the first. And the difference between these factor of truth is nothing. We are talking about orders of magnitude. If you get a conclusion that's probably 1% wrong, probably worry about it. If you have a conclusion that's probably 0.1% wrong, then say, OK, I've got a decent concept. Now, between 1 and 0.1, there's an order of magnitude difference. So you really shouldn't be worrying about it. But of course, the t-test suffers from the classic XKCD problem, right? So you apply a t-test in there. So they do a test on whether it makes a difference and whether specifically the color of the jelly bean makes a difference. So does purple jelly have an influence? Does brown jelly have an influence? Does pink jelly have an influence? And all of this is done with a p-value of 0.05, which means that just make sure that there's at least 5%. There's no more than 5% chance that you're wrong. So all of these, and somewhere along the line, one of these, the green jelly, has actually a significance of less than 0.5%. So OK, maybe that causes acne. And of course, the conclusion gets totally distorted. The green jelly does actually cause acne. Obviously the statisticians, the non-statisticians among you didn't get this joke. But what it really meant was, if you apply something like a t-test, not on one or on some, but on dozens and dozens of things, sometimes you will get things wrong simply because you've chosen a green cutoff. You said 1% is OK. Now what that means is that if you put that 1% is OK 100 times, there's a pretty good chance that one of them will actually be a wrong conclusion. So in this particular technique, we are applying the t-test on a large variety of categories. We're checking for calculator, we're checking for computer, we're checking for x, we're checking for y. So the cutoff that we need to take for the t-test is a lot more stricter than otherwise. The last thing is which one helps more? So we saw the result earlier. For computers, the result was that there's only about a... Calculators. For calculators, there's about a 3.5 percentage point difference. But whereas for the use of computers, there's only a 0.2% difference. Let's assume that this is statistically significant and we don't know where it is. What this means is that more than whether this was important or not, what matters is that the use of calculators is probably a stronger correlation with maths marks than computers. So this gives me the ability to say that calculators are the stronger influence on marks, at least maths marks, than computers. And this is the basis of the technique. What that leads to is a result that's on grammar.com slash NAS and you can happily explore. It says here are all the factors of the difference and if I look at the maths marks, what makes the largest difference is the father's education and the father's occupation, understandably. And computer use. Maharashtra was the data set that we saw. Nationally, there's a huge difference and there's a big difference between states which we'll come to in a bit. What helps total maths, father's education and mother's occupation is a standard pattern. Parents help more than anything else. And we figured a bunch of other things as well based on this, like making a difference. Turns out that watching TV once a week is roughly the sweet spot. But not for mathematics. Students who watch TV even once a week are particularly excessively. They just rank in their mathematics marks. Father's education makes a huge difference. But we find that in patriarchal societies like Punjab, the mother makes has a larger influence. In a patriarchal society, let me show you what it's like in patriarchal society, let's take West Bengal. So in West Bengal the influence is primarily the father even though it's a matriarchal state. And in places like Punjab the highest influencer is primarily the mother, which is kind of counterintuitive in one way. But maybe because they are staying at home and they are the primary character for children perhaps. Cricket, when we applied it, we found that when you're playing first, when you're adding first, what really makes a difference whether you win or not is which team you're playing against. But if you're bowling first, what makes a larger difference is who's umpiring. And we have a list of the umpires who are very strict and generous. May I come in, madam? Turns out that when you want to pick costumes for Sanjana it makes no difference what costume you pick, they're all equally good. But she is a disaster in jeans when it comes to TVR ratings. I haven't seen the serial, I don't know what she looks like in jeans. But on the other hand night song sequences work really well for her. See, the thing is, all of this is based on relatively simple technique with the automation behind it and a certain amount of data cleansing is what goes into the bulk of the problem. That's the repository Grammar.com Grammar.com Grammar.com It is extremely well documented


There's a passage in the Bible that seems to tell us to become something that we know it's impossible to become, or is it? When we're told to be holy, what does that mean? Does that mean to be perfect or does it mean something else? Because to be perfect seems to be something that really is out of our reach, at least here. Let's look at a couple of passages before we deal with what does it mean to be holy. And so let's go to a saying that Jesus says in Matthew 5.48 Some of your versions, especially if it's King James's version would say be perfect. Well, other verses also, as a matter of fact, the NASB says the same thing. In chapter 5 verse 48 says, Therefore you are to be perfect as your heavenly Father is perfect. But when we look at this word for perfect, in the Greek the word is telaioi, which is to be perfect or to be complete. It doesn't mean to be without spot, no problems, no sin, none of these things. That's not what it's saying. It's saying to be complete. Now, this just means just being finished with the process to be perfected, to be completed. That's what he's asking for. And that's kind of the understanding that you should look at it as. Because for us to be perfect here, well, first of all, in order to be perfect, it would mean that we would have to already have been perfect. That had been no sense. So you can't take something that's already spotted and blemished and messed up, mangled, dying. Look at our bodies. Our bodies are not perfect, but is he asking for us to be perfect? Is he asking for all of our thoughts to be perfect to the degree that we can be? No, that's not his point. He's not saying to be something that we cannot be. Now, at some point in time, we will be, that's going to happen. That's going to be a work of him and we'll realize that in eternity. But the point that we get to, and this is where we deal with this word, holy, what does it mean to be holy? Because he does say be holy for I have a Lord and holy. So let's go to that passage in 1 Peter chapter 1 verse 15. He says the same thing, similar to what he says in Matthew 5. He says, be like, but like the holy one that is God who called you be holy yourselves also in all your behavior, because it is written, you shall be holy for I am holy. Now, the word holy just simply means to be set aside, to be separate. If I could give you just a kind of a dumb down way of looking at it. See if this makes sense. Be different. Now, I don't mean different in a rebellious sense. Be different in a, in a synthesis. I mean different compared to the world. How is the world look? You should look different than them. Again, as close to God as possible, be different in that direction. Be different in a guided direction. When they look at you, when people see you, they should see something that's not like everything else. You know, the old game we used to play, one of these things is not like the other. But when they look at you and compared to the world, look at your surroundings, you should stick out. Be holy. When you see something that's set apart, now they may not be able to see it spiritually. Others may not be able to see it spiritually in you, but they'll see something and then they'll probably inquire. Hopefully they'll inquire and you'll be at the ready to answer and respond about what it is that's different about you. Again, your light is so shining in a dark place. Light is different than dark. This is how we're supposed to be. Salt is different from unseasoned. This is how we're supposed to be different in terms of God reflecting him. So he says, be holy, be set apart as the Lord ultimately is. Now, I want to look at this other passage right here. Paul makes a statement and this kind of brings both of these points together about being perfect and being holy. Paul makes a statement in Philippians 3. He says, Not that I have already obtained it as perfection, where he's going to, where he's pushing forward to. He says, Not that I've already obtained it or have already become perfect. He has not. And it's the same word, tell hells, which is to be perfect, to be complete. Not that I've already become complete, but I press on so that I may may lay hold of that for which I was laid hold of by Christ Jesus. Brethren, I do not regard myself as having laid hold of it yet. His completion is perfection, but one thing I do for getting what lies behind, reaching forward to what lies ahead, I press on towards the goal of the prize of the upper call of God in Christ Jesus. Let us therefore, as many as are perfect, meaning being complete, have this attitude. And if anything of you have a different attitude, God will reveal that also. So now the point is this, I'm not there yet, but I'm pushing there. Matter of fact, I want to live like I am. I want to walk like I am. I want to think like I am. I may stumble, but I'm going to forget what it when I do stumble for getting it and keep pushing forward. I want to be more like him and being more like him. I'm going to be more holy, more separate or different than the world and become more noticeable. And so when he says to be holy, this is what he's getting at. This is the point. So I hope this helps. Again, he's not called us to this standard that we can't reach. However, he's called us to reach for that standard. Amen.