hierarchical base. So what I kind of want to do here is build on what Shannon talked about yesterday afternoon, which I think really laid the foundation for, in my mind, the idea that ecological data are complex. They have a lot of idiosyncrissities and it's important for us to bring appropriate statistical tools to deal with that complexity of data, often construct models individualistically for specific types of analyses based on the characteristics of those data and their challenges, rather than taking data and trying to, you know, twist them, sometimes inappropriately to fit some long-standing canned test. The classic ones we learned in intrastats were derived, you know, quite a while ago at a time when we couldn't solve problems computationally and so you had to solve things analytically. So you think about like why regression has so many assumptions. So many assumptions because they needed to be able to solve it analytically. If you didn't have to solve it analytically, you wouldn't have made those assumptions and we no longer have to solve it analytically, so we should not feel bound to those assumptions. In terms of hierarchical models, I think they deal with one of the things I was trying to highlight in my lecture yesterday morning, which was, to me, one of the real characteristics of ecological systems is their, their, their heterogeneity and their variability and the fact that we can't, we can often account for that variability even if we can't always explain exactly what's causing for that and it's important to be able to account for that unexplained variability or better way of thinking of it is the as yet unexplained variability because sometimes we can use hierarchical models to characterize variability and then that helps point us to what scales and space and time and organization and phylogeny and whatever that are the most important in driving our system to help us actually understand where there might be additional process understanding. That said, we deal with systems where there is just incredible complexity and we can never measure everything and so hierarchical models are a way of helping us account for the fact that we can't ever measure everything. There's always going to be unexplained variability. So I'm going to start with a very simple case to hopefully give you the idea behind it. So imagine I have data and I'm sampling this data over different observational units. This may be different individuals, different plots, different watersheds, different lakes, streams, islands.  And so imagine I've got data sets coming from why different sampling units and these data sets themselves might just not be individual numbers but whole vectors and matrices of observations, whole tables of data, but they're the data I collected on a particular observational unit. It could be a plot, it could be a year, it could be a species. And when I'm confronted with making the same measurements over different observational units, I'm often confronted with a conundrum of how to analyze that data in terms of how I think about the independence of that data. So one thing I can do is I can take data from a bunch of different plots. I can lump all of that data together and fit one model to that aggregated data. As a simplest case, imagine I've got observations from a bunch of different plots and I'm just going to fit a mean and say, you know, what's the mean abundance of a specific species? Well, I'm going to aggregate the data from all the different plots and calculate an overall mean. At the other extreme, I might say, well, the observations within each plot are not equivalent to each other. You know, things going on in one plot might be slightly different than things going on in another plot and so I might want to calculate independent means for each of these plots separately. So these represent the extremes in what I think of as a continuum from all the observations being completely identical to all the observations being completely independent. And often what we have in reality is something in between where things are different from plot to plot, unit to unit, river to river, but they're not completely independent. You know, what's going on here is not just utterly wildly different from what's going on over there. They're shared information about the process and this is where hierarchical models are handy. So they provide this intermediate case to represent this continuum. So in a simple hierarchical model for, say, calculating a mean, I would have a mean that I'm calculating at each site, which I'm calling here the theta one, theta two, theta three, that's telling me what is the mean of this data, but then I'm also going to fit a hierarchical across site mean. So it's called hierarchical because from the perspective of the parameters in the model, I've added another layer. What's going on here is described by a model at a higher level. This is a fairly simple model because all it's saying is what's going on at this level is I just have an overall mean and I have some site to site variability, which is actually a pretty powerful concept, though a simple one, which is that once I've done this, I've now actually partitioned my variability. So before, the variance here is just the variance over all the whole data sets. Here I have variability within each data set, but I'm fitting them independently. And here I've taken the overall variance and partitioned it into the variability within each of these sites and the variability across sites. So I've taken the overall variability and partitioned it into an across site component versus a within site component. And I said this represents a continuum between these two extremes because that continuum is driven by what you see in the data. If the data tell you that your sites are very similar, then your cross site variance is going to be very small and this model is going to behave more like this one. If the data tells you that there's a lot of variability from site to site, then this across site mean isn't providing much constraint and it's going to behave more like this independent mean. So the actual amount of variability tells you kind of informs to what extent the system behaves on this end to that end and kind of represents that whole continuum in between. From a practical perspective, what we do when we fit hierarchical models is we're not fitting these means, taking them as knowns and then fitting that mean, but what we're actually doing is fitting them both at the same time. So from the perspective of doing this in the context of a Bayesian model, when I fit this model, I have a mean and I have to have a prior on that. And it's just a prior with some parameters I give. Here I have priors on each of these independent means. Here when I'm fitting this mean, this is essentially acting as a prior on what's going on in this particular site, but this itself has unknown so I have to have priors on this thing. So I've pushed up the priors in the model to a higher level. And so it's kind of like from the perspective of each site, you're fitting it giving an informative prior, but you're calculating the informative prior from what you're seeing at all the other sites simultaneously. And that actually leads to something I'll reinforce as I go through this some more, is this idea of borrowing strength. That the information, the inference you make at each of these sites, borrows information from the other sites because the cross-site variability tells us what the overall mean and variability is and that acts then as the prior for what you're seeing site by site. So I'm going to go from the high level concept and dive into some of the math of how you would do this for a simple case. And then from there I'm going to actually dive into kind of the JAGS code of how you might implement these things, but then add, you know, kind of explore some of the complexities as we go along. And then again highlight some of the strengths of thinking about things hierarchically. So what I've kind of done here is I've taken what we looked at graphically earlier and kind of written it down in terms of some probability distribution. So here I'm saying y of k, so k could be any one of those data sets from each of those sites, assuming that it's distributed normally with some site specific mean and some variance. I'm starting now with a model where I'm saying this is data, these two are unknown parameters, and then since these two are unknown parameters, I'm just going to put priors on them. So these are the priors on those. So this is a model where I'm fitting a mean independently to every single data set, but just for to keep things simpler, I've just fit one variance for all of them. So I'm assuming the variability within the sites is similar. I could easily have fit a site specific variance as well. So this is, you know, just a basic emphase model for fitting a mean. Likelihood of the data given a mean and variance priors on the mean and variance. Okay, so here I'm fitting each data set independently, but assuming they each have the same prior. So when I'm writing down this model, these musing tau's, s's 1 and s2 are actual, you know, specific numbers would need to be plugged in there for your choice of prior. I'll just say explicitly here the IG is an inverse gamma. It's not the only possible choice of a prior on a variance. It's one that I use frequently because it's conjugate. Okay, so if we wanted to extend this instead of being independent means with a common variance for hierarchical models instead assume the prior contains unknown model parameters. So if we want to move this to a hierarchical model, we want this not to be a specific number we put in, but we want that to be an unknown that we're fitting based on the data. So if that becomes an unknown that we're fitting based on the data, then we need priors on these two things. What's our prior on the mean and what's our prior on the tau? So that tau instead of being the mean on, you know, the variance on UK becomes our site, the cross-site variance. Hierarchical mean, assuming a common variance, yeah, we're just going to put priors on each of these. So that's an unknown, so it needs a prior. That's an unknown, so it needs a prior. Those of you who've done Bayes a lot know this. Those of you who are relatively new to it, you know, in Bayesian inference everything, everything that's unknown, that is an unknown needs a prior. Anything you're trying to estimate has a prior. So once we move having the cross-site mean and variance being unknowns, we need to put priors on them. I put this slide in orange because it highlights kind of the some of the key take home messages about hierarchical models and why I find them particularly useful. So first of all, what they're allowing us to do is to model the variability in the parameters of a model. In the simple case, we're modeled with two parameters, a mean and a variance. So we're modeling the variability in the mean. You know, any model of any complexity, you know, from a simple mean to a linear model to nonlinear model to some complex process-based models can be fit in a hierarchical context and in that context, we use this additional hierarchical layer to model the variability in the parameters. You know, so in some of the works one of my postdocs system is doing right now, she's fitting process-based threshold carbon cycle models in a hierarchical framework where she's asking what is the site-to-site variability in, you know, these mechanistic models with mechanistic parameters. But the concept is completely identical. You have some model of what's going on at each site and then you have a statistical model above that describes the site-to-site variability in the parameters. And there's lots of reasons to expect that in real ecological systems that the parameters are models are not always identical for every site. But at the same time, there's reason to expect that they're not completely independent of each other. You know, if we think that there are common processes, we won't, we don't think that the variability from one site to another is unbound, that there's some range of variability of what we would expect to find and that nature kind of tells us about what that range of variability is. And then, again, we can kind of try to understand what might be causing that. So it's allowing us to take the overall unexplained variability in a system and partitioned out into multiple terms. So in the simple example, we partitioned, you know, the variability in mean across sites. But we can partition variability in hierarchical models at multiple layers and in multiple ways. So often things that represent your sampling strategies, things that would be different if you repeated the experiment again. So if I were to repeat some study a second time, I would put out different plots. I would make the measurements in different years. I would measure different individuals. So those things are the sorts of things that you would put in a hierarchical model. If I repeated the experiment again, I would, I might use the same experimental treatment levels. So the treatment levels are not random effects. Those are your, your covariates. So things that are, that you think explain the variability mechanistically or you think are tied to hypotheses, those are the things that you would put in in your process model. But the things that are random, things that are variable, things like plots, individuals. Yeah, often things having to do with your sampling schemes. And a lot of work we do, they often point to specific, you know, spatial scales, temporal scales that help us actually partition the variability explicitly. And then again, this idea of borrowing strength across datasets.  But it's often true that you have, for example, unbalanced datasets where, you know, you have more information at these sites and less information at these other sites, and you can actually borrow constraint through the hierarchical level from your data rich parts of your analyses to help better constrain some of your more data poor parts. So in that sense, kind of the, the well constrained parts are, are constraining the hierarchical level. And then that is kind of acting as an informative prior on the poorly constrained parts. So taking to extreme, you would be borrowing strength to a new, to a site that has zero data. That would be the, the limit. You can't get less than zero data. If someone knows how to make negative data, I don't want to hear about it. Actually, that's not true. I've had a few undergraduates generate negative data because they, turns out they broke the data we already had. The idea of negative zero data, so is one strength of hierarchical models is they also let you formally think about a distinguishing, distinguishing between within sample and out of sample inference. So if I am, so if I am trying to predict what I think is going to happen, so this brings us back to forecasting. If I'm trying to predict what's going to happen in this site, I use this model. If I'm trying to predict it, it's happening at this site, I use this model. If I predict this site, I use this model. If I have site number four, site number four, theta four. Theta four has no observations. It's out of sample. I've never made measurements before there. How do I predict that? Well, I have this hierarchical layer that provides an informative prior on what I expect to see here and what the degree of site to site variability is in this system. So I can formally say, you know, I'm using this model with insight inference and I'm essentially using this hierarchical layer to provide us a constraint on the out of sample inference. And because you have to integrate over this site to site variability, you get a very natural result, which is out of sample predictions are more uncertain than in sample predictions, which should happen. That makes perfect sense.  By contrast, when you fit models independently, statisticians mean something very specific when they say independent. They really mean it. If I am fitting models independently, I have site four. From a statistical perspective, the fact that I fit models at these three sites, I have zero basis for making a prediction at site four. I have no idea. If I assume that all these things are just completely independent, that means I'm assuming that if I had a new site, it's independent of everything I've seen before. So I can't, I'm not learning anything that's useful to me to make that prediction at that new site, because I'm assuming that it was independent of the sites I've already seen. This gives me a way of predicting it, but it says that I predict the same thing everywhere, which might not make sense. Even if it does make sense, the hierarchical model would tell you that because it would tell you it's not seeing any site to site variability. Okay. So it has this idea of partitioning variability, of borrowing strength. The details of hierarchical models are usually hidden in the subscripts. This is the thing that I think kills people when they're trying to read hierarchical models and I haven't seen them before, which is usually we just blow over the equations. Let's be honest. You're reading through some stats textbook or some really mathy paper, you're following the words, suddenly there's an equation, you just jump. You read the next words. In hierarchical models, not only are the equations important, but what's important all is hidden in the subscripts.  That really matters. This is, that I'm fitting this for each site and then I have this.  So pay attention to them. And also to remember that from a statistical perspective, hierarchical models are hierarchical with respect to parameters. You're making a model where there's multiple layers of parameters. It's like with linear models. Linear models can have polynomial terms in them. They're linear with respect to the parameters even though statisticians, whenever they classify anything, they're always thinking about the parameters, not the equations, not the data. So you can have hierarchical models on data sets that are not themselves hierarchical. That said, you can also write models that are hierarchical in the data. So a very simple hierarchical model might just be I have an across site mean and that across site mean tells me about the variability from site to site. But there's nothing that says you can't add process at that hierarchical level. There's nothing that says I can't try to put explanatory variables at that hierarchical level. So I have site to site variability. I'm capturing that. I can write a model at that hierarchical level that says here's what I think explains that site to site variability. I can add process. I mean I can add a mechanistic model at the hierarchical level to say this is what explains, but whatever you're writing at the hierarchical level is trying to explain why the parameters in the model, in the lower level models, are different. So you're always writing models about the parameters and why the parameters would change. And in fact, one of the things you'll see discussed at the end of the hands-on activity, though you don't have to implement it, is a not uncommon thing to do. For example, random time effect or random spatial effect is to move from a simple independent random effect to one where there's autocorrelation. So you might have a random year effect, but you might not assume those random year effects might themselves be autocorrelated. So you've built an autocorrelation model at the hierarchical level, or you might build a spatial model at the hierarchical level saying the parameters are varying from site to site, but they're not varying independently. I've also, I think I'll give an example later of where I've put in, you know, put in covariates at the hierarchical level. It's the modeling of the parameters that makes it hierarchical. Okay, so one of the most common special cases of hierarchical models are what are called random effects models. And so if we take what we had before kind of a model that says each site has a mean, there's a cross-site mean and variance, and so I have a within site variance and a cross-site variance and a cross-site mean as the unknown parameters that I'm fitting. I can rewrite this model of the mean at each site, writing it in terms of the global mean across all sites plus alpha k, which is how this particular site differs from the global mean. So I'm just refactoring this term in this way. When I do that, instead of a model for the mean at each site, I have a model at the hierarchical level for how each site differs from the global mean. Now I'm going to assume that the differences from the global mean are unbiased, so these should be zero centered. How every site differs from the zero mean should be centered around the global mean. It's kind of a weird model to write something that, you know, the difference in the global mean are not centered around the mean. Probably wouldn't want to make that assumption. So this is always zero, and again this tau stays as the description of site-to-site variability or plot-to-plot variability or unit-to-unit, whatever. I have the same prior on the within-site variability, and I have the same prior on the global across-site mean and the same prior on the site-to-site. The only real difference is I've re-expressed this in terms of having the global mean at this level and the deviation from it. That's kind of how we rewrite hierarchical models in terms of random effects models. For a simple mean, model of mean that gives you the exact same result. It can be handy though when you write more complex models that have multiple sources of randomness to them. So again highlighting that random effects always have a mean of zero, and the random effects are again partitioning. Random effect variance attributes a portion of the uncertainty of a specific source, so we're partitioning the overall variability into the within-site and the cross-site in this case. The random effects framework lets us, I think, write models more easily than to have multiple sources of uncertainty. So I alluded to this earlier, but I just wanted to come back to it more formally. What things can be random effects? What do we put in that hierarchical level? Random effects are things that would be different if the experiment was replicated. Like I said before, the plots, the blocks, the years, the individuals, the study sites, they would be different if you repeated and we're often using random effect as one way of accounting for the lack of independence between observations within a block or observations within a year observations made on the same individual are not independent of each other. Any of our treatments, covariates, accelerotrol, things we have hypotheses about are our fixed effects and so this gives us, in terms of linear models or generalized linear models, gives us a class of models once you add random effects in. The things that we're always in those models are fixed effects. You add random effects and you end up with what's called a mixed effects model. Okay, so things to watch for when writing down random effects models. They're not magic. They can't estimate variability across plots, units, individuals, lakes, rivers, islands, watersheds, whatever if you don't have replication. They can do things that sometimes seem like magic. Like I've written random effect models with random effects on individual plants where I actually technically am fitting more parameters in the model than there are individuals in the study, which seems like magic, but it only works because I've been following those individuals over time and therefore I have some basis for saying what how is this individual's behavior different than other individuals behavior. If I just measured that individual once, I would have no basis for saying how is this guy different. I'd have no within individual replication. Likewise, if I have a study that has one plot, I can't put a plot random effect on it because my estimate of plot-to-plot variability is, well, it's literally just my prior. I mean, technically you can do it, but what you get back is what you put in. I have an assumption about what my prior understanding of site-to-site variability is. I see one plot. I have not updated my understanding of plot-to-plot variability.  You can fit it with n equals two, you get a little bit of constraint, but not much. The degree to which this can blow up on you has a lot to do with what your priors are. So if you're starting with really uninformed of priors on your, say, site-to-site variability, and then you throw a really small number of sites at it, you will get very little constraint. They can converge, but they're going to be all over the place. I've written down models where it can be very difficult to distinguish the residual noise term from the random effect if you don't have enough replication. That said, I mean, there are a few places, particularly if you're updating on previous work, where you might actually have an informed prior on, say, what your site-to-site or individual to individual watershed-to-watershed variability actually is. So if you do have informative priors, you can get by with much lower replication. But I find it not uncommon for folks to be like, I've got big data sets with lots and lots of rows in them. Why is nothing converging? It's like, well, because those big data sets represent three sites and you're fitting a site effect and you don't have actually much constraint on what your site-to-site variability is. I can set up a data logger to measure data every second at a site, but if I only set up two data loggers, I don't know much about their variability. And an infinite amount of observations at those sites don't tell me about the variability across them. I had this slide yesterday just to reinforce again this idea that the partitioning of things into random effects actually affects the inferences and the predictions we make. So now you can actually think about what we were doing when I showed this slide yesterday. So I was fitting a simple model that just has a random year effect and a random site effect. And so here, when I'm making that prediction to a new year for a known site, I'm doing an in-sample prediction where the site effect is known for a known site, but the year effect is not. So I have to integrate over the uncertainty of the year effect. But in this case, there's not much observed year-to-year variability. So that's where that prediction comes from. By contrast, if I'm making a prediction in year 10 for a new site, I have a known year effect, but I have an unknown site effect. I have to integrate over that site-to-site variability, which happens to be large. And if I'm making a prediction for a new site in a new year, I have to integrate over both of those sources of uncertainty and then likewise over here. So this just kind of highlights, again, some of those points that I made earlier about how hierarchical models affect prediction, because they allow us to make predictions about unobserved site species year-to-year extent of whatever we've made are random effects. When we make the out-of-sample predictions, we're integrating over that uncertainty versus in-sample where we're not. But also pointing out something I didn't say before is that because the hierarchical model provides these informative priors when making out-of-sample predictions, you can often improve your predictive ability at those new sites with a relatively small number of observations because they had this benefit of the hierarchical constraint. And I'll give an example of that in a little bit with some work we did with allometries, where if I have just a few observations on a rare species, I can get some constraint, I can get much more constraint in a hierarchical model because I'm borrowing strength than I can if I was just trying to fit that rare species by itself without saying I'm learning something from say similar species. And this is an example that goes back to my own graduate work where we're working with forest models and this unfortunately ended up low resolution, but here we're, these are time steps in years, so this is a thousand-year simulation, adult tree density, two species that were put into competition with each other. The parameters describing the means were identical between these two simulations. What was different was whether you counted for the random effects on those parameters and actually showing that in this simulation we qualitatively changed the existence criteria depending on whether we just stuck in like every parameter is at its mean or counted for the fact that there was structure to the variability. So here when we don't include the random effects, the variance is the same because we put that that variability gets pushed into the residual, we're including it, but we're not partitioning it. And here we're showing that by, you know, partitioning it we actually, that structure allowed species to coexist that and actually changed which one was dominant. Okay, other general rule of thumb with any statistical modeling, but definitely with hierarchical modeling, start simple, progressively add complexity. That'll be good rule of thumb as you dive into your own projects this week. Write down the absolutely simplest model you can for something and then add a random effect one at a time. Add, you know, some of the stuff Shannon talked about in terms of like dealing with latent variables and errors and variables and observation error. Add these things in a little bit at a time. If you add everything in at once, if you write down the Uber model that you think is going to be the one that like accounts for everything, and it doesn't work, you don't know why. If you add things one at a time and it stops working, you're like, aha, that last thing I added, that was the thing that didn't work. If you had two factors at a time and it doesn't work, you have to go take them out individually and figure out which one caused the problem. So to take a simple example, imagining a bunch of spaghetti plot of biomass through time, but this is structured, the color here represents observations coming from the same observational block. So there's some, you know, say spatial structure there. They're from the same location and then obviously there's time. What I want to do here is to dive into what this would look like in terms of some JAGS code. Some of you use other languages like Stan and stuff like that, but the codes look very similar. We're using JAGS this week. So first model, fitting a global mean. You know, I have a prior in the mean, I have a prior in the variance, I have a likelihood for the data giving normal likelihood with some mean and variance. And here the data happens to be stored in array that loops over time, block, and individual. So I'm just looking up each individual. And this is, if it weren't for the fact that the data was structured in array, this is just fitting a mean, which is about the simplest Bayesian model you can write down. Confidence interval is very tight. Predictive interval is very wide. It doesn't make a particularly interesting prediction because it's fitting a mean. It's the same every year in every individual. Let's try to explain some of that year to year effect. So we're going to write down a model that says the expected value of x at some specific time depends on that global mean and my alpha t, so my random effect for time at a specific time. That then goes into my likelihood. And then because I have a random effect for alpha, I have to write down its model. So I have a loop over all of my years where each alpha t is sampled from a normal again with mean zero and tau t being my year to year variability. Tau t is an unknown so I need to put a prior on it. So I've kind of tried to highlight and read the things that I added in order to add this hierarchical random effect. I added the alpha, I needed to add the estimation of the alpha in a loop and then that year to year variability needed a prior. And so now I get a slightly wider confidence interval because I'm now estimating more parameters but my predictive intervals are showing much more interesting because I'm now kind of capturing some of the year to year variability. I'm not explaining why there's year to year variability but I'm capturing that it exists. I could then take that output for all of those random year effects and then ask kind of an exploratory mode what explains those year effects. That's actually something that I would very often do is take the posterior estimates of a random effect and then I can do you know quickly do a bunch of exploratory analyses. Plot the random year effects against temperature, plot the random year effects against precipitation, plot the random year effects against wind or humidity or you know nutrient deposition or whatever it is that I think is explaining that year to year variability. Get a feel for what is likely to be important and then go back and add those effects into the model without going and refitting a bajillion models. We can likewise write very similarly the model that accounts for a random block effect. So instead of a random effect on time we have a random effect of block. I have to loop over the blocks and I have a variance on the block which needs a prior. It's essentially the exact same code except I'm looping over a different factor just how the data is structured. And then I didn't put the confidence intervals on this because it makes it messy but we can see that the posterior don't vary from year to year because I didn't put a year effect in but I am seeing that there are consistent differences. You know the black and the cyan tends to be high, the blue and the green tend to be low. If you're colorblind you have no idea what's going on anymore. And then if you wanted to put a random effect on both we just have that the expected value of x depends on both time and block with an overall mean, a random effect on block and a random effect on time. So this is actually where I think a good example of why refactoring hierarchical models in terms of random effects is actually handy because it's easy to then write down a model like this where the different random effects are just combined additively. If you move on to more complex models you can likewise put random effects on parameters. So if I have a say a linear regression and I just have an additive random effect on the end that essentially is a random effect on the intercept. But I could likewise write a model where I put a random effect on the slope to say the slope might be different from site to site not just the intercept. And so I would write down a model that says you know beta 2 is you know beta 2 mean plus beta 2 random effect for whatever is causing that random effect and then I just plug that into the regression. Cool. I don't actually plot this because it would end up really noisy. I just actually plot what I show here just a overall summary table of the models that were fit. Every model has a mean. Every model has this residual uncertainty. We can see as we added random effects we actually started explaining more and more of that unexplained residual uncertainty. So overall variability is here but we know we've partitioned that out and explained the random effects that don't have process to them but they do still explain over two-thirds of the variability. We can see our DIC score which is our model selection criteria does support including those random effects. The other thing that we do see which is a kind of a classic thing that goes on in model selection when we increase the number of parameters the uncertainty in our parameters do increase. So when we're only fitting the mean we're very confident about the mean. When we're fitting this model we're considerably less confident about the mean because we're fitting the mean and then we're also fitting a variance but then we're fitting the cross site variance the temporal variance the cross block variance and then we're fitting 10 year effects and five block effects. So this is where you can get hierarchical models that suddenly seem like they have huge numbers of parameters but an important thing to remember about those alphas is they're not independent. So the total number of so this is one of the things that's challenging about hierarchical models you know what is the number of parameters in the model. Well it's not the number of alphas because those alphas are not independent so the effective sample size of parameters is often much smaller and you know it may be it's ill defined just by the structure of the model because it really depends on what the estimates of those random effective variances actually are. So the random effect variances are very tight the effective number of parameters is much smaller if the random effect variances are very large you're effectively are fitting an alpha for every single site because you're effectively fitting every site independently. So it's something that emerges dataset the number makes kind of makes things a little unintuitive but that the idea that the effectively the number of parameters in the model depend on what data it's fit to. Okay I showed this earlier but I wanted to come back to it again you know just showing going beyond fitting a mean we can fit models that have both fixed and random effects kind of went through this earlier some high lever points this idea that we're using random effects to try to explain the unexplained variability you can use this to point to things that we need to explain and then we can add covariates to try to explain this so this is exactly what we were talking about earlier this idea starting with random effects figuring out the scales that need additional explanation trying to put process understanding on the things that aren't understood to try to explain the unexplained variability but noting that sometimes those additional fixed effects are not justified so that's where model selection is helpful you know they might explain it but it might not be parsimonious so a simple year effect so consider some number of new younger produced per adult female in a population of birds then let's say that we fit a year effect and it shows that there is coherent year to year variability through the whole population in that reproductive output and based on that you could use those year effects to look at the additional covariates without necessarily and like I said you can do the exploratory analysis on that without having to go back and refit a bajillion models if you have simple data you can go ahead and fit a bajillion models but you if you have complex if you have large data in complex models you might not you know things that where it takes are weak for the MCMC to run you don't want to try a bajillion candidate models you want to have some idea of which ones are likely and then you can refine the model to add these additional covariates so some take home messages this is just as important to account for the variability as it is to account for the covariates and this is again something I tried to emphasize yesterday yesterday when we're making when we're doing forecasts how we characterize the uncertainties how we partition them into different sources you know the driver uncertainty the initial condition uncertainty the parameter uncertainty the random effects understanding the variability can be just as important to our ability to make predictions is what the mean model is predicting and then we use these random effects to account for these unmeasured often unmeasurable covariates these things we can't yet explain you know the slides like I said are all in lines you can come back to them so this is an example from my own dissertation work where we're trying to fit relationships between tree diameter in tree height and one of the take homes is this idea of borrowing strength so here are common species I have measurements of diameter and height across the full range and lots of things and so the black line is the Bayesian estimate the red line is just a linear regression that line is the maximum tree size tree height from the literature and they're the same you know I have lots of data the hierarchical model gives me the same observation the same estimate as though I just fit these things independently because the data at that scale dominates here I have trees species that are predominantly just in the canopy I didn't there's not recruitment for these species and so some of my you know linear regression fits say you know yeah they're right at their maximum height and if the diameter is smaller they would have been taller because that's that's the maximum likely to fit by contrast here is what we're getting we're getting the idea that because we see consistent across species variability in the relationship between size in height that it says well no trees have to start small and get big this is the pattern by which trees tend to start small and get big and I make sure it goes through the data so I have an intercept that's consistent with the data I have a slope that's largely coming from the hierarchical model and likewise over here we have rare species that tend to be more in the understory where you know sometimes I have no relationship to height sometimes I'm you know soup growing super fast the hierarchical model gives much more biologically plausible because I am borrowing the strength from the common species to constrain the rare species or species that aren't necessarily always rare but you know not observed over a range of variability that lets me constrain the parameters fully and then these other examples just go on to say that you can also apply random effects to nonlinear models so here's a simple beaverton hole density dependence model recruits are a nonlinear function of spawners this alpha parameter and this r parameter were treated as random effects treated hierarchically so they were fit with an overall mean on each of those and then variability on each of those and so you can get you can fit them hierarchically and I think the hierarchical fits are the dashed lines and the site to site fits are the solid lines and the site to site site level fits sometimes do these somewhat unsensible things and then here's another example of a nonlinear model is coming out of one of my grad students dissertations are fitting the the farquhar von kammer berry photosynthesis model which is nonlinear with a change point transition between these two parts and here we decided that we wanted to explain the variability in two of the parameters vc max which controls the maximum photosynthetic capacity and this alpha the light use of the quantum yield which can kind of controls photosynthesis at low lights and we actually wrote down models for those things in these nonlinear process based models that were a function of fixed effects and random effects we actually had a random leaf effect a fixed effect for month a fixed effect for leaf nitrogen and then here we had a fixed effect for leaf chlorophyll and leaf specifically area so we ended up with a model that's got a lot going on quickly how it behaved predictive observed before and then after we counted for the leaf belief variability


Yeah, and everybody forced here ripping 2020 only baseball jumbo to box number two random teams check out the results anytime late sports cards Com there's a link in chat as well. There was that and then there was like the The walk-off Because it was like because it's a walk-off because it was when they beat the Bulls I think whenever whenever Wendell put that put that a tip it in it's a walk-off Dude Not gonna lie The magic social media accounts are always on point it is the best social media in sports too good Like a lot of them are really like Like the Red Sox are boring and like the Yankees are boring like they don't I don't know There's just not a lot going on there, but the magic are always just doing whatever them and the Chargers The Chargers are good Just tortured franchises, I guess have the better better accounts Chris Bryant light blue to 499 cubs Victor Victor Mesa Got so nice the name them twice Light blue paper Isaac Peretti is the 499 Tigers Yeah, but I mean that's how they do it Varland and Jarenda ran there. It's how they do it Every picture ever taken they really do they have every meme ever made as well They make half the memes that are already there your team just got beat by whatever whatever it is some dumb meme Royce Lewis It's a Volpe paper Yankees and we that's why that's why we love it. We're That's why we love it Like if you're on Instagram or you're on or and or Twitter you need to follow even if you're on a fan Like basketball you need to follow the Orlando magic Play that song play that song Dominguez paper Yankees Isos pump drop nine to change my mind go Michigan State change my mind I'm gonna go whoever's in the lead. That's why that's why I think it's gonna win Iso first boom in your R. N. Carnation auto it's a 299 on the speckle for the Marlins Your R. N. Carnation That sounds awesome dude, it's just straight to personal That's so good. There's a whit paper For the Royals They run Laura He was really hot prospect at one time Still got a ton of upside he was like six six or something like that is now fielding like I feel like If you are like a long-standing franchise that has won a lot and like a big city Like if you're like Boston, New York or Chicago like You can't be cool because all the old-timer fans will be like They won't get it you know what I mean Like the like the Bruins account is very just like the Bruins account is just like well look at us We are the Bruins. We're kind of funny. Hey There's never never any meme no song no meme We just did it. It's all Jeremy Pena Like I'm sorry. We have Brad Marchand on our team. We're not we're not putting out ridiculous content. Come on now There are accounts that do but not the official ones obviously there's a Gold shimmer auto to 50 Micah bellow for the Brewers Micah bellow Niko Horner Micah and Niko it definitely sounds like some mobster brothers From he low No, so you're like Vaughn That makes sense atomic hidden finds to 150 Danny Jansen blue jays Dude Vaughn Vaughn the Tampa Bay Lightning when he when they got swept by by the blue jackets, dude That was ridiculous dude, that was such a bad move by there like Social media account they got destroyed on that one People only forget about it kind of because they won the next year the next two years But man was oh Man that thing got got roasted like just post the score and move on Is that bad? Cabello is a Volpe Chrome They are like one of those cups and they were a great team The the reason it was a meme is because they were a Powerhouse that season and they lost to a team that barely made in the playoffs. They got swept It was it was just everything combined to make a perfect storm. Yeah, usually NHL socials is the lamest one a Tony de Braille first woman auto for the Mets Dibrol or de Braille? They saw them Mets Yeah, I was over Mars. Yeah, Kyle's here So the size or Jeremy Pena Chrome Blue shimmer now is a Gunner Henderson and 150 for the Orioles Nice color there for you. Oh Sam is also live on drip. He's got personals all night over there Personals on drip. No personals on this stream tonight Children are for you The ball the Mexicali Purple first woman auto to reach scuba solid one there for the Tigers 63 of 250 we go Detroit Cardenas That's true to Vaughan. Yeah, the Sun's last year Kyle Lewis Maya Huff Cabello. So Huff is first in this Cabello the Sun's in for guy Yeah, Sun's in for yeah, yeah, yeah, that was Guy gets in a fight in the stands. We're like, yep, this is our guy Sun's in for a guy. Oh, that was yours or Mars. I think it's sweet The sheriff has a sweet card That's by my favorite Manga yet That's one of the cooler ones. I've seen a few more on Instagram on like eBay and they're all pretty darn awesome. Yeah the There's a who is it one of the Jets rookies I think and it looks like he's Like Falcon he has like he has like a jet pack on with like like wings is pretty sick Like blue paper. Oh, you're almost a 499 Giants I think the manny is still the coolest one of his it's it's like board. It's like a borderline downtown got Volpe on the chrome The burrow one is the burrow one is might might be the coolest one isn't he's in the jungle Burrow is my trout like George George of the jungle Joe of the jungle There in Kenya Brendan Fraser legend Academy award-winning Brendan Fraser actually yeah for George of the jungle in great movie. Yeah, I think that was like a makeup Oh, sorry. I'm gonna give it to you here. It's like a makeup The burrow one is disgusting I don't even I can't picture anybody honestly, there's a pain your chrome I Literally can't picture anybody refractor first bowman will produce the deal for the meds to 499 Let's look at the cast The first one that shows up after him and the I've read the love interesting to get her name Decent casting though especially for the time Look at his face is punchable Bobby Whit paper Herb among the school. Yeah with I with the lightning. There's a Dominguez chrome Yeah, I think that was the first one. I put wait. I don't know if that was the first one. No, the first one I pull was chase. I also pulled a Herbert though Either way, they're all sick. I think all of them but Brady are better than last year purple paper Nolan Gorman It's 250 for the Cardinals Kino Kino Let's get a lead basketball filled up everybody First woman auto Yankees Ezekiel Doran Yankees weekend of Zeke Cowboys trade a brand of cooks I know Zeke in the cowboy anymore Traded a fifth and a Third or something. I don't know It's good trade for the cowboys They need someone other than CD land that can make a play in the passing game Durans definitely a solid name because never the Yankees Still gonna be a good break for you Hey guys, it's going to lead. Let's get it filled up Atomic coming up rookie auto Jordan Yamamoto to 100 for the Marlins. Yeah, I actually bought a couple of cards last year franchise I'm still just sitting on a couple of our hands. They got traded. It's happy with it That's an aqua Jordan Groschans to 125 for the Blue Jays. She'd be coming up pretty soon Groschans Mota did Go back to Japan I keep missing one And I'll do it Hey as well they paid just about as well as the MLB does competitions Pretty darn good, too That's why they're gonna go to the WBC final I would think Autos of Yamamoto for the Marlins Atomic there Duran for the Yankees first woman base purple tree scuba tigers at 250 base Tony Debrale or Debrale or Debrale something like that for the Mets gold shimmer auto Mike and bellowed a 50 for the Brewers and Speckle first woman auto you are in Karnassi on Marlins at 299. That'll do it. Thanks again everybody more baseball coming up


I have visions of being a rapper, an actor and all these different things and I just feel the excitement from that and move towards it. So if an opportunity makes me feel the same, take steps towards it and that's how I see the universe and that's how I operate. Being that energy, trusting in that unknown, not trying to find the exact path by my mind. Dancing in this unknown of taking steps that feel right and also open to the infinite possibilities that can happen in every moment. Hi, my name is Steven Selly, I'm 30 years old and I'm here to enjoy. I love life and I want to share it with you. When I was 18, I thought to myself, what if I can dunk? And I thought that my whole life as a child and for some reason I said I'm going to go for it. Life changing decision, I got my first dunk in about a year, could watch all those videos on my YouTube channel and it sent me on a journey of transformation. Through that journey, it taught me what limiting beliefs are and it completely changed my perspective of what's possible in this reality. I achieved something that was just a dream. It wasn't 1% possible, it was zero and the spark of that journey was just for the fun of it, just for that possibility of that what if feeling. So that one step turned into a mountain that I never even saw coming. I was the shortest out of all my friends, so jumping was my way of overcoming this way I felt inferior and expressing who I was. My college degree was in information technology, I got a corporate job and the mindset I developed and the person I became through the dunk journey allowed me to see that I was holding myself back in that job as well. So the second I secured that job I was safe and I thought to myself okay this is definitely not the job I want but it allowed me the safety to say what do I want and I thought to myself well I'm already making videos, I like doing that, I see vloggers blowing up heavily inspired by Casey Neistat so I thought to myself okay I'm going to turn the camera on me, I love talking, I love sharing, I'm already doing it, maybe I'll blow up on YouTube as a vlogger and live my dream. So I did grow my channels but it didn't pan out because I wasn't that great at monetizing that channel but I learned how much I love expressing myself on the camera, sharing my story and connecting with others. Three years of doing that I honed my skills freelancing photo, video, editing, podcast and realized others may need this skill and this courageous leap came for me to do it full-time and I quit my corporate job. I never looked back, life has been a roller coaster since then, I had no entrepreneurial experience back then, I didn't have any background in learning photo, video, professionally, everything I've ever done is self-taught. I'm very proud of that but in the past it was feeding into my imposter syndrome of who am I to charge this much as a photographer, everyone else is better than me and now after working through that I realize I can be proud of that and I want to inspire other creatives who don't see themselves as artists to say you know what what I love to do is my art I don't have to do it this way I can do it my way and as long as I'm doing it out of the love out of the passion it's going to make it the most valuable it can be so three to four years of doing it myself is when I met some business that I started collaborating with in business now I see entrepreneurship as a spiritual awakening it's a way for me to become a leader to step into a role that is about something bigger than me so it's not just creating content all about me or for myself it's about creating content that helps others and helping others create content and together our business organization and collaborating with other clients we have a collective energy that's bigger than just one of us the whole is greater than the sum of just its parts so now the things I know for sure right now is following the excitement I moved to California in 2022 originally from Florida grew up in Florida born and raised in Boca Raton now living in California I found my community it was another leap of following that fun and my life's been expanding ever since I'm the founder of a creative agency called courageous growth I do all the creative content we help brands grow we focus on people that we align with they're taking courageous steps in this world to build a product to build a brand that's changing the way we do things in the healthiest most aligned that's great for the people that consume it and great for the planet so why do I love sharing I feel so excited by life that I want to share it I like to equate it to a good food you taste something like oh you have to try this you want someone to share it and that's why I make art because I have these feelings and art to me is about trying to express something about how you feel so others can feel it too so the ways I do that right now is photography videography music and rapping I love dancing I love physically moving my body and I love helping others do the same so I love helping people tap into what's unique to them their unique energy because to me finding that flow within of when I feel like I'm completely pulled by a force greater than myself and I'm in this flow and this magic unfolds and these experiences that just blow my mind I want others to experience that as well so if you're new here welcome I love sharing my journey as I go through it and I love creating art so I hope you laugh I hope you feel something I hope you feel inspired to find your own energy and express it thank you for watching thank you for all your support thank you for being you the best thing we can do the best way we can express is being the most authentic to ourselves we have this unique life and if we can tap into everything that's blocking our unique light we can be the best vibration we can tap into that highest vibration just have the most fun be ourselves and feel that freedom of expression


What we'll do is I'm gonna take you through just a full blown tutorial of everything that you'll need Ever like there's only a few there's only a few things that you'll ever need this right here is obviously one of them So let's what what what we do is we will create a folder at the top so at the top of the bookmarks Create a folder like can you go to the very very top where you have all those bookmarks and just go to the very right and click No down down in the empty space. Yeah, right click right now to the left See if you can right click right there to the right Right? Yeah, perfect.  Okay as manager. Let's name this one ads manager Perfect safe. Okay, cool. So this is gonna be the first one. We're gonna bookmark this page Let's call this one the dashboard. This is dashboard and and some of this stuff. Maybe maybe you know You already know but but there's gonna be a lot that you that you haven't saved yet So let's name this save this one. This is dashboard And this put that in the as manager folder No, put it in the as manager folder not the bookmarks bar. So put in the as manager folder Perfect and what rename it rename it rename it. You got to name it rename it.     That's not the right name.  So hit that blue star at the top and And remove Okay, do it again. So hit the blue hit the star and let's rename it So this was dashboard.   So that's the dashboard now click on the dots to the left As to the left the dots to the very left the square those dots to the very very left right there down Down right there down. No down down right there Click on that and then we're gonna go to we're gonna go to billing you always got to know the billing as well So know what card is on file know what date you're being billed. That's super important You don't want to just wake up to a surprise charge from Facebook. So click on payment settings And just bookmark this page as well, but pay payment settings click on payment settings to the right Well, yeah payment settings and know your billing date know what card you have on file Know the billing date. There's the American Express. There's our billing date bookmark this page name it billing. This is billing Okay, perfect. So go ahead and hit done and then Perfect. So now what we'll do is hit the dots again to the left and then now let's go to let's go to business settings Let's make sure everything is set up correctly in the settings And all we're gonna do is we're gonna double check a few tabs. So Go to go to pages Click on pages and just bookmark this because as you start to request access to all your clients You're gonna want to be able to get to this part right away click on Actually, what did you name it? Unstar it unstar it and do it again. It's name is from pages Perfect, and then and then click on add accounts Bookmark that one to add accounts down to the left Right there down add accounts perfect Okay, cool. So this one's gonna be add accounts Okay, perfect. So we're good there. We're good there. And then if you scroll down, do you do you have an Instagram account? No, okay. Okay. So we don't need to worry about that. Okay, perfect So then click on the dots at the top the square and then let's go to so we got business settings We got the ads manager. Let's go to let's go to audiences and then we'll go to events manager But let's go to actually know what go to go to add account settings at the very top add account settings click on that one Because this is very very important You want to you want to know this this information right here. This is most important if you ever You can all you're gonna always reach out to me Like if you ever need anything you can always reach out to me whether it's cartridge or whether it's a Facebook ad Whatever like it's only these initial calls that we got to get through and then after that I'll be here no matter what But sometimes if I can't answer a question sometimes some random shit happens sometimes with Facebook and you'll have to reach out to them Like sometimes like like my last client her shit got hacked and I was trying to figure out the damn thing But then I had to refer I had to say hey man, you have to reach out to Facebook in this case So if you ever have to reach out to Facebook those two numbers are the most important your ad account ID number the 306 and the business ads manager Account number which is the one one three one So bookmark this page So if you ever reach out to Facebook if you ever email Facebook, they're gonna want to know the ad account ID number and Or they're gonna want to know the business ID number and Those are the two numbers Business manager business manager. Yeah business manager ID. So account ID and business manager ID Yeah Perfect. So hit done. So those two numbers are the most important Also, what we want to do is go back go back to the business settings What I want you to do is I want you to verify your email because that's gonna be very very important so go back to business settings and Then and then to the left you'll scroll down and you'll go to business info Business info at the bottom perfect click on that one and then You have all that right there. Okay, cool. Okay, so scroll down. Did you already verify? I think I Scroll down. Yeah. Yeah, you did you did you did is that email right there? Perfect. Okay, so you're good You're good with this one. Okay. Good. Okay. So now now let's click on the dots again and let's go back We were at more is there anything that I need to do with pixel here We're gonna get there. We're gonna get there for sure Let's click on so we're to get the ad accounts. We got the number perfect. So now let's click on audiences I'm gonna have you bookmark this over here Real quick Okay, so bookmark this this is custom audiences You always want to be able to get to this part right away. So custom audiences Perfect, and then hit done. Okay, cool. So this is your custom audiences We'll start working on these custom audiences as well for sure This is where and in just just to let you know like When it comes to Facebook ads, this is what most people don't don't even care to even talk about like the most as far as someone That's trying to teach someone something When it comes to Facebook ads a lot of people think you can just run an ad and in a shitload of leads are gonna come in It doesn't always happen that way at all at all Where where 99% of your leads are gonna come in from is when you're Retargeting your custom audiences and a custom audience will be people who either watch your videos or People that land on your pages So if I'm running an ad and they click learn more and they go to my landing page I'm running an ad for an insurance agent and they're trying to capture leads. We're trying to capture leads They see the ad they click on the ad on Facebook. They go to the landing page, but they don't fill out the form That's a custom audience That's what you're gonna be doing a lot of that's why cartridge. So if I run if I run let's say for five agents And I would have one assuming five different customer audience. Yeah, but they're not it They're not it all right here at all at all. They're gonna be in their own ad accounts Okay, okay. All right. Okay. So this is specific. This is only for Royal Media Marketing ad account Everyone's gonna have their own ad account. Everyone's gonna have their own audiences Okay, okay, so when it comes to yours, this is for your cartridge page only So so because that one pixel has to go on your page Everyone has their own pixel. Everyone has their own ad account So so but what I'm saying is the custom audiences you'll be creating custom audiences around those who are landing on your landing page That's a website visitor and then you'll create other custom audiences of people who land on the thank you page Those are people who go to your landing page fill out the form and then go to the thank you page Right like they that's a lead. So whoever lands on the thank you page Like let me just share let me just share my screen. Stop share come to my page real quick So here's an example This right here. Let's say let's say I'm a real estate agent and I'm running an ad for a house that I have for sale They see the Facebook ad they click on learn more they come to this one page My Facebook pixel is on the page So people who land here is a custom audience people that land here and don't fill out the form So they come here, but they don't put in their name and email. That's a custom audience. That's a custom audience Another custom audience are people that come here They like what they see They put their name and email send me the details. They then come to the thank you page This is the thank you page. My facebook pixel is also on this page There's only two pages the landing page the thank you page. Both pages are custom audiences So so the landing page custom audience web page views visitors only they never fill out the form Second custom audience are people that land here that custom audience are leads But if they're your leads, what do you do with them if they already become your leads? Now they're now they're in a custom audience. So here's why this is so important Because as I go run my next facebook ad I'm going to exclude this audience from seeing it You were breaking up. I didn't hear you as the reason why these need to be set up as custom audiences Is because those who land on the on the on the landing page, but they don't fill it out That's a custom audience Those are that's an audience that i'm going to always retarget in the future So every ad from this point on my second ad my third ad You can ever say so you can exclude once already Land here Yes, yes. Yes. Yes. So so when you fill out the form and you actually Click the yellow button and you land over here. You filled it out. You are a lead Now I have everyone coming to this page in a custom audience and yes every ad from that point on I'm going to exclude this audience I'm set Make sense Yep So so so what i'm saying though what i'm saying is that that's where all the magic is at the magic Is in your custom audiences go to share your screen The magic is when you can start creating these custom audiences the the the two biggest audiences The reason why custom audiences are so important because this is where 99 of your roi is going to come from Is when you're creating custom audiences and retargeting everyone who lands on the landing page, but never filled it out And when you're creating custom audiences of everyone who's watching your videos So if your clients are going to start running Facebook video ads Let's say they do a video talking about life insurance and it's a two minute video You're going to create a custom audience of everyone who watches at least one minute of it And on that video ad you can always Yes, you can like a call like the call to action battle like To learn more or whatever. Yeah, there's learn more on every ad every ad but what i'm saying is that the audience So if someone watches a facebook video ad It's two minutes and they watch one minute of it That's an audience. Yes. Yes, that's who I would set up right here. So I would click on the blue button I would click on create audience And we won't do that yet We'll get to that on our next call But but I wanted you to bookmark this page just just so you have it right away But yes What i'm no leave it there leave it there don't click don't don't do nothing But what i'm saying is that this is where your ROI is at So it's not it's not going to be on the front end just running one ad You're going to start capturing leads when these clients of yours can run ads every week When you can run an ad every week and that's what they're paying you for So they're not paying you to run one ad in your case They're they're bringing you on as a monthly partner to run ads for them every month Which I which what I would do is I would have them run a run an ad every week So a new ad is running every week four times a month And so what you're going to be doing is you're going to be creating audiences of everything creating audiences of everyone who's watching their video And creating audiences of everyone who lands on their on their on their landing page, but they they never fill out the form Okay, I understand. So how many basically like in average how many ads you would run For a for appliance one every week. I would What's that? Yeah, you said one every week, but is that like uh Which means like every every second week do I turn one off and then I start new ad with the same budget? Yes Yes, you can you can you can you can turn it all depends on how it's doing So we're we're going to get into the into the full blown setup of it on on on our fourth call But it just depends on how well that ad is doing if that ad is not doing as well as it should be And it's already been seven days turn it off and run another one. So in the in the mindset of the client I'm I'm just always telling them to do a new video every week. That's all they need to know Just do a new video if if they're doing video some of them are not going to want to do video But but those are the only type of clients that I take on I only take on clients that do video I don't do I don't do any other client Now you can do whatever you want, but I'm just telling you right now If if a person is not willing to do video, I wouldn't take them on as a client Because especially in life insurance in life. What are you going to do running out on an image an image of what?  Right like and that's why with real estate agent What you're going to do an image. Okay, you're doing an image of a house I've seen an ad of a house three million times So what separates you from the other two million real estate agents?  Okay great. Yeah, that's fine But what if it's not a house and you're just running an ad just to run an ad to capture leads But you're running an image ad on a house when it's not even your house for sale But you're just trying to capture a lead that makes no sense to me.  Mr. Client What are we going to run an image of what your life insurance? What can you show? What are you going to show me a A picture of two people in a suit shaking hands like what do you what is there to show me? That's why video is so important man. Take on clients that are willing to do video Of themselves. Yeah, you know, so so to answer your question I just get them in the habit And I mentally prepare them for a new video every week. So if it's every friday, okay, great Have a new video every friday upload it to your business page. That's what I have them do. That's all I have them do Your only job, mr. Client is to upload a video every friday or monday or tuesday. Whatever pick your day Let's say it's every friday your only job, mr. Client. I'm going to take care of everything else Your only job is to upload a new video on your facebook business page every friday by 6 a.m That's our plan. That would be basically like uh Educational video nothing nothing educational video with a call to action at the end So so what you want to do with these clients is you want to And you know this because you're in the industry you want to be able to give away something for free Yes, yes, absolutely. Yeah, so that pdf a free download a free, you know information package on life insurance That's what you want to be given away Exactly. That's what I do Yeah, three informations for perfect. That's the call to action So so all you're going to do is you're going to tell them. Hey, do your two minute video Talking about a tip or talking about education something educating them on life insurance something We don't know or five tips or whatever and at the end of the video The call to action is to is to say hey and by the way you guys Click learn more to download your free pdf in this pdf We talk about the best terms of life insurance you can possibly have for you and your family click learn more It'll take you straight to our page where you can download your free copy. See you guys soon So all I tell the client is your job is to make a video every week Friday at 6 a.m. Upload it to the page and at the end of the video make sure you say that To click learn more to download your free copy That is it So on your end, sometimes you'll need that video for the second week Sometimes you won't need to run it right away on the second week because the first week you're capturing leads and that video is doing very well Yeah, I understand. Oh, okay. Okay. Great. Okay. Perfect. Yeah, but but the mindset of the client I don't want them thinking that I don't want them thinking. Okay. Let's see how this ad goes And then maybe maybe I'll do another video. No, dude. No, I need you to do a video every week Okay, I understand perfectly. Yeah, you know, I mean Yes, so that's the video ad will take them to the landing page which will I mean they will leave their info So that landing page will be From cut cut right? Yes. Yes. Yes. Okay. So we're gonna work on that next time. I guess Yes, for sure for sure. So right here. I just want I want you to bookmark this. Let's go to another one Hit the um hit the hit the dots to the left And then let's click on events manager Events manager is where we get our pixel. It's where we also Set up our custom conversion So right here I'm familiar with this. Oh, yeah. Yeah, I'll I'll I'll help you with all this stuff.  So go ahead and go ahead and um Yeah, so your pixel your pixel hasn't been Hasn't been set up yet, which is which is fine. So what we'll do is we will go to Click on is is is at the bottom of the page. Can you go any lower than that? Click on upload no down down upload events Okay, so you got to accept. So go ahead and accept this Yeah, okay. Okay. That's what we got to do first. So go ahead and accept this Okay, perfect. So now let's let's get out of here x out of this And let's just yeah, um x out of that just x out. Yeah cancel. Yeah x Just go to um back to uploads or is that taking us back here? Yeah, we got to get out of this page Okay, perfect. So try refreshing this real quick. Here refresh Garage cell collector and that's fine. So Why don't you rename it rename it royal media marketing? So hit that edit garage cell collector hit that edit No, the pencil to the in the top up right there Let's rename it Royal media marketing Perfect. Okay. So this is your pixel. So go ahead and click on save Actually, you don't have to put pixel just put royal media marketing Perfect hit save. Okay. This is your pixel. So you go to continue pixel setup Now we're not going to be able to do it Actually, you have you have the cartridge one, but we're going to do this on another call We won't do it right here But you'll install code manually and then you'll get the code and then you'll paste it into the cartridge page Which I'll show you how to do but just x out of this one. Let's bookmark this right here x out of this Bookmark this and it's going to be pixel Perfect. Perfect. Perfect. Perfect. Okay. So pixel Hit done and then we have one more so click on the blue triangle to the left And you're going to click on custom conversions And we're not going to be able to do it yet until we until we add the pixel onto the cartridge pages But bookmark this just so we have this one So this one's going to be custom conversions And without this part you're not going to capture leads. So that's why this right here is the most important But we have to we have to add the pixel to the pages first and we'll do that on the next call. So let's Look at that folder Look at the folder. Click on the folder as manager. Let's see what we got in there to the right No to the right that in the bookmarks bar So let's look at everything we got No, no, no, no the folder click on the folder Adds manager Right there. So we got the dashboard the billing the pages the ad accounts the The ad account business manager id the custom audience the pixel and the custom conversion That right there my friend is everything you're going to need. That's it. Like there ain't nothing else There ain't nothing else Now one thing you do want to set up is the business settings. Let's we didn't save the business settings for some reason Actually, it's under pages. You have it under pages Let's go to let's yeah, click on pages And I'm going to have you bookmark one last one Which is which is right here, but I'm going to I'm going to name it Click on click on users click on users right through the left users Click on people and let's just bookmark it from here as well Let's bookmark it from here and let's call this on business settings. It's right there. So just yeah business settings Perfect And that's it And then on this on this one down David can't tarot just click that drop down and just delete that one Or you can hit the trash can So so when I'm done with someone's account, that's what I'll do if it's just if I just request access to the business page I just do that and I just get rid of the page because I don't want to be tied to their business page anymore If I'm not working with them anymore So that's how I do it or I could tell them to go back to their page roles and click remove So the same way that I clicked on respond to request to give you access now. I can go back. In fact, let me just show you So so go ahead and stop sharing So now if we're no longer working together I can do it that way on my end And I can also tell you to go back to your business page click on settings to the left Can you see my screen? Okay, and then I go to page roles And then all I do is I come down and I can click on remove So right here where it says agencies. We should see it. Yes, right there and I just click remove So now Now I wouldn't be tied to that page anymore All right, perfect. So let me just do quick a recap Let me go to your screen Yep Okay All right. So basically, uh, I do everything from here even when I want to access to other ads managers Yes, that's a comment. So whatever we did here with uh ads manager folder Billing patient. Do I need to do that on each? With each client their business manager No, no, you know good question very very good question Once you're once you're in their ad account Because I'm the one who's going to be setting up everything for them even business managers because most of my let's say first 10 20 of my clients I know that in fact They they have only personal page. So from personal their personal page. I will be setting up Creating the the business manager and uh ads account ad account. Yes, and all that So when I'm doing that, I don't need to be worrying about the pages ads account the custom audience pixel Or do I No, you don't know because you're not going to bookmark it for them on their computer, right? So But once you're logged in to their account So if you're logged in to their ad account and now you're in their ad account You can just go to your folder And click on any of those and it's going to take you straight to their Custom audience or it's going to take you straight to their pixel So those are those are universal these bookmarks As long as you're in their their account, you're going to be able to access all that information by just clicking on your folder Oh, I see. Okay, great. Perfect. So yeah, so that right there bookmarking everything makes it super easy Because that's all you're going to need. You're not going to need nothing else Or so basically I don't need I mean, let's say with with my first few clients I don't need access to Page I mean business page. I would need the access to ads manager Because the ad account to add account. Yes. Yes. Yes. All right. So once I set up business manager and add account on their page I just need Access from mine to theirs and then I start running the ad. Let's say let's say let's say you Let's say you just created a new business a new ad account or let's say you created Let's say you let's say let's say you just created a new business page for me I'm your client. I only have my personal facebook page But you just created a new business page so I can start marketing my business. You created a business page for me right What you're going to do now is you're going to now Go into my into my business page because you have access to it And you're going to set up a new ad account Because I don't have an ad account yet Right. I don't have an ad account yet question question quick question So once you have your personal page And if you want to have a business page do you Uh, do you have to have a business manager? Or no, or you can just create from your personal page business page You could just create from your yeah, so these clients you're not gonna they're not going to need a business manager You need a business manager because you're managing other people's pages So you so they don't need business manager. No, they just need ad manager. That's all they need you That's why you don't need to set it That's why there's may look a little different. You're not gonna have a full-blown business suite. You have a full-blown business manager But but but they're not they're gonna just have an ad manager. Let's go back. Okay, so let's go back All right, right So you'll just come like you are my new client You are the insurance agent and you have just your personal page david material personal page nothing else Mm-hmm. Mm-hmm, and I have whatever I have here So what do I do for you?  It's just not gonna have every you're not gonna have Some of the features but it looks just like this.  That's that's a question as well.  Now I have better picture.    Yeah, and now I I know what one needs to be done great sounds good my friend sounds good. So that that right there is is our