 In this example, we're gonna consider, we have a sales person here, Ila, where she has a base salary, so she's paid by some base salary, but she's also placed, she's paid based upon a commission for selling insurance policies, right? So that's what Ila's gonna do here. So if her weekly income, we'll call it I, I for income, and if the number of new policies she sells is in, so in for number of policies, her income will be determined by the number of policies, new policies, that she sells each, what's the timeframe, week, right? So she wants to sell these insurance policies. So what we know is the following, say last week, Ila, she sold three new policies and she earned $760 for that week. So that's how much money she made. This actually gives us a data point, right? So when N was three, the I was 760. So we're thinking in the following way, right? N is our X-coordinate now and I is our Y-coordinate. The week before that, she sold five new policies and she earned $920. So we have a different data point. When N was five, the income was 920. Now assume, and that's the only thing that determines her income here, a flat salary that she always gets and then the number of commissions, right? This is gonna be a linear model for her salary, because the commission is just gonna be directly proportional to the number of policies she sells. And so her income, I of N here, it's gonna look exactly like some, it's gonna look like some flat fee plus the some commission fee, which I don't exactly know what that is right now. We're gonna call this just MN plus B. So B, this flat fee right here, this flat salary, that's just her base salary, the white intercept. If she sold nothing, how much would she make? And then the M here, the slope of the line is gonna give us, it's gonna tell us how much money Isla makes per policy she sells. And so you'll notice here that because we have these two data points, it's basically, we have to fit a line, we have to fit a line to two points. In which case, I would find the slope using the slope formula. So the slope here, M, we're gonna take 920 minus 760 divided by five minus three, 920 take away 760, that should be 160 and then five take away three is a two. And so this tells us that her commission is gonna be $80 per policy that she sells. All right, so she gets 80 bucks for every policy she sells. And then in terms of the white intercept, we could solve the white intercept or we could just use the point slope for the approach I'm gonna take. We're gonna take I minus, pick your favorite point here. I'm gonna take, let's do this one right here. It doesn't really matter which one you do. You're gonna take Y minus 760 and this is gonna equal 80 times N minus three. That's what we're gonna get right there. And so putting this into slope intercept form will distribute the 80. This is gonna give us 80 N minus 240. And then we want to add the 760 to both sides, add 760. And so then we get that the slope intercept form would look like Y equals 80 N. And then I guess it's a plus here. You're gonna get 760 take away 240, which is 560. So this right here tells us this is the function model for Isla's commission. She makes $80 per policy she sells. She also has a base salary of $560. So if we record our findings in the following way right here we see the following. So Isla earns, she earns $80 per policy sold and has a base salary of $560 per week. That's how her paycheck is gonna be determined. And we got this from interpreting our linear model here. The whiner set, the slope had significant ramifications on the answer right here. And we found both the slope and the whiner set by using the slope form, by using point slope form. Our way of creating the line, the equation of line given points on the line is exactly how we found this equation for Isla's income.