 Hello and welcome to screencast about models. So today we're going to talk about three new types of models and the first one is going to be exponential So if a data set is going to be modeled by an exponential function, then the points obviously need to have some kind of an exponential shape to them So if it's growth, it's going to you know start off kind of slow and then eventually swing way up Or if it's decay, it's eventually or it's going to kind of start off decaying slowly and then Really quickly oopsie. Sorry. So this one is growth and this one is decay Okay, so if you are using the Texas Instruments TI-83, TI-84, whatever You're going to want to use the expre G which on our calculator is option number zero when you go over to the regression option and then it's going to give you an equation of the form a Times B to the X Okay, next type up is our logarithmic and Again, if you have a data set that models this it needs to kind of look like a log function So a log function then kind of grows quickly and then kind of starts to settle down a little bit okay, so again if you're using the Texas Instruments calculator, you're going to want to use the option of LN which stands for natural log technically REG and That is option number nine and That's going to give you an equation of the form y equals a plus b LN of X Okay, then the third form is going to be logistic So logistic basically combines those two functions together So it kind of starts to grow quickly like an exponential or it starts off slow Then grows quickly and then it kind of settles down like a log Okay, so logistic like I said is kind of those two previous models pushed together okay, so if you're using the Calculator it actually just says logistic and this is option number B And then the form of this this one's kind of crazy. So you get y equals c over one plus a e To the negative bx Okay, so this one definitely takes a lot more work if you have to do any sort of Answering of questions for it or that kind of thing, but otherwise it's just like we've done before with previous problems Where you just you know will either plug in some values for x or maybe you have to solve for a or solve for something else So anyway keep practicing these and continue on with your models Here are credits, so thank you for watching