This week we’ll get some more practice using multiple regression by completing the promotional display case study on Monday. On Wednesday we’ll start talking about models purely or primarily for prediction.

Final project information

This week we’ll get some more practice using multiple regression by completing the gas prices case study and another case study about the effect of promotional displays on sales.

On Monday this week we will get some practice working with interaction terms in multiple regression models. On Wednesday we will practice hypothesis testing in regression models.

On Monday this week we will review the “Hold my beer” case study and get some more practice with multiple regression for adjustment by working through the “Beauty in the Classroom” case study On Wednesday we will explore how and when to use interaction terms in regression models by revisiting the “Beauty in the Classroom” case study, and (time permitting) in other examples as well.

On Monday this week we will cover the material about randomized experiments and matching that we didn’t get to last week. On Wednesday we will start talking about computing adjusted or partial effects of variables that account for potential confounding relationships by using multiple regression.

On Monday this week we will talk about doing hypothesis tests when you don’t know the sampling distribution of the test statistic under the null hypothesis by using permutation tests.
We will focus on tests evaluating null hypothesis of no difference between groups. On Wednesday we will start talking about methods for isolating causal effects – that is, if we observe a difference between two groups (say a treated group and an untreated group) is that difference caused by their membership in those groups, or simply associated with it?

On Monday this week we will talk about the bootstrap approximation to sampling distributions, confidence intervals, and making group comparisons with regression models.

On Monday this week we will review the Market Models case study from last time, and begin a new demand modeling case study. On Wednesday we’ll discuss the demand modeling case study and review the concept of sampling distributions.

A note for our future selves: Week 5 and Monday of this week were lost to the weather. I hope things are returning to normal for you all!

On Monday this week we’ll finish up with the normal distribution by talking about standardization, which will lead into our discussion of anomaly detection, hypothesis testing, and p-values.

On Monday this week we will talk about Bayes rule, or how we should update our beliefs based on new information. On Wednesday we will review expected value, variance, and standard deviation of random variables, as well as the normal distribution.

On Monday this week we will discuss the meaning of conditional probabilities and how they differ from joint probabilities. We’ll also get some practice computing joint, marginal, and conditional probabilities from tables. On Wednesday we will discuss the law (or rule) of total probability and Simpsons paradox.

Welcome to STA 371H! Today we will review the syllabus and course structure, and get our first introduciton to R and RStudio. We won’t cover everything on the syllabus in detail, so please make sure you read it over this week. It’s posted to Canvas and linked under the About and Resources sections of this webpage.