I explain here how multiple linear regression differs from what we learned in the previous chapter. It's easy stuff, but it's also important to make sure you understand exactly how the variables in this chapter differ from those in simple linear regression (there are many independent variables instead of just one!)
Just like with the test of slope, interpreting the coefficient of an indicator variable requires new wording to get it right. Remember - indicator variables are not simply nominal variables, they are BINARY nominal variables.
Most exam questions from this chapter will include a computer output of the solution, but very often there will be some values missing. I'll show you here how to pay "Fill in the blanks" multiple-regression style. This is probably the most common exam question from this chapter.
Here we complete the test of the hypotheses created in part (d) using a 5% significance level. These tests will tell us whether or not there is a difference between the category represented by an indicator variable and the 'missing' category discussed in part (a)
In a multiple regression model we have more than one independent variable, and so there is more than one slope. Should we test each one individually? Watch this lesson where I explain why the F-test needs to be introduced to replace multiple t-tests.
Test your understanding of Chapter 18: Model Building. Each question is accompanied by a mini-video lecture showing you how I decided which solution was the correct one.
The sample regression equation is determined here using the output provided in the question.
In part (b) the ask us is the model valid? …and why? To answer this, we make use of the ANOVA table that’s provided in output. This is the F-test of validity. I go through all of the steps required for full marks in a hypothesis test including writing the hypotheses, looking up the rejection region on the F-tables, and using the test statistics to decide whether or not to reject the null.