When comparing two population means, you need to first run an F-test to determine whether or not the population variances can be considered equal. If the results of that F-test of variances leads you to reject the null hypothesis, then you can assume the population variances differ. This t-test is the follow-up step where you can then compare the two population means (unequal variances assumed).
There are three ways to use regression to make predictions of the dependent variable (y). Here I discuss all three: point estimates of y, prediction intervals of y, and confidence intervals of the AVERAGE y.
This chapter is VERY heavy on the math, but I can show you some quick shortcuts to save you time on the exam! As well, ANOVA may be a small section in your textbook, but there is a large amount of theory there that you will be tested on.
Studies done by testing the same group of subjects twice (before and after some event / treatment) can produce more useful results than comparing the responses of two independent groups. When repeatedly measureing the same group is not possible, then matching pairs of individuals across the independent groups can also increase the odds that a test can find significant results.
We are investigating whether or not x can help us to predict y. Maybe it does - but does that give us the whole picture? Perhaps OTHER factors also influence the value of y. The coefficient of determination tells us just how much the value of the dependent variable (y) can be determined by the independent variable that we're studying (x).
This chapter can be easy to learn. The data tested is nominal which means that you'll deal only with frequencies, and just like ANOVA there are no estimation formulas. You will need however, to pay close attention to the small differences between the different tests taught here - it's what you'll be tested on during the exam.
The steps of a hypothesis test - for ANY hypothesis test - are always the same. This includes not only the z-test introduced in this chapter, but also all other types of hypothesis tests introduced in the chapters to come.
The CASE 1 z-test of proportion can check to see if the proportion of individuals who fit in a specific category is larger in one population than it is in another population, or if it's smaller in one population than it is in another population, or if it's different in one population than it is in another population.
So far, everything we know about the relationship between x and y is based on the sample - and not a direct observation of the population that we're trying to comment on. How do we know that we can trust the sample to accurately represent the population. The t-test of the slope addresses this issue.
Chapter 16 contains a lot of new information, and is focused on heavily in the upcoming final. Understanding this chapter is crucial for a good mark!