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!
In the chapters that follow, you'll be introduced to several different formulas for hypothesis tests. Each of these formulas is designed for a different set of conditions (number of populations being studied, type of data collected, focus of the study, etc). This video explains when to use the z-test population of μ.
The CASE 2 z-test of proportion can check to see if the proportion of individuals who fit in a specific category is larger by a defined amount in one population than it is in another population, or if it's smaller by a defined amount in one population than it is in another population, or if it's different by a defined amount in one population than it is in another population.
Regression is all about making predictions. When you make a prediction, you can expect to make mistakes (errors). The Standard Error of the Estimate measures how big of an error we can typically expect. I go through the calculation and the interpretation and introduce the Sum of Squares Error (SSE) along the way.
Here is a sample of the 250 multiple choice questions you'll find in the Final Exam Bundle. I've made this selection of questions to reflect the most common, and the most challenging multiple choice that you'll face on the final.
There are a few reasons someone would determine the value of a p-value:
- The p-value is an alternate method for deciding whether or not to reject Ho (as opposed to using a rejection region)
- The p-value tells you the exact probability of selecting a sample as rare as the one you have - given that the Ho is true (the rejection region does not do this)
- It will get you the mark you need when asked to 'calculate the p-value' :)
Test your understanding of Chapter 13: Inference About Comparing two Populations . Each question is accompanied by a mini-video lecture showing you how I decided which solution was the correct one.