Your textbook can be confusing when it tries to explain the Laws of Expected Value. It can be easier to understand what's happening if you think about them as the Laws of THE NEW Expected Value. I explain in more detail in this video.
Learn the difference between SAMPLING ERROR and NON-SAMPLING ERROR, and how they are introduced to the samples that researchers collect.
Knowing how to use the binomial tables is a MUST for the test! They can save you critical time compared with using the binomial formula. The tables however are not intuitive and so you'll first need to understand what kind of question the tables are designed to answer. Once that's clear, you can rework most binomial problems so that the table can solve them quickly.
Once again, I extend the tabular approach that I used to find the expected value (previous video) so be able to find the variance of x. The standard deviation of x is simply the square root of the variance (just like in Chapter 4)
A comparison of the three types of samples covered in this chapter:
- Simple Random Samples
- Stratified Samples
- Cluster Samples
This is a very typical chapter 8 normal distribution question. I take you perform a complete walk-through on how to identify the correct method, and then perform the solution quickly
Random samples should act in a random (unpredictable) way... right? WRONG! As samples grow in size, the probability of randomly selecting samples with specific means becomes easier and easier to predict. I show how this is the logical result of the Central Limit Theorem using an easy to follow example.
Now that we have the probability distribution, finding the probabilities of specific outcomes couldn't be any easier. If you can add, you can do this!
Why work with samples when what we really want is the population data?
Just two simple keywords in this question identify it as a chapter 9 problem. I'll show you how to look for these kinds of questions on your test, and how to solve them