First Semester Final Exam

We couldn't possibly cover all of the first semester's content in two days of review, so let's point out a few topics that no one has asked about:

Correlation coefficient r and coefficient of determination r-squared. What special insight does the value of r-squared give you about the relationship between x and y?

Why can't we just take the square root of r-squared to get the value of r?

Slope of a LSRL = the estimated increase (or decrease) in the response variable for every unit increase in the explanatory variable.

The easiest way to get it is r*sy/sx where sy is the sample standard deviation of y and sx is the sample standard deviation of x.

While we're talking about sample standard deviations. . . the formula is SQRT(variance of the variable), so the sample standard deviation of x would be

SQRT[(sum of the squares of (Xi - Xbar) for all values of X)/(n-1)]. N is the sample size.

Don't panic if you can't read that--just look up the formula in the text.



What does it mean to be resistant to outliers? Give examples of measures which are resistant. Give examples of some which are not.

What are the benefits of different types of graphs (box and whisker, stem and leaf, histogram)?

How do you know if a set of data is approximately normally distributed? Look it up.

Why do we block?

Why do we experiment?

What makes an experiment special?

What are the characteristics of a well-designed experiment?

Why do people sometimes need double-blind experiments?

What is the placebo effect?

How do you know if two characteristics are independent?

Things to think about when you should be studying

The icon used for the command SAVE in Microsoft's Office applications is a 3.5" diskette. Now that diskettes are nearly obsolete, when will they change the icon and what will they change it to?

Chapter 8 Binomial and Geometric Probabilities

What are the differences between **having two kids and counting x=the number of girls** and **having kids until you get a girl**? What is the random variable x in the second case? What are the means [expected values] of the random variable x for each of these scenarios? What is the standard deviation of x in the first case? How could you simulate each of these scenarios?

How are these distributions similar? How are they different?