Sections Covered in Textbook
NOTE: This is not a list of all topics covered in this course, but is a list of all topics covered in both lecture and the book.  Of course, sometimes the book goes into much more detail than we did, and sometimes the lecture goes into more detail than the book. When in doubt, trust the lecture.

As of May 7, 1999:

3.1-3.4 (skipping 3.3.2, 3.3.4, 3.4.3)  Types of Studies/ collecting data
4.3.4  Graphical techniques (histogram, stem and leaf, and later, box plots)
4.1, 4.3 Summarizing data measures of center and spread
2.9.1 Standardized variables (although this is more applicable than this section suggests)
4.4.2 Summarizing linear relationships -- correlation, simple linear regression
2.1 Basic Probability
2.1.2  Equally likely outcomes, some counting rules, combining events
2.2.1 Conditional Probability
2.2.2 Independence
2.2.3 Bayes' Theorem
2.3 Random Variables
2.3.1 Discrete Random Variables
2.4.1 Expected value for discrete RV's
2.4.2 SD and Variance for discrete RV's
2.7.1 Bernoulli Distribution
2.7.2 Binomial distribution
2.3.2 Continuous RV's
2.4.1 Expected Values
2.4.2 SDs and Variance
2.8 Some Continuous RV's:
    2.8.3 Uniform
    2.9 Normal Distribution
More 2.9
5.1.1 Central Limit Theorem
5.1.2 Normal approximation to the binomial
2.6: Law of Large Numbers
6.1 Point Estimation
6.2 Confidence Intervals for mu when sigma known
6.3 Hypothesis Tests
(skip 6.3.6)
7.1 Hypothesis tesets for mu when sigma known
7.2 Confidence interval for mu when sigma is unknown
5.3 The t-distribution
7.2.2 hypothesis test for mu when sigma is unknown (skip power calculation and everything that follows, although reading this might help you understand other things a little more.)
8 .  Comparing two samples
8.1 independent samples vs. matched pairs
8.2 grpahical emthods
8.3.1, p. 254-256.comparing means of two indpt. samples when variances are equal but unknown