Date of RAT  Topics covered 
What to focus on 
Mon., Jan. 20 
Section 2.7 (pp. 4045) in textbook 
 Know what is meant by a bellshaped curve and know that a normal curve is
a special case
 Understand the concept of standard deviation
 Know how to calculate the standard deviation by hand for a simple example
 Know the empirical rule and how to apply it to simple examples like
Example 2.11
 Know how to compute a standardized score or zscore from an observed
value if you also know the mean and standard deviation

Mon., Jan. 27 
Section 9.2 (pp. 261265) in textbook 
Note that a binomial experiment is a situation in which
we take a random sample of size n from a population consisting
of yes's and no's.
It is assumed that the proportion of yes's in the population equals p.
An example would be flipping a coin n times (in which case "heads" might
be considered "yes") or asking a random sample of n people whether they
are in favor of some law.
 Know the difference between the true (population) proportion, p, and
the sample proportion, phat. One is a parameter, the other a statistic.
 Know how to compute phat from sample data.
 Know what the normal curve approximation rule says.
 Know the conditions that must be met for the approximation rule to
apply.
 Understand the scenarios given in the book
for which the approximation rule applies.
 Know how to compute the standard error of phat from sample data
(the standard error of a statistic
is an estimate of the standard deviation of its sampling distribution).

Mon., Feb. 3 
Introduction and first two sections of Chapter 11 (pp. 313317)
in textbook 
 Know the five steps in any hypothesis test
 Know what a null hypothesis is (box on p. 314)
 Know what an alternative hypothesis is (box on p. 315)
 For examples such as 11.1 and 11.2, know the difference
between a onesided hypothesis and a twosided hypothesis test
 Understand the "innocent until proven guilty" logic
of a hypothesis test
(assume the null is true, then compute whether that assumption makes
the observed data very unlikely to have occurred)

Mon., Feb. 10 
Section 11.6 (pp. 334335) and all of Chapter 3
(pp. 5374) 
 11.6: Understand the difference between practical significance and
statistical significance
 3.1: Know the fundamental rule for using data for inference
 3.1: Know the definition of simple random sample
 3.2: Know the difference between an observational study and
a randomized experiment
 3.2: Know what a confounding variable is and what effect it can have
 3.3: Understand that causeandeffect relationships may be
established by randomized experiments but not observational studies
 3.3: Know the terms control group, placebo, singleblind, and
doubleblind

Mon., Feb. 17 
Section 15.2 (pp. 468473) and Section 12.1 (pp. 347350) 
 15.2: Read and understand (but don't memorize) the shortcut formula for
the chisquare statistic for a 2x2 table
 15.2: Understand when a chisquare test on a 2x2 table is
equivalent to a Ztest for a difference in two proportions
 15.2: Understand what Fisher's exact test is used for
 12.1: Know how to recognize which of the four situations
on pp. 348349 a research question falls into
 12.1: Know which parameters (and sample estimates) are associated
with each of the four research situations
 12.1: Understand how paired data may be viewed as a special
case of one mean

Mon., Feb. 24 
Sections 4.1 through 4.3 (pp. 8198) in textbook 
 Understand the fact regarding sample size in the last paragraph of p. 81
 Understand the three types of bias named on p. 84
 Know what stratified random sampling is (p. 87)
 Know what cluster sampling is (p. 88)
 Know what systematic sampling is (p. 89)
 Understand the five problems listed at the top of p. 93
 Read and understand all of the examples

Mon., Mar. 3 
Chapter 13 (pp. 385415) 
 Know the general formula for a standardized statistic
near the middle of page 386
 Know the five steps in any hypothesis test
 Know how to formulate hypotheses correctly for each of the
testing situations in Sections 13.2, 13.3, 13.4
 Know which testing situations require Zstatistics
as the standardized statistics and which
situations require Tstatistics instead
 Know the necessary conditions for each test; these are the
same as in Chapter 12 and may be found on pages 388, 397, and
405
 Understand how the standard error for the difference of two
proportions is different in Section 13.4 than in Section 12.6
 Understand the relationship between twosided
tests and confidence intervals in 13.5 (don't worry about the relationship
between onesided tests and confidence intervals)
 Know what the multipletesting phenomenon in Section 13.6 refers to
 Know the definition of power and know what things
can affect the power of a test

Mon., Mar. 17 
First Monday after spring break 
No RAT this week 
Mon., Mar. 24 
Sections 14.1 through 14.3 (pp. 425438) in textbook 
You should know the assumptions about the population deviations
from the regression line; in general, be well familiar with the brown box
on page 431.
In addition,
you should be able to do all of the following, given
full or partial regression output from Minitab:
 Find and interpret rsquared
 Calculate yhat for a given value of x
 Calculate a tstatistic from the coef and SE coef
 Locate and interpret the pvalue for the slope
 Interpret the sign of
the slope (same as the sign of r)
 Locate and interpret the standard deviation

Mon., Mar. 31 
Sections 14.4 through 14.6, 16.3
(pp. 439447 and pp. 503507) 
 In sections 14.4 and 14.5, understand the difference
between a prediction interval for y and a confidence interval for E(Y).
(Do not memorize any formulas.)
 Know all of the conditions for linear regression on
pp. 443444.
 Know what a residual plot is and what it should look like
if the regression conditions hold.
 Know which condition a histogram or normal probability plot
of the residuals would check.
 Know that nonparametric tests like the KruskalWallis test or
Mood's median test may be used in place of ANOVA when the conditions
for ANOVA are not satisfied.
 Know which hypotheses are being tested by a KruskalWallis test or
Mood's median test.

Mon., Apr. 7 
Sections 7.2 through 7.3 (pp. 176186) 
 Understand the relative frequency interpretation of probability
 Understand what the book means by the personal probability interpretation
 For all of the examples in Section 7.3, understand what is meant by
the sample space, a simple event, and an event.
 For all of the examples, understand what complementary events and mutually
exclusive events are. Note that complementary events are always mutually exclusive,
but not necessarily the other way around. Also note that disjoint means the same thing
as mutually exclusive.
 Study examples 7.7 and 7.8 and understand what independent/dependent events are in that context
 Understand what conditional probabilities mean in the context of example 7.8

Mon., Apr. 14 
All of Chapter 1 (pp. 110) 
Read and understand each of the seven case studies along with
the accompanying "morals of the stories".
Make sure you can answer all of the exercises at the end of
the chapter that don't ask you to use data in any way.
(For instance, you can safely ignore exercises like 1.7, though
I'm hoping you could do this using Minitab if you needed to!)

Mon., Apr. 21
Last RAT of the semester! 
Sections 7.4 (pp. 186192), 7.7 (pp. 202208), and 8.5
(pp. 233235). 
 In Section 7.4, know all of the basic rules for finding probabilities
(these are summarized on p. 193):
 Rules 1 and 2 can be easily derived using Venn diagrams (try it!).
 Rule 4 can also be derived using Venn diagrams if you recall that
conditioning on an event means that event becomes the new sample space.
 Rule 3 should be memorized; in fact, rule 3 is often taken to be the
definition of independence.
 Understand all examples in section 7.4
 Know the difference between sampling with replacement and
sampling without replacement
 Know how to correctly answer questions such as the one posed by Eddy
on p. 202
 Know what sensitivity and specificity of a test refer to
 Don't worry much about the stuff on coincidences and the gambler's
fallacy in Section 7.7
 Know what a density function for a continuous random variable is
 Know what the density function for a uniform random variable looks like
 Know how to find the probability that a given uniform random variable
lies in a given interval (as in the continuation of Example 8.13)
