5-1 Inferences About the Difference b/w 2 Population Means for Independent Samples: σ1 and σ2 Known, p. 395
define, and use in context, the following key terms: independent samples versus dependent samples; sampling distribution of the difference between 2 sample means,
Independent vs dependent samples
use the critical value approach to perform a hypothesis test about the difference b/2 2 population means, m1−m2, based on independent samples, whose population standard deviations, σ1 and σ2, are both known.
Hypothesis testing about mu1-mu2
Mean, standard deviation, and sampling distribution of
Internal estimation of mu1-mu2
omit the information about the p-value approach. You are responsible for only the critical value approach.
5-2 Inferences About the Difference b/w 2 Population Means for Independent Samples: σ1 and σ2 Unknown but Equal, p. 403
use the critical value approach to perform a hypothesis test about the difference b/w 2 population means, m1−m2, based on independent samples, with population standard deviations, σ1 and σ2, unknown but equal.
Hypothesis testing about
Interval estimation of mu1-mu2
In Section 10.2.2, omit the information about the p-value approach. You are responsible for only the critical value approach.
5-3 Inferences About the Difference b/w 2 Population Means for Paired Samples, p. 416
define, and use in context, the term "paired samples" (or "matched samples").
use the critical value approach to perform hypothesis tests about the difference between 2 population means based on paired samples.
Hypothesis testing about mu_d
Inferences about the mean of paired samples (dependent samples)
Inferences about the difference b/w 2 population means for independent samples: sigma1 and sigma2 unknown and unequal
Omit Section 10.3 entirely.
Interval estimation of mu_d
In Section 10.4.2, omit the information about the p-value approach. You are responsible for only the critical value approach.
5-4 Inferences About the Difference b/w 2 Population Proportions for Large and Independent Samples, p. 425
define, and use in context, the concept of "sampling distribution of a difference of 2 population proportions, p1 and p2 ."
Mean, standard deviation, and sampling distribution of
use the critical value approach to perform hypothesis tests about the difference b/w 2 population proportions based on large and independent samples.
Hypothesis testing about p1-p2
use the p-value approach to perform hypothesis tests about the difference b/w 2 population proportions based on large and independent samples.
In Section 10.5.3, you are responsible for both the critical value approach and the p-value approach.
Interval estimation of p1-p2
5-5 Goodness-of-Fit Tests, p. 448
define, and use in context, the following key terms: chi-square distribution; multinomial experiment; observed frequency; expected frequency
The chi-square distribution
a multinomial experiment
observed and expected frequencies
use the critical value approach to perform hypothesis tests about goodness of fit.
A goodness-of-fit test
degrees of freedom for a goodness-of-fit test
test statistic for a goodness-of-fit test
5-6 Tests for Independence and Homogeneity, p. 459
define the term "contingency table", and use contingency tables to solve problems.
A contingency table
use the critical value approach to perform hypothesis tests about the independence of 2 attributes of a population.
A test of independence
Degrees of freedom for a test of independence
test statistic for a test of independence
Expected frequencies for a test of independence
use the critical value approach to perform hypothesis tests about the homogeneity of 2 or more populations.
A test of homogeneity
5-7 Inferences About the Population Variance, p. 468
use the critical value approach to perform a hypothesis test for the population variance, σ2, or for the population standard deviation, σ.
Hypothesis tests about the population variance
In Section 11.4.2, use the critical value approach.
Estimation of the population variance
Omit Section 11.4.1.
5-8 Analysis of Variance, p. 483
define, and use in context, the following key terms: F distribution; one-way analysis of variance (ANOVA)
The F distribution
One-way analysis of variance
test statistic F for a one-way anova test
use the critical value approach to perform a one-way ANOVA test.