Transcript Lecture 52 - Review

Review
Lecture 52
Tue, Apr 26, 2005
Chapter 1





Sections 1.1 – 1.4.
Be familiar with the language and principles of
hypothesis testing.
Given two explicit hypotheses, be able to
calculate  and .
Given a value of the “test statistic,” be able to
calculate the p-value.
Etc.
Chapter 2


Sections 2.1 – 2.8.
Know the characteristics of the different
sampling methods:
Simple random sampling
 Stratified sampling
 Systematic sampling
 Cluster sampling.

Chapter 2

Be familiar with the different types of bias:
Selection bias.
 Response bias.
 Non-response bias.
 Experimenter bias.


Etc.
Chapter 3


Sections 3.1 – 3.5.
Know the difference between
An observational study and an experiment.
 A prospective study and a retrospective study.




Be able to distinguish among explanatory,
response, and confounding variables.
Be familiar with some methods of minimizing
bias.
Etc.
Chapter 4


Sections 4.1 – 4.3.2, 4.4.1 – 4.4.2, 4.4.4, 4.5.
Be able to draw correctly
Pie charts
 Bar graphs
 Frequency plots
 Histograms


Know which ones are appropriate for which
kinds of data.
Chapter 4


Be familiar with the important characteristics of
a distribution’s shape.
Etc.
Chapter 5


Sections 5.1 – 5.3.
Measures of center:
Mean
 Median
 Mode


Measures of variation
Range
 Interquartile range

Chapter 5
Variance
 Standard deviation



Be able to draw a boxplot.
Etc.
Chapter 6




Sections 6.1 – 6.4.
Be able to find a probability or percentile
associated with a normal distribution.
Be able to find a probability or percentile
associated with a uniform distribution.
Know and be able to apply the 68-95-99.7 Rule.
Chapter 6


Be able to draw a discrete probability
distribution and find probabilities associated
with it.
Etc.
Chapter 7





Section 7.5 – 7.5.1, 7.5.3.
Know what a random variable is.
Know the difference between discrete and
continuous random variables.
Be able to calculate the mean, variance, and
standard deviation of a discrete random variable
from its probability distribution.
Etc.
Chapter 8





Sections 8.1 – 8.4.
Know what is meant by a sampling distribution
of a statistic.
Be very familiar with the Central Limit Theorem
for proportions, summarized on page 482.
Be very familiar with the Central Limit Theorem
for means, summarized on page 500.
Be able to recognize problems that call for the
Central Limit Theorem and be able to apply it.
Chapter 8


Understand what bias and variability mean for a
random variable.
Etc.
Chapter 9







Sections 9.1 – 9.4.
Know the sampling distribution of p^.
Know the criteria for when the sample size is
large enough.
Be able to test a hypothesis concerning p.
Be able to calculate a confidence interval for p.
Know the 5 steps of hypothesis testing.
Etc.
Chapter 10






Sections 10.1 – 10.3.
Know the sampling distribution ofx.
Know the criteria for when the sample size is
large enough.
Be able to test a hypothesis concerning.
Be able to calculate a confidence interval for .
Know how to decide whether to use the normal
distribution or the t distribution.
Chapter 10


Be able to find p-values and percentiles for the t
distribution.
Etc.
Chapter 11





Sections 11.1 – 11.2, 11.4 – 11.5.
Know the difference between paired samples
and independent samples.
Be able to test a hypothesis concerning the
difference between two population proportions.
Be able to estimate the difference between two
population proportions.
Know when and how to use a pooled estimate
of p.
Chapter 11





Be able to test a hypothesis concerning the
difference between two population means.
Be able to estimate the difference between two
population means.
Know the criteria in all cases for using the
normal distribution vs. the t distribution.
Know when and how to use a pooled estimate
of .
Etc.
Chapter 13



Sections 13.1 – 13.3, 13.7 – 13.7.1, 13.9.
Be able to draw a scatterplot of bivariate data.
Be familiar with the important characteristics:
Linear association.
 Positive or negative association.
 The strength of the association.


Know exactly what distinguishes the least
squares regression line from all other lines.
Chapter 13

Be able to calculate the following:
The coefficients a and b of the regression line.
 The residuals.
 The predicted value of y, for a given value of x.
 The residual sum of squares, SSE.
 The regression sum of squares, SSR.
 The total sum of squares, SST.
 The correlation coefficient r.
 The coefficient of determination r2.

Chapter 13



Be able to interpret the correlation coefficient.
Be able to interpret the coefficient of
determination.
Etc.
Chapter 14



Sections 14.1 – 14.5.
Be able to find chi-square probabilities and
percentiles.
Be able to perform hypothesis tests for
Goodness of fit (univariate data)
 Homogeneity (bivariate data)
 Independence (bivariate data)



In all cases, be able to find the expected counts.
Etc.
The TI-83




Most of the calculations in Chapters 1 – 10 can
be done on the TI-83.
You should be able to do them both on the TI-83
and by hand.
Most of the calculations in Chapters 11, 13, and
14 can be done on the TI-83.
Anything that can be done on the TI-83 in
Chapters 11 - 14, you do not need to be able to
do by hand.
Formulas




You should know the necessary formulas from
Chapters 1 – 10 and perhaps a few
miscellaneous formulas from Chapters 11 – 14.
The formulas that you do not need to know are
listed on the Statistical Formulas sheet.
Do not bring this sheet with you to the final; it
will be provided.
The standard normal table, the t tables, and the
chi square tables will be provided.