PREFACE TO THE INSTRUCTOR 15
variable is grouped. This allows the student to see the
complete distribution of each category of the explanatory
variable. In addition, the material now includes stacked (or
segmented) conditional bar graphs.
Chapter 6 Discrete Probability Distributions
The graphical representation of discrete probability distri-
butions no longer is presented as a probability histogram.
Instead, the graph of a discrete probability distribution is
presented to emphasize that the data is discrete. Therefore,
the graph of discrete probability distributions is drawn using
vertical lines above each value of the random variable to a
height that is the probability of the random variable.
Chapter 7 The Normal Probability
Distribution
The assessment of normality of a random variable using nor-
mal probability plots has changed. We no longer rely on nor-
mal probability plots drawn using Minitab. Instead, we utilize
the correlation between the observed data and normal scores.
This approach is based upon the research of S.W. Looney
and T. R. Gulledge in their paper, “Use of the Correlation
Coefcient with Normal Probability Plots,” published in the
American Statistician. This material may be skipped with-
out loss of continuity (especially for those who postponed
the material in Chapter 4). Some problems from Chapter 9
through 13 may need to be skipped or edited, however.
Chapter 9 Estimating the Value
of a Parameter
The Putting It Together section went through an extensive
renovation of the exercises. Emphasis is placed on
identifying the variable of interest in the study (in particular,
whether the variable is qualitative or quantitative). In
addition, there are problems that simply require the student
to identify the type of interval that could be constructed to
address the research concerns.
Chapter 10 Hypothesis Testing Regarding
a Parameter
The Putting It Together section went through an extensive
revision. Again, emphasis is placed on identifying the
variable of interest in the study. The exercises include a mix
of hypothesis tests and condence intervals. Plus, there are
problems that require the student to identify the type of
inference that could be constructed to address the research.
Chapter 11 Inference on Two Samples
The material on inference for two dependent population pro-
portions is now covered in Section 12.3 utilizing the chi-square
distribution. As in Chapter 9 and Chapter 10, the Putting It
Together section’s exercises were revised extensively. There is
a healthy mix of two-sample and single-sample analysis (both
hypothesis tests and condence intervals). This will help stu-
dents to develop the ability to determine the type of analysis
required for a given research objective.
Chapter 12 Inference on Categorical
Data
In Section 12.2, we now emphasize how to distinguish
between the chi-square test for independence and the chi-
square test for homogeneity of proportions. The material
on inference for two dependent proportions formerly in
Section 11.1 is now a stand-alone Section 12.3 so that we
might use chi-square methods to analyze the data.
Chapter 13 Comparing Three or More
Means
The Analysis of Variance procedures now include
construction of normal probability plots of the residuals to
verify the normality requirement.
Chapter 14 Inference on the Least-Squares
Regression Model and Multiple Regression
Section 14.3 Multiple Regression from the fourth edition
has been expanded to four sections. The discussion now
includes increased emphasis on interaction, dummy
variables, and polynomial regression. Building regression
models is now its own section and includes stepwise,
forward, and backward regression model building.
Flexible to Work with Your Syllabus
To meet the varied needs of diverse syllabi, this book has
been organized to be exible.
You will notice the “Preparing for This Section”
material at the beginning of each section, which will tip
you off to dependencies within the course. The two most
common variations within an introductory statistics course
are the treatment of regression analysis and the treatment
of probability.
• Coverage of Correlation and Regression The text was
written with the descriptive portion of bivariate data
(Chapter 4) presented after the descriptive portion of
univariate data (Chapter 3). Instructors who prefer
to postpone the discussion of bivariate data can skip
Chapter 4 and return to it before covering Chapter 14.
(Because Section 4.5 on nonlinear regression is
covered by a select few instructors, it is located on the
website that accompanies the text in Adobe PDF form,
so that it can be easily printed.)
• Coverage of Probability The text allows for light to
extensive coverage of probability. Instructors wishing
to minimize probability may cover Section 5.1 and
skip the remaining sections. A mid-level treatment
of probability can be accomplished by covering
Sections 5.1 through 5.3. Instructors who will cover the
chi-square test for independence will want to cover
Sections 5.1 through 5.3. In addition, an instructor
who will cover binomial probabilities will want to
cover independence in Section 5.3 and combinations
in Section 5.5.
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