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Introductory Statistics Using Technology

Prem S. Mann
John Wiley
Publication Date: 
Number of Pages: 
[Reviewed by
Katherine St. Clair
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The fifth edition of Prem S. Mann’s Introductory Statistics using Technology covers all the basic topics found in an introductory statistics course. It starts with standard graphical and numerical summaries for one variable, then moves to probability and random variables, and ends with inference procedures including one-way analysis of variance, simple linear regression, and chi-square goodness-of-fit and independence tests. There is a chapter on nonparametric inference methods, but, as in lots of new and revised editions these days, this chapter is only available by download off of the book’s web site. This text is accessible to a wide range of students and a calculus background is not needed.

The teaching options for this book are flexible because chapters tend to be broken into small sections. The exercises provided for most sections were divided into conceptual and procedural problems and application problems. Each chapter contains self-review problems with answers given in the back of the text. There are also mini-projects for each chapter which seem to entail either analysis of a larger set of data or an “activity” project which makes students collect data or search for and interpret articles which use statistics.

There are plenty of examples discussed in each chapter which are solved first by hand and then by technology, when possible. The three technologies used are a TI-83 calculator, MINITAB, and Excel using the KADDSTAT plug-in. The explanation accompanying each use of technology (e.g. how to calculate descriptive statistics or draw a histogram) is very thorough and would definitely help students using either of the three technology options. One drawback is that this approach can lead to a very lengthy example and busy pages. In some sections it was hard to determine when the example ended and the discussion picked up again.

My overall impression of Introductory Statistics using Technology was favorable. It presents most concepts in a concise manner while solving examples which use the concepts in detail. The text is especially suited for an instructor looking to incorporate any of the three technologies into their introductory class.

Katherine St. Clair is Assistant Professor of Statistics at Colby College.

Chapter 1: Introduction.

1.1 What is Statistics.

1.2 Types of Statistics.

Case Study 1-1. Collegians Charge On.

1.3 Population vs. Sample.

Case Study 1-2. Most Do Not Know Their Mail Carrier.

Case Study 1-3. Yes, Looks Can Kill Your Chance for a Job.

1.4 Basic Terms.

1.5 Types of Variables.

1.6 Cross-Section vs. Time-Series Data.

1.7 Sources of Data.

1.8 Summation Notation.

*Uses & Misuses.


*Supplementary Exercises.

*Self Review Test.

*Mini Project.

*Technology Assignments.

Chapter 2: Organizing Data.

2.1 Raw Data.

2.2 Organizing and Graphing Qualitative Data.

Case Study 2-1. If You Are Productive, It Must Be Tuesday.

Case Study 2-2. Favorite Donut Flavors.

2.3 Organizing and Graphing Quantitative Data.

Case Study 2-3. Graduates in Debt.

2.4 Shapes of Histograms.

Case Study 2-4. using Truncated Axes.

2.5 Cumulative Frequency Distributions.

2.6 Stem-and-Leaf Displays.

Chapter 3: Numerical Descriptive Measures.

3.1 Measures of Central Tendency for Ungrouped Data.

Case Study 3-1. Lifespan of $1 Bill: 18 Months.

Case Study 3-2. Education and Earnings.

3.2 Measures of Dispersion for Ungrouped Data.

3.3 Mean, Variance, and Standard Deviation for Grouped Data.

Case Study 3-3. Here Comes the SD.

3.4 Use of Standard Deviation.

3.5 Measures of Position.

3.6 Box-and-Whisper Plot.

Chapter 4: Probability.

4.1 Experiment, Outcomes & Sample Space.

4.2 Calculating Probability.

4.3 Counting Rule.

4.4 Marginal & Conditional Probabilities.

Case Study 4-1. Facing a Crowd Isn't Easy.

4.5 Mutually Exclusive Events.

4.6 Independent vs. Dependent Events.

4.7 Complementary Events.

4.8 Intersection of Events & the Multiplication Rule.

Case Study 4-2. Baseball Players Have "Slumps" and "Streaks".

4.9 Union of Events and the Addition Rule.

Chapter 5: Discrete Random Variables & Their Probability Distributions.

5.1 Random Variables.

5.2 Probability Distribution of a Discrete Random Variable.

5.3 Mean of a Discrete Random Variable.

5.4 Standard Deviation of a Discrete Random Variable.

Case Study 5-1. Aces High Instant Lottery Game.

5.5 Factorials and Combinations.

Case Study 5-2. Playing Lotto.

5.6 The Binomial Probability Distribution.

5.7 The Hypergeometric Probability Distribution.

5.8 The Poisson Probability Distribution.

Case Study 5-3. Ask Mr. Statistics.

Chapter 6: Continuous Random Variables and the Normal Distribution.

6.1 Continuous Probability Distribution.

Case Study 6-1. Distribution of Time Taken to Run a Road Race.

6.2 The Normal Distribution.

6.3 The Standard Normal Distribution.

6.4 Standardizing a Normal Distriution.

6.5 Applications of the Normal Distribution.

6.6 Determining the z and x Values When an Area Under the Normal Distribution Curve is Known.

6.7 The Normal Approximation to the Binomial Distribution.

Chapter 7: Sampling Distributions.

7.1 Population and Sampling Distributions.

7.2 Sampling and Non-Sampling Errors.

7.3 Mean and Standard Deviation of x.

7.4 Shape of the Sampling Distribution x.

7.5 Applications of the Sampling Distribution of x.

7.6 Population and Sample Proportions.

7.7 Mean, Standard Deviation, and Shape of the Sampling Distribution of p.

Case Study 7-1. Calling the Vote for Congress.

Chapter 8: Estimation of the Mean and Proportion.

8.1 Estimation: An Introduction.

8.2 Point and Interval Estimates.

8.3 Interval Estimation of a Population Mean: Large Samples.

Case Study 8-1. Where Your Energy Dollar Goes.

8.4 Interval Estimation of a Population Proportion: Small Samples.

Case Study 8-2. Cardiac Demands of Heavy Snow Shoveling.

8.5 Interval Estimation of a Population Proportion: Large Samples.

Case Study 8-3. What Is Causing Job Stress?

8.6 Determining the Sample Size for the Estimation of Mean.

8.7 Determining the Sample Size for the Estimation of Proportion.

Chapter 9: Hypothesis Tests About the Mean and Proportion.

9.1 Hypothesis Tests: An Introduction.

9.2 Hypothesis Tests About æ for Large samples Using the p-Value Approach.

9.3 Hypothesis Tests About a Population Mean: Large Samples.

9.4 Hypothesis Tests About a Population Mean: Small Samples.

9.5 Hypothesis Tests About a Population Proportion: Large Samples

Case Study 9-1. Employees' Concerns.

Chapter 10: Estimation & Hypothesis Testing: Two Populations.

10.1 Inferences About the Difference Between Two Population Means For Large and Independent Samples.

10.2 Inferences About the Difference Between Two Populations Means for Small and Independent Samples: Equal Standard Deviations.

10.3 Inferences About the Difference Between Two Population Means for Small and Independent Samples: Unequal Standard Deviations.

10.4 Inferences About the Difference Between Two Population Means for Paired Samples.

10.5 Inferences About the Difference Between Two Population Proportions for Large and Independent Samples.

Case Study 10-1. Teenagers Butt Out of Smoking.

Chapter 11: Chi Square Tests.

11.1 The Chi Square Distribution.

11.2 A Goodeness-of-Fit Test.

Case Study 11-1. Up with "the Color Purple".

11.3 Contingency Tables.

11.4 A Test of Independence or Homogeneity.

11.5 Inferences About the Population Variance.

Chapter 12: Analysis of Variance.

12.1 The F Distribution.

12.2 One-Way Analysis of Variance.

Chapter 13: Simple Linear Regression.

13.1 Simple Linear Regression Model.

13.2 Simple Linear Regression Analysis.

13.3 Standard Deviation of Random Errors.

13.4 Coefficient of Determination.

13.5 Inferences About B.

13.6 Linear Correlation.

13.7 Regression Analysis: A Complete Example.

13.8 Using the Regression Model.

13.9 Cautions in Using Regression.

Chapter 14: Nonparametric Methods.

14.1 The Sign Test.

14.2 The Wilcoxon Signed-Rank Test for Two Dependent Samples.

14.3 The Wilcoxon Rank Sum Test for Two Independent Samples.

14.4 The Kruskal-Wallis Test.

14.5 The Spearman Rho Rank Correlation Coefficient Test.

14.6 The Runs Test for Randomness.

Appendix A: Sample Surveys, Sampling Techniques, and Design Experiments.

A.1 Sources of Data.

A.1.1 Internal Sources.

A.1.2 External Sources.

A.1.3 Surveys and Experiments.

A.2 Sample Surveys and Sampling Techniques.

A.2.1 Why Sample.

A.2.2 Random and Nonrandom Samples.

A.2.3 Sampling and Nonsampling Errors.

A.2.4 Random Sampling Techniques.

A.3 Design of Experiments.

Appendix B: Explanation of Data Sets.

Data Set I: City Data.

Data Set II: Data On States.

Data Set III: NBA Data.

Data Set IV: Population Data on Manchester Road Race.

Data Set V: Sample of 500 Observations Selected from Manchester Road Race Data.

Appendix C: Statistical Tables.

I Table of Binomial Probabilities.

II Values of e-.

III Table of Poisson Probabilities.

IV Standard Normal Distribution Table.

V The t Distribution Table.

VI Chi-Square Distribution Table.

VII The F Distribution Table.

VIII Critical Values of X for the Sign Test.

IX Critical Values of T for the Wilcoxon Signed-Rank Test.

X Critical Values of T for the Wilcoxon Rank Sum Test.

XI Critical Values for the Spearman Rho Rank Correlation Coefficient Test.

XII Critical Values for a Two-Tailed Runs Test with … = .05.




* These features are included at the end of every chapter.