# Introductory Statistics A Problem Solving Approach

, by Kokoska, Stephen**Note:**Supplemental materials are not guaranteed with Rental or Used book purchases.

- ISBN: 9781464111693 | 1464111693
- Cover: Hardcover
- Copyright: 12/17/2014

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Steve Kokoska received his undergraduate degree from Boston College, and his M.S and Ph.D. from the University of New Hampshire. His initial research interests included the statistical analysis of cancer chemoprevention experiments. He has published a number of research papers in mathematics journals, including: Biometrics, Anticancer Research, and Computer Methods and Programs in Biomedicine. He has also presented results at national conferences, written several books, and been awarded grants from the National Science Foundation, the Center for Rural Pennsylvania, and the Ben Franklin Program.

Steve is a long-time consultant for the College Board and conducted workshops in Brazil, the Dominican Republic, and China. He was the AP Calculus Chief Reader for four years, and has been involved with calculus reform and the use of technology in the classroom. He has been teaching at Bloomsburg University for 25years and recently served as Director of the Honors Program.

Steve has been teaching introductory statistics classes throughout his academic career, and there is no doubt that this is his favorite course. This class (and text) provides students with basic, life-long, quantitative skills that they will use in almost any job and teaches them how to think and reason logically. Steve believes very strongly in data-driven decisions and conceptual understanding through problem solving.

**0. Why Study Statistics**

The Science of Intuition

The Statistical Inference Procedure

Problem Solving

With a Little Help From Technology

**1. An Introduction to Statistics and Statistical Inference**

1.1 Statistics Today

1.2 Populations, Samples, Probability, and Statistics

1.3 Experiments and Random Samples

**2. Tables and Graphs for Summarizing Data**

2.1 Types of Data

2.2 Bar Charts and Pie Charts

2.3 Stem-and-Leaf Plots

2.4 Frequency Distributions and Histograms

**3. Numerical Summary Measures**

3.1 Measures of Central Tendency

3.2 Measures of Variability

3.3 The Empirical Rule and Measures of Relative Standing

3.4 Five-Number Summary and Box Plots

**4 Probability**

4.1 Experiments, Sample Spaces, and Events

4.2 An Introduction to Probability

4.3 Counting Techniques

4.4 Conditional Probability

4.5 Independence

**5. Random Variables and Discrete Probability Distributions**

5.1 Random Variables

5.2 Probability Distributions for Discrete Random Variables

5.3 Mean, Variance, and Standard Deviation for a Discrete Random Variable

5.4 The Binomial Distribution

5.5 Other Discrete Distributions

**6. Continuous Probability Distributions**

6.1 Probability Distributions for Continuous Random Variables

6.2 The Normal Distribution

6.3 Checking the Normality Assumption

6.4 The Exponential Distribution

**7. Sampling Distributions**

7.1 Statistics, Parameters, and Sampling Distributions

7.2 The Sampling Distribution of the Sample Mean and the Central

Limit Theorem

7.3 The Distribution of the Sample Proportion

**8. Confidence Intervals Based on a Single Sample**

8.1 Point Estimation

8.2 A Confidence Interval for a Population Mean when s Is Known

8.3 A Confidence Interval for a Population Mean when s Is Unknown

8.4 A Large-Sample Confidence Interval for a Population Proportion

8.5 A Confidence Interval for a Population Variance

**9. Hypothesis Tests Based on a Single Sample**

9.1 The Parts of a Hypothesis Test and Choosing the Alternative Hypothesis

9.2 Hypothesis Test Errors

9.3 Hypothesis Tests Concerning a Population Mean when s Is Known

9.4 p Values

9.5 Hypothesis Tests Concerning a Population Mean when s Is Unknown

9.6 Large-Sample Hypothesis Tests Concerning a Population Proportion

9.7 Hypothesis Tests Concerning a Population Variance or Standard Deviation

**10. Confidence Intervals and Hypothesis Tests Based on Two Samples or Treatments**

10.1 Comparing Two Population Means Using Independent Samples when Population Variances Are Known

10.2 Comparing Two Population Means Using Independent Samples from Normal Populations

10.3 Paired Data

10.4 Comparing Two Population Proportions Using Large Samples

10.5 Comparing Two Population Variances or Standard Deviations

**11. The Analysis of Variance**

11.1 One-Way ANOVA

11.2 Isolating Differences

11.3 Two-Way ANOVA

**12. Correlation and Linear Regression**

12.1 Simple Linear Regression

12.2 Hypothesis Tests and Correlation

12.3 Inferences Concerning the Mean Value and an Observed Value of Y for x 5 x*

12.4 Regression Diagnostics

12.5 Multiple Linear Regression

**13. Categorical Data and Frequency Tables**

13.1 Univariate Categorical Data, Goodness-of-Fit Tests

13.2 Bivariate Categorical Data, Tests for Homogeneity and Independence

**14. Nonparametric Statistics**

14.1 The Sign Test

14.2 The Signed-Rank Test

14.3 The Rank-Sum Test

14.4 The Kruskal–Wallis Test

14.5 The Runs Test

14.6 Spearman’s Rank Correlation

Notes and Data Sources

Tables Appendix

Answers to Odd-Numbered Exercises

Index

Optional Online Sections

Section 6.5 The Normal Approximation to the Binomial Distribution

Section 12.6 The Polynomial and Qualitative Predictor Models

Section 12.7 Model Selection Procedures