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by Terry Sincich, David M. Levine and David Stephan

Cover type: HardbackEdition: 2ND 02

Copyright: 2002

Publisher: Prentice Hall, Inc.

Published: 2002

International: No

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For undergraduates with a background in college algebra in an introductory statistics course.

More than any of its competition, this text integrates technology into the teaching of the introductory statistics course. Both Microsoft Excel and MINITAB are incorporated as tools for data analysis into a practical introduction to statistics. Real-world applications and critical thinking skills are emphasized throughout to prepare students for the workplace.

- NEW--Excel and MINTAB tutorials--Added throughout the text.

Gives students access to step-by-step instructions and screen shots for using the software to perform the statistical techniques presented in the chapter.

- NEW--''Statistics in the Real World Revisited'' examples--Introduce each chapter and have been added throughout the text.

Illustrates the value of statistics in real-world settings for students.

- NEW--Microsoft Excel and MINITAB are integrated into the text.

Allows the student to learn the subject matter and the software simultaneously.

- NEW--Reorganized content--Rank tests are integrated throughout the text, dot plots added in Chapter 2, cumulative binomial tables added to appendix, section on the normal approximation to the binomial distribution added to Chapter 6, goodness-of-fit test of multinomial category probabilities added to Chapter 8.
- NEW--10-20% of the more than 1,000 problems have been revised or updated--According to chapter.

Gives ample practice for students who can check their answers in the appendix and provides a wide choice for instructors.

- New ideas are introduced and illustrated by real data based examples taken from a wide variety of sources and disciplines.

Demonstrates how to solve a wide range of statistical problems in the real world.

- Emphasis on critical thinking and interpretation of Excel/MINITAB Output.

Encourages students to develop critical thinking skills that will allow them to realize greater success in the workplace; allows instructors to focus on the statistical analysis of data and the interpretation of the results rather than calculations.

- Built-in Study Guide.

Helps students retain new ideas and prepare for tests.

- Self-test questions--Appear immediately after important ideas have been introduced.

Allows student to check comprehension of new concepts and to develop good study habits.

- Summary Boxes.

Outline step-by-step instructions for the statistical techniques presented.

- Side Notes.

Give students additional explanations of key ideas adjacent to where the concept is first referenced.

- Lists of Key Terms, Formulas, and Symbols with page references conclude each chapter.

Guides the student back to the text where they can review the element in context.

**Sincich, Terry : University of South Florida**

**Levine, David M. : Baruch College**

**Stephan, David : Baruch College **

Microsoft Excel Primer.

MINITAB Primer.

1. Introduction: Statistics and Data.

What Is Statistics? Types of Data. Descriptive vs. Inferential Statistics. Collecting Data. Random Sampling. Other Types of Samples. Ethical Issues and Other Concerns in Statistical Applications. What Readers Need to Know about Microsoft Excel, the PHStat Add-In, and Minitab for this textbook.

2. Exploring Data with Graphs and Tables.

The Objective of Data Description. Describing a Single Qualitative Variable: Frequency Tables, Bar Graphs and Pie Charts. Describing a Single Qualitative Variable: Frequency Tables, Dot Plots, Stem-and-Leaf Displays and Histograms. Exploring the Relationship between Two Qualitative Variables: Cross-Classification Tables and Side-by-Side Bar Charts. Exploring the Relationship between Two Quantitative Variables: Scatterplots. Proper Graphical Presentation.

3. Exploring Quantitative Data with Numerical Descriptive Measures.

Objectives of Numerical Descriptive Measures. Summation Notation. Measures of Central Tendency: Mean, Median, and Mode. Measures of Variation: Range, Variance, and Standard Deviation. Interpreting the Standard Deviation. Measures of Relative Standing: Percentiles and z-scores. Methods for Detecting Outliers. A Measure of Association: Correlation. Numerical Descriptive Measures for Populations.

4. Probability: Basic Concepts.

The Role of Probability in Statistics. Experiments, Events, and The Probability of an Event. Probability Rules for Mutually Exclusive Events. The Combinatorial Rule for Counting Simple Events. Conditional Probability and Independence. The Additive and Multiplicative Laws of Probability.

5. Discrete Probability Distributions.

Random Variables. Probability Models for Discrete Random Variables. The Binomial Probability Distribution. The Poisson Probability Distribution. The Hypergeometric Probability Distribution.

6. Normal Probability Distributions.

Probability Models for Continuous Random Variables. The Normal Probability Distribution. Descriptive Methods for Assessing Normality. The Normal Approximation to the Binomial Distribution. Sampling Distributions. The Sampling Distribution of the Sample Mean and the Central Limit Theorem.

7. Estimation of Population Parameters Using Confidence Intervals: One Sample.

Point Estimators. Estimation of a Population Mean: Normal (z) Statistic. Estimation of a Population Mean: Student's (t) Statistic. Estimation of a Population Proportion. Choosing the Sample Size. Estimation of a Population Variance.

8. Testing Hypotheses about Population Parameters: One Sample.

The Relationship between Hypothesis Tests and Confidence Intervals. Hypothesis-Testing Methodology: Forming Hypotheses. Hypothesis-Testing Methodology: Test Statistics and Rejection Regions. Guidelines for Determining the Target Parameter. Testing a Population Mean. Reporting Test Results: p-Values. Testing a Population Proportion. Testing a Population Variance. Testing Category Probabilities for a Qualitative Variable. Potential Hypothesis-Testing Pitfalls and Ethical Issues.

9. Inferences about Population Parameters: Two Samples.

Determining the Target Parameter. Comparing Two Population Means: Independent Samples. Comparing Two Population Means: Matched Pairs. Comparing Two Population Proportions: Independent Samples. Comparing Two Population Proportions: Contingency Tables. Comparing Two Population Variances. A Nonparametric Test for Comparing Two Populations: Independent Samples. A Nonparametric Test for Comparing Two Populations: Matched Pairs.

10. Regression Analysis.

Introduction to Regression Models. The Straight-Line Model Simple Linear Regression. Estimating and Interpreting the Model Parameters. Model Assumptions. Measuring Variability around the Least Squares. Inferences about the Slope. Inferences about the Correlation Coefficient. The Coefficient of Determination. Using the Model for Estimation and Prediction. Computations in Simple Linear Regression. Residual Analysis: Checking the Assumptions. Multiple Regression Models. A Nonparametric Test for Rank Correlation. Pitfalls in Regression and Ethical Issues.

11. Analysis of Variance.

Experimental Design. ANOVA Fundamentals. Completely Randomized Designs: One-Way ANOVA. Follow-Up Analysis: Multiple Comparisons of Means. Factorial Designs: Two-Way ANOVA. Checking ANOVA Assumptions. A Nonparametric Test for Comparing Populations: Independent Samples.

Appendix A: Review of Arithmetic and Algebra.

Appendix B: Statistical Tables.

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Summary

For undergraduates with a background in college algebra in an introductory statistics course.

More than any of its competition, this text integrates technology into the teaching of the introductory statistics course. Both Microsoft Excel and MINITAB are incorporated as tools for data analysis into a practical introduction to statistics. Real-world applications and critical thinking skills are emphasized throughout to prepare students for the workplace.

- NEW--Excel and MINTAB tutorials--Added throughout the text.

Gives students access to step-by-step instructions and screen shots for using the software to perform the statistical techniques presented in the chapter.

- NEW--''Statistics in the Real World Revisited'' examples--Introduce each chapter and have been added throughout the text.

Illustrates the value of statistics in real-world settings for students.

- NEW--Microsoft Excel and MINITAB are integrated into the text.

Allows the student to learn the subject matter and the software simultaneously.

- NEW--Reorganized content--Rank tests are integrated throughout the text, dot plots added in Chapter 2, cumulative binomial tables added to appendix, section on the normal approximation to the binomial distribution added to Chapter 6, goodness-of-fit test of multinomial category probabilities added to Chapter 8.
- NEW--10-20% of the more than 1,000 problems have been revised or updated--According to chapter.

Gives ample practice for students who can check their answers in the appendix and provides a wide choice for instructors.

- New ideas are introduced and illustrated by real data based examples taken from a wide variety of sources and disciplines.

Demonstrates how to solve a wide range of statistical problems in the real world.

- Emphasis on critical thinking and interpretation of Excel/MINITAB Output.

Encourages students to develop critical thinking skills that will allow them to realize greater success in the workplace; allows instructors to focus on the statistical analysis of data and the interpretation of the results rather than calculations.

- Built-in Study Guide.

Helps students retain new ideas and prepare for tests.

- Self-test questions--Appear immediately after important ideas have been introduced.

Allows student to check comprehension of new concepts and to develop good study habits.

- Summary Boxes.

Outline step-by-step instructions for the statistical techniques presented.

- Side Notes.

Give students additional explanations of key ideas adjacent to where the concept is first referenced.

- Lists of Key Terms, Formulas, and Symbols with page references conclude each chapter.

Guides the student back to the text where they can review the element in context.

Author Bio

**Sincich, Terry : University of South Florida**

**Levine, David M. : Baruch College**

**Stephan, David : Baruch College **

Table of Contents

Microsoft Excel Primer.

MINITAB Primer.

1. Introduction: Statistics and Data.

What Is Statistics? Types of Data. Descriptive vs. Inferential Statistics. Collecting Data. Random Sampling. Other Types of Samples. Ethical Issues and Other Concerns in Statistical Applications. What Readers Need to Know about Microsoft Excel, the PHStat Add-In, and Minitab for this textbook.

2. Exploring Data with Graphs and Tables.

The Objective of Data Description. Describing a Single Qualitative Variable: Frequency Tables, Bar Graphs and Pie Charts. Describing a Single Qualitative Variable: Frequency Tables, Dot Plots, Stem-and-Leaf Displays and Histograms. Exploring the Relationship between Two Qualitative Variables: Cross-Classification Tables and Side-by-Side Bar Charts. Exploring the Relationship between Two Quantitative Variables: Scatterplots. Proper Graphical Presentation.

3. Exploring Quantitative Data with Numerical Descriptive Measures.

Objectives of Numerical Descriptive Measures. Summation Notation. Measures of Central Tendency: Mean, Median, and Mode. Measures of Variation: Range, Variance, and Standard Deviation. Interpreting the Standard Deviation. Measures of Relative Standing: Percentiles and z-scores. Methods for Detecting Outliers. A Measure of Association: Correlation. Numerical Descriptive Measures for Populations.

4. Probability: Basic Concepts.

The Role of Probability in Statistics. Experiments, Events, and The Probability of an Event. Probability Rules for Mutually Exclusive Events. The Combinatorial Rule for Counting Simple Events. Conditional Probability and Independence. The Additive and Multiplicative Laws of Probability.

5. Discrete Probability Distributions.

Random Variables. Probability Models for Discrete Random Variables. The Binomial Probability Distribution. The Poisson Probability Distribution. The Hypergeometric Probability Distribution.

6. Normal Probability Distributions.

Probability Models for Continuous Random Variables. The Normal Probability Distribution. Descriptive Methods for Assessing Normality. The Normal Approximation to the Binomial Distribution. Sampling Distributions. The Sampling Distribution of the Sample Mean and the Central Limit Theorem.

7. Estimation of Population Parameters Using Confidence Intervals: One Sample.

Point Estimators. Estimation of a Population Mean: Normal (z) Statistic. Estimation of a Population Mean: Student's (t) Statistic. Estimation of a Population Proportion. Choosing the Sample Size. Estimation of a Population Variance.

8. Testing Hypotheses about Population Parameters: One Sample.

The Relationship between Hypothesis Tests and Confidence Intervals. Hypothesis-Testing Methodology: Forming Hypotheses. Hypothesis-Testing Methodology: Test Statistics and Rejection Regions. Guidelines for Determining the Target Parameter. Testing a Population Mean. Reporting Test Results: p-Values. Testing a Population Proportion. Testing a Population Variance. Testing Category Probabilities for a Qualitative Variable. Potential Hypothesis-Testing Pitfalls and Ethical Issues.

9. Inferences about Population Parameters: Two Samples.

Determining the Target Parameter. Comparing Two Population Means: Independent Samples. Comparing Two Population Means: Matched Pairs. Comparing Two Population Proportions: Independent Samples. Comparing Two Population Proportions: Contingency Tables. Comparing Two Population Variances. A Nonparametric Test for Comparing Two Populations: Independent Samples. A Nonparametric Test for Comparing Two Populations: Matched Pairs.

10. Regression Analysis.

Introduction to Regression Models. The Straight-Line Model Simple Linear Regression. Estimating and Interpreting the Model Parameters. Model Assumptions. Measuring Variability around the Least Squares. Inferences about the Slope. Inferences about the Correlation Coefficient. The Coefficient of Determination. Using the Model for Estimation and Prediction. Computations in Simple Linear Regression. Residual Analysis: Checking the Assumptions. Multiple Regression Models. A Nonparametric Test for Rank Correlation. Pitfalls in Regression and Ethical Issues.

11. Analysis of Variance.

Experimental Design. ANOVA Fundamentals. Completely Randomized Designs: One-Way ANOVA. Follow-Up Analysis: Multiple Comparisons of Means. Factorial Designs: Two-Way ANOVA. Checking ANOVA Assumptions. A Nonparametric Test for Comparing Populations: Independent Samples.

Appendix A: Review of Arithmetic and Algebra.

Appendix B: Statistical Tables.

Publisher Info

Publisher: Prentice Hall, Inc.

Published: 2002

International: No

Published: 2002

International: No

For undergraduates with a background in college algebra in an introductory statistics course.

More than any of its competition, this text integrates technology into the teaching of the introductory statistics course. Both Microsoft Excel and MINITAB are incorporated as tools for data analysis into a practical introduction to statistics. Real-world applications and critical thinking skills are emphasized throughout to prepare students for the workplace.

- NEW--Excel and MINTAB tutorials--Added throughout the text.

Gives students access to step-by-step instructions and screen shots for using the software to perform the statistical techniques presented in the chapter.

- NEW--''Statistics in the Real World Revisited'' examples--Introduce each chapter and have been added throughout the text.

Illustrates the value of statistics in real-world settings for students.

- NEW--Microsoft Excel and MINITAB are integrated into the text.

Allows the student to learn the subject matter and the software simultaneously.

- NEW--Reorganized content--Rank tests are integrated throughout the text, dot plots added in Chapter 2, cumulative binomial tables added to appendix, section on the normal approximation to the binomial distribution added to Chapter 6, goodness-of-fit test of multinomial category probabilities added to Chapter 8.
- NEW--10-20% of the more than 1,000 problems have been revised or updated--According to chapter.

Gives ample practice for students who can check their answers in the appendix and provides a wide choice for instructors.

- New ideas are introduced and illustrated by real data based examples taken from a wide variety of sources and disciplines.

Demonstrates how to solve a wide range of statistical problems in the real world.

- Emphasis on critical thinking and interpretation of Excel/MINITAB Output.

Encourages students to develop critical thinking skills that will allow them to realize greater success in the workplace; allows instructors to focus on the statistical analysis of data and the interpretation of the results rather than calculations.

- Built-in Study Guide.

Helps students retain new ideas and prepare for tests.

- Self-test questions--Appear immediately after important ideas have been introduced.

Allows student to check comprehension of new concepts and to develop good study habits.

- Summary Boxes.

Outline step-by-step instructions for the statistical techniques presented.

- Side Notes.

Give students additional explanations of key ideas adjacent to where the concept is first referenced.

- Lists of Key Terms, Formulas, and Symbols with page references conclude each chapter.

Guides the student back to the text where they can review the element in context.

**Sincich, Terry : University of South Florida**

**Levine, David M. : Baruch College**

**Stephan, David : Baruch College **

Microsoft Excel Primer.

MINITAB Primer.

1. Introduction: Statistics and Data.

What Is Statistics? Types of Data. Descriptive vs. Inferential Statistics. Collecting Data. Random Sampling. Other Types of Samples. Ethical Issues and Other Concerns in Statistical Applications. What Readers Need to Know about Microsoft Excel, the PHStat Add-In, and Minitab for this textbook.

2. Exploring Data with Graphs and Tables.

The Objective of Data Description. Describing a Single Qualitative Variable: Frequency Tables, Bar Graphs and Pie Charts. Describing a Single Qualitative Variable: Frequency Tables, Dot Plots, Stem-and-Leaf Displays and Histograms. Exploring the Relationship between Two Qualitative Variables: Cross-Classification Tables and Side-by-Side Bar Charts. Exploring the Relationship between Two Quantitative Variables: Scatterplots. Proper Graphical Presentation.

3. Exploring Quantitative Data with Numerical Descriptive Measures.

Objectives of Numerical Descriptive Measures. Summation Notation. Measures of Central Tendency: Mean, Median, and Mode. Measures of Variation: Range, Variance, and Standard Deviation. Interpreting the Standard Deviation. Measures of Relative Standing: Percentiles and z-scores. Methods for Detecting Outliers. A Measure of Association: Correlation. Numerical Descriptive Measures for Populations.

4. Probability: Basic Concepts.

The Role of Probability in Statistics. Experiments, Events, and The Probability of an Event. Probability Rules for Mutually Exclusive Events. The Combinatorial Rule for Counting Simple Events. Conditional Probability and Independence. The Additive and Multiplicative Laws of Probability.

5. Discrete Probability Distributions.

Random Variables. Probability Models for Discrete Random Variables. The Binomial Probability Distribution. The Poisson Probability Distribution. The Hypergeometric Probability Distribution.

6. Normal Probability Distributions.

Probability Models for Continuous Random Variables. The Normal Probability Distribution. Descriptive Methods for Assessing Normality. The Normal Approximation to the Binomial Distribution. Sampling Distributions. The Sampling Distribution of the Sample Mean and the Central Limit Theorem.

7. Estimation of Population Parameters Using Confidence Intervals: One Sample.

Point Estimators. Estimation of a Population Mean: Normal (z) Statistic. Estimation of a Population Mean: Student's (t) Statistic. Estimation of a Population Proportion. Choosing the Sample Size. Estimation of a Population Variance.

8. Testing Hypotheses about Population Parameters: One Sample.

The Relationship between Hypothesis Tests and Confidence Intervals. Hypothesis-Testing Methodology: Forming Hypotheses. Hypothesis-Testing Methodology: Test Statistics and Rejection Regions. Guidelines for Determining the Target Parameter. Testing a Population Mean. Reporting Test Results: p-Values. Testing a Population Proportion. Testing a Population Variance. Testing Category Probabilities for a Qualitative Variable. Potential Hypothesis-Testing Pitfalls and Ethical Issues.

9. Inferences about Population Parameters: Two Samples.

Determining the Target Parameter. Comparing Two Population Means: Independent Samples. Comparing Two Population Means: Matched Pairs. Comparing Two Population Proportions: Independent Samples. Comparing Two Population Proportions: Contingency Tables. Comparing Two Population Variances. A Nonparametric Test for Comparing Two Populations: Independent Samples. A Nonparametric Test for Comparing Two Populations: Matched Pairs.

10. Regression Analysis.

Introduction to Regression Models. The Straight-Line Model Simple Linear Regression. Estimating and Interpreting the Model Parameters. Model Assumptions. Measuring Variability around the Least Squares. Inferences about the Slope. Inferences about the Correlation Coefficient. The Coefficient of Determination. Using the Model for Estimation and Prediction. Computations in Simple Linear Regression. Residual Analysis: Checking the Assumptions. Multiple Regression Models. A Nonparametric Test for Rank Correlation. Pitfalls in Regression and Ethical Issues.

11. Analysis of Variance.

Experimental Design. ANOVA Fundamentals. Completely Randomized Designs: One-Way ANOVA. Follow-Up Analysis: Multiple Comparisons of Means. Factorial Designs: Two-Way ANOVA. Checking ANOVA Assumptions. A Nonparametric Test for Comparing Populations: Independent Samples.

Appendix A: Review of Arithmetic and Algebra.

Appendix B: Statistical Tables.