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ISBN13: 978-0130186799

ISBN10: 0130186791

Cover type:

Edition: 8TH 01

Copyright: 2001

Publisher: Prentice Hall, Inc.

Published: 2001

International: No

ISBN10: 0130186791

Cover type:

Edition: 8TH 01

Copyright: 2001

Publisher: Prentice Hall, Inc.

Published: 2001

International: No

For a one/two-term Business Statistics course.

Designed for students with a background in basic algebra, this best-selling introduction to statistics for business and economics text emphasizes inference--with extensive coverage of data collection and analysis as needed to evaluate the reported results of statistical studies and to make good business decisions. It stresses the development of statistical thinking, the assessment of credibility and the value of inferences made from data--both by those who consume and those who produce them--and features numerous case studies, examples, and exercises--that draw on real business situations and recent economic events.

Author Bio

**McClave, James T. : University of Florida **

**Benson, P. George : University of Georgia **

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

(NOTE: Each chapter concludes with ''Quick Review'')

1. Statistics, Data, and Statistical Thinking

The Science of Statistics

Types of Statistical Applications in Business

Fundamental Elements of Statistics

Processes (Optional)

Types of Data

Collecting Data

The Role of Statistics in Managerial Decision-Making

2. Methods for Describing Sets of Data

Describing Qualitative Data

Graphical Methods for Describing Quantitative Data

Summation Notation

Numerical Measures of Central Tendency

Numerical Measures of Variability

Interpreting the Standard Deviation

Numerical Measures of Relative Standing

Methods for Detecting Outliers (Optional)

Graphing Bivariate Relationships (Optional)

The Time Series Plot (Optional)

Distorting the Truth with Descriptive Techniques

3. Probability Events, Sample Spaces, and Probability

Unions and Intersections

Complementary Events

Additive Rule and Mutually Exclusive Events

Conditional Probability

The Multiplicative Rule and Independent Events

Random Sampling

4. Random Variables and Probability Distributions

Two Types of Random Variables

Probability Distributions for Discrete Random Variables

The Binomial Distribution

The Poisson Distribution (Optional)

Probability Distributions for Continuous Random Variables

The Uniform Distribution (Optional)

The Normal Distribution

Descriptive Methods for Assessing Normality

Approximating a Binomial Distribution with a Normal Distribution (Optional)

The Exponential Distribution (Optional)

Sampling Distributions

The Central Limit Theorem

5. Inferences Based on a Single Sample: Estimation with Confidence Intervals

Large-Sample Confidence Interval for a Population Mean

Small-Sample Confidence Interval for a Population Mean

Large-Sample Confidence Interval for a Population Proportion

Determining the Sample Size

6. Inferences Based on a Single Sample: Tests of Hypothesis

The Elements of a Test of Hypothesis

Large-Sample Test of Hypothesis about a Population Mean

Observed Significance Levels: p-Values

Small-Sample Test of Hypothesis about a Population Mean

Large-Sample Test of Hypothesis about a Population Proportion

A Nonparametric Test about a Population Median (Optional)

7. Comparing Population Means

Comparing Two Population Means: Independent Sampling

Comparing Two Population Means: Paired Difference Experiments

Determining the Sample Size

Testing the Assumption of Equal Population Variances (Optional)

A Nonparametric Test for Comparing Two Populations: Independent Sampling (Optional)

A Nonparametric Test for Comparing Two Populations: Paired Differences Experiment (Optional)

Comparing Three or More Population Means: Analysis of Variance (Optional)

8. Comparing Population Proportions

Comparing Two Population Proportions: Independent Sampling

Determining the Sample Size

Comparing Population Proportions: Multinomial Experiment

Contingency Table Analysis

9. Simple Linear Regression

Probabilistic Models

Fitting the Model: The Least Squares Approach

Model Assumptions

An Estimator of ...s2

Assessing the Utility of the Model: Making Inferences about the Slope ...b1

The Coefficient of Correlation

The Coefficient of Determination

Using the Model for Estimation and Prediction

Simple Linear Regression: A Complete Example

A Nonparametric Test for Correlation (Optional)

10. Introduction to Multiple Regression

Multiple Regression Models

The First-Order Model: Estimating and Interpreting the ...b Parameters

Model Assumptions

Inferences about the ...b Parameters

Checking the Overall Utility of a Model

Using the Model for Estimation and Prediction

Residual Analysis: Checking the Regression Assumptions

Some Pitfalls: Estimability, Multicollinearity, and Extrapolation

11. Methods for Quality Improvement

Quality, Processes, and Systems

Statistical Control

The Logic of Control Charts

A Control Chart for Monitoring the Mean of a Process: The x-Chart

A Control Chart for Monitoring the Variation of a Process: The R-Chart

A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart

Appendix A: Basic Counting Rules

Appendix B: Tables

Appendix C: Calculation Formulas for Analysis of Variance: Independent Sampling

Answers to Selected Exercises

References Index

James T. McClave, P. George Benson and Terry Sincich

ISBN13: 978-0130186799ISBN10: 0130186791

Cover type:

Edition: 8TH 01

Copyright: 2001

Publisher: Prentice Hall, Inc.

Published: 2001

International: No

For a one/two-term Business Statistics course.

Designed for students with a background in basic algebra, this best-selling introduction to statistics for business and economics text emphasizes inference--with extensive coverage of data collection and analysis as needed to evaluate the reported results of statistical studies and to make good business decisions. It stresses the development of statistical thinking, the assessment of credibility and the value of inferences made from data--both by those who consume and those who produce them--and features numerous case studies, examples, and exercises--that draw on real business situations and recent economic events.

Author Bio

**McClave, James T. : University of Florida **

**Benson, P. George : University of Georgia **

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

Table of Contents

(NOTE: Each chapter concludes with ''Quick Review'')

1. Statistics, Data, and Statistical Thinking

The Science of Statistics

Types of Statistical Applications in Business

Fundamental Elements of Statistics

Processes (Optional)

Types of Data

Collecting Data

The Role of Statistics in Managerial Decision-Making

2. Methods for Describing Sets of Data

Describing Qualitative Data

Graphical Methods for Describing Quantitative Data

Summation Notation

Numerical Measures of Central Tendency

Numerical Measures of Variability

Interpreting the Standard Deviation

Numerical Measures of Relative Standing

Methods for Detecting Outliers (Optional)

Graphing Bivariate Relationships (Optional)

The Time Series Plot (Optional)

Distorting the Truth with Descriptive Techniques

3. Probability Events, Sample Spaces, and Probability

Unions and Intersections

Complementary Events

Additive Rule and Mutually Exclusive Events

Conditional Probability

The Multiplicative Rule and Independent Events

Random Sampling

4. Random Variables and Probability Distributions

Two Types of Random Variables

Probability Distributions for Discrete Random Variables

The Binomial Distribution

The Poisson Distribution (Optional)

Probability Distributions for Continuous Random Variables

The Uniform Distribution (Optional)

The Normal Distribution

Descriptive Methods for Assessing Normality

Approximating a Binomial Distribution with a Normal Distribution (Optional)

The Exponential Distribution (Optional)

Sampling Distributions

The Central Limit Theorem

5. Inferences Based on a Single Sample: Estimation with Confidence Intervals

Large-Sample Confidence Interval for a Population Mean

Small-Sample Confidence Interval for a Population Mean

Large-Sample Confidence Interval for a Population Proportion

Determining the Sample Size

6. Inferences Based on a Single Sample: Tests of Hypothesis

The Elements of a Test of Hypothesis

Large-Sample Test of Hypothesis about a Population Mean

Observed Significance Levels: p-Values

Small-Sample Test of Hypothesis about a Population Mean

Large-Sample Test of Hypothesis about a Population Proportion

A Nonparametric Test about a Population Median (Optional)

7. Comparing Population Means

Comparing Two Population Means: Independent Sampling

Comparing Two Population Means: Paired Difference Experiments

Determining the Sample Size

Testing the Assumption of Equal Population Variances (Optional)

A Nonparametric Test for Comparing Two Populations: Independent Sampling (Optional)

A Nonparametric Test for Comparing Two Populations: Paired Differences Experiment (Optional)

Comparing Three or More Population Means: Analysis of Variance (Optional)

8. Comparing Population Proportions

Comparing Two Population Proportions: Independent Sampling

Determining the Sample Size

Comparing Population Proportions: Multinomial Experiment

Contingency Table Analysis

9. Simple Linear Regression

Probabilistic Models

Fitting the Model: The Least Squares Approach

Model Assumptions

An Estimator of ...s2

Assessing the Utility of the Model: Making Inferences about the Slope ...b1

The Coefficient of Correlation

The Coefficient of Determination

Using the Model for Estimation and Prediction

Simple Linear Regression: A Complete Example

A Nonparametric Test for Correlation (Optional)

10. Introduction to Multiple Regression

Multiple Regression Models

The First-Order Model: Estimating and Interpreting the ...b Parameters

Model Assumptions

Inferences about the ...b Parameters

Checking the Overall Utility of a Model

Using the Model for Estimation and Prediction

Residual Analysis: Checking the Regression Assumptions

Some Pitfalls: Estimability, Multicollinearity, and Extrapolation

11. Methods for Quality Improvement

Quality, Processes, and Systems

Statistical Control

The Logic of Control Charts

A Control Chart for Monitoring the Mean of a Process: The x-Chart

A Control Chart for Monitoring the Variation of a Process: The R-Chart

A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart

Appendix A: Basic Counting Rules

Appendix B: Tables

Appendix C: Calculation Formulas for Analysis of Variance: Independent Sampling

Answers to Selected Exercises

References Index

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