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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: 978-0130186799For 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