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"Designed for the one-term MBA or undergraduate introduction to business statistics course, this text places emphasis on data and the common techniques and methods used to analyze them in business. It introduces concepts using practical examples and illustrates them with computer output from MINITAB", Microsoft½ Excel, and JMP. The book integrates a business decision-making case into each chapter for motivational and illustration purposes and includes a business case assignment at the end of each chapter. These cases revolve around realistic business settings with realistic data sets that put students in the role of managers who need to make business decisions based on data. Review problems requiring students to use previously learned concepts also appear throughout to promote understanding of the relationships among statistical methods."
1. MAKING SENSE OF DATA.
Executive Overview. What Do We Mean by "Data?" Data About What? Gathering Data. Summarizing Data. The Role of Probability. Evaluating Other People's Data and Conclusions. The Role of the Computer. Summary.
2.SUMMARIZING DATA.
Executive Overview. Chapter Case Introduction. The Distribution of Values of a Variable. Two-Variable Summaries. On the Average: Typical Values. Measuring Variability. Calculators and Statistical Software. Statistical Methods and Quality Improvement. Chapter Case Analyses. Summary. Supplementary Exercises. Business Cases.
3. A FIRST LOOK AT PROBABILITY.
Executive Overview. Chapter Case Introduction. Basic Principles of Probability. Statistical Independence. Probability Tables, Trees, and Simulations. Chapter Case Analysis. Summary. Supplementary Exercises. Business Cases. Review Exercises: Chapters 2 and 3.
4. RANDOM VARIABLES AND PROBABILITY DISTRIBUTION.
Executive Overview. Chapter Case Introduction. Random Variables: Basic Ideas. Probability Distributions: Discrete Random Variables. Expected Value, Variance, and Standard Deviation. Joint Probability Distributions and Independence. Covariance and Correlation of Random Variables. Chapter Case Analysis. Summary. Supplementary Exercises. Business Cases.
5. SOME SPECIAL PROBABILITY DISTRIBUTIONS.
Executive Overview. Chapter Case Introduction. Counting Possible Outcomes. Bernoulli Trials and the Binomial Distribution. The Poisson Distribution. The Normal Distribution. Checking Normality. Chapter Case Analysis. Summary. Supplementary Exercises. Business Cases.
6. RANDOM SAMPLING AND SAMPLING DISTRIBUTIONS.
Executive Overview. Chapter Case Introduction. Random Sampling. Sample Statistics and Sampling Distributions. Sampling Distributions for Means and Sums. Chapter Case Analysis. Summary. Appendix: Standard Error of a Mean. Supplementary Exercises. Business Cases. Review Exercises: Chapters 4-6.
7. POINT INTERVAL ESTIMATION.
Executive Overview. Chapter Case Introduction. Point Estimators. Interval Estimation of a Mean, Known Standard Deviation. Confidence Intervals for a Proportion. How Large a Sample is Needed? The t Distribution. Confidence Intervals with the t Distribution. Assumptions for Interval Estimation. Chapter Case Analysis. Summary. Supplementary Exercises. Business Case.
8. HYPOTHESIS TESTING.
Executive Overview. Chapter Case Introduction. A Test for a Mean, Known Standard Deviation. Type II Error, ß Probability, and Power of a Test. The p-Value for a Hypothesis Test. Hypothesis Testing with the t Distribution. Assumptions for t Tests. Testing a Proportion: Normal Approximation. Hypothesis Tests and Confidence Intervals. Chapter Case Analysis. Summary. Supplementary Exercises. Business Case. Review Exercises: Chapters 7-8.
9. COMPARING TWO SAMPLES.
Executive Overview. Chapter Case Introduction. Comparing the Means of Two Populations. A Nonparametric Test: The Wilcoxon Rank Sum Test. Paired-Sample Methods. The Signed-Rank Method. Two-Sample Procedures for Proportions. Chi-Squared Test for Count Data. Chapter Case Analysis. Summary. Supplementary Exercises. Business Cases.
10. ANALYSIS OF VARIANCE AND DESIGNED EXPERIMENTS.
Executive Overview / Chapter Case Introduction / Testing the Equality of Several Population Means / Comparing Several Distributions by a Rank Test. Specific Comparisons Among Means / Two-Factor Experiments / Randomized Block Designs / Chapter Case Analysis / Summary / Supplementary Exercises / Business Cases.
11.LINEAR REGRESSION AND CORRELATION METHODS.
Executive Overview. Chapter Case Introduction. The Linear Regression Model. Estimating Model Parameters. Inferences about Regression Parameters. Predicting New Y Values using Regression. Correlation. Chapter Case Analysis. Summing Up. Supplementary Exercises. Business Cases.
12. MULTIPLE REGRESSION METHODS.
Executive Overview. Chapter Case Introduction. The Multiple Regression Model. Estimating Multiple Regression Coefficients. Inferences in Multiple Regression. Testing a Subset of the Regression Coefficients. Forecasting Using Multiple Regression. Chapter Case Analysis. Summing Up. Supplementary Exercises. Business Cases.
13. CONSTRUCTING A MULTIPLE REGRESSION MODEL.
Executive Overview. Chapter Case Introduction. Chapter Case Analysis. Summary. Supplementary Exercises. Business Cases.
"Designed for the one-term MBA or undergraduate introduction to business statistics course, this text places emphasis on data and the common techniques and methods used to analyze them in business. It introduces concepts using practical examples and illustrates them with computer output from MINITAB", Microsoft½ Excel, and JMP. The book integrates a business decision-making case into each chapter for motivational and illustration purposes and includes a business case assignment at the end of each chapter. These cases revolve around realistic business settings with realistic data sets that put students in the role of managers who need to make business decisions based on data. Review problems requiring students to use previously learned concepts also appear throughout to promote understanding of the relationships among statistical methods."
Table of Contents
1. MAKING SENSE OF DATA.
Executive Overview. What Do We Mean by "Data?" Data About What? Gathering Data. Summarizing Data. The Role of Probability. Evaluating Other People's Data and Conclusions. The Role of the Computer. Summary.
2.SUMMARIZING DATA.
Executive Overview. Chapter Case Introduction. The Distribution of Values of a Variable. Two-Variable Summaries. On the Average: Typical Values. Measuring Variability. Calculators and Statistical Software. Statistical Methods and Quality Improvement. Chapter Case Analyses. Summary. Supplementary Exercises. Business Cases.
3. A FIRST LOOK AT PROBABILITY.
Executive Overview. Chapter Case Introduction. Basic Principles of Probability. Statistical Independence. Probability Tables, Trees, and Simulations. Chapter Case Analysis. Summary. Supplementary Exercises. Business Cases. Review Exercises: Chapters 2 and 3.
4. RANDOM VARIABLES AND PROBABILITY DISTRIBUTION.
Executive Overview. Chapter Case Introduction. Random Variables: Basic Ideas. Probability Distributions: Discrete Random Variables. Expected Value, Variance, and Standard Deviation. Joint Probability Distributions and Independence. Covariance and Correlation of Random Variables. Chapter Case Analysis. Summary. Supplementary Exercises. Business Cases.
5. SOME SPECIAL PROBABILITY DISTRIBUTIONS.
Executive Overview. Chapter Case Introduction. Counting Possible Outcomes. Bernoulli Trials and the Binomial Distribution. The Poisson Distribution. The Normal Distribution. Checking Normality. Chapter Case Analysis. Summary. Supplementary Exercises. Business Cases.
6. RANDOM SAMPLING AND SAMPLING DISTRIBUTIONS.
Executive Overview. Chapter Case Introduction. Random Sampling. Sample Statistics and Sampling Distributions. Sampling Distributions for Means and Sums. Chapter Case Analysis. Summary. Appendix: Standard Error of a Mean. Supplementary Exercises. Business Cases. Review Exercises: Chapters 4-6.
7. POINT INTERVAL ESTIMATION.
Executive Overview. Chapter Case Introduction. Point Estimators. Interval Estimation of a Mean, Known Standard Deviation. Confidence Intervals for a Proportion. How Large a Sample is Needed? The t Distribution. Confidence Intervals with the t Distribution. Assumptions for Interval Estimation. Chapter Case Analysis. Summary. Supplementary Exercises. Business Case.
8. HYPOTHESIS TESTING.
Executive Overview. Chapter Case Introduction. A Test for a Mean, Known Standard Deviation. Type II Error, ß Probability, and Power of a Test. The p-Value for a Hypothesis Test. Hypothesis Testing with the t Distribution. Assumptions for t Tests. Testing a Proportion: Normal Approximation. Hypothesis Tests and Confidence Intervals. Chapter Case Analysis. Summary. Supplementary Exercises. Business Case. Review Exercises: Chapters 7-8.
9. COMPARING TWO SAMPLES.
Executive Overview. Chapter Case Introduction. Comparing the Means of Two Populations. A Nonparametric Test: The Wilcoxon Rank Sum Test. Paired-Sample Methods. The Signed-Rank Method. Two-Sample Procedures for Proportions. Chi-Squared Test for Count Data. Chapter Case Analysis. Summary. Supplementary Exercises. Business Cases.
10. ANALYSIS OF VARIANCE AND DESIGNED EXPERIMENTS.
Executive Overview / Chapter Case Introduction / Testing the Equality of Several Population Means / Comparing Several Distributions by a Rank Test. Specific Comparisons Among Means / Two-Factor Experiments / Randomized Block Designs / Chapter Case Analysis / Summary / Supplementary Exercises / Business Cases.
11.LINEAR REGRESSION AND CORRELATION METHODS.
Executive Overview. Chapter Case Introduction. The Linear Regression Model. Estimating Model Parameters. Inferences about Regression Parameters. Predicting New Y Values using Regression. Correlation. Chapter Case Analysis. Summing Up. Supplementary Exercises. Business Cases.
12. MULTIPLE REGRESSION METHODS.
Executive Overview. Chapter Case Introduction. The Multiple Regression Model. Estimating Multiple Regression Coefficients. Inferences in Multiple Regression. Testing a Subset of the Regression Coefficients. Forecasting Using Multiple Regression. Chapter Case Analysis. Summing Up. Supplementary Exercises. Business Cases.
13. CONSTRUCTING A MULTIPLE REGRESSION MODEL.
Executive Overview. Chapter Case Introduction. Chapter Case Analysis. Summary. Supplementary Exercises. Business Cases.