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Full Description
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.
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.
