Statistics for Managers Using Microsoft Excel

Statistics for Managers Using Microsoft Excel

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Designed for either one-semester or two-semester introductory business statistics course at the undergraduate or graduate level. This is the first business statistics text designed specifically to use Microsoft Excel as a means of teaching statistical business applications. Provides streamlined coverage of a range of statistical topics with a managerial focus. The UPDATED VERSION will contain: 1.) a new chapter on Decision Analysis (Ch. 14); 2.) Excel 97 (8.0) supplement - this supplement consists of four parts; 3.) A disk which contains fully developed Excel worksheets for all Excel examples/problems in the book. These are all ready-to-use and completely documented. PLUS, we have enhanced the text with 18 of the most commonly used worksheets which have been automated. This allows the user to solve examples/problems of any size all with the click of a button.show more

Product details

  • Mixed media product | 880 pages
  • 203.2 x 254 x 40.64mm | 1,814.36g
  • Pearson Education (US)
  • Upper Saddle River, United States
  • Updated ed.
  • 0130950718
  • 9780130950710

Table of contents

1. Introduction and Data Collection. Why a Manager Needs to Know About Statistics. The Growth and Development of Modern Statistics. Statistical Thinking and Modern Management. Descriptive vs. Inferential Statistics. Enumerative vs. Analytical Studies. The Need for Data. Data Sources. Measurement Scales. Types of Samples. Drawing the Simple Random Sample. Ethical and Other Issues in Survey Research. Introduction and Data Collection: A Review and Preview. Supplement Introduction to Using Microsoft Excel. Microsoft Excel Orientation. Getting Started with Microsoft Excel. 2. Presenting Data in Tables and Charts. Organizing Numerical Data: The Ordered Array and Stem-and-Leaf Display. Using Microsoft Excel to Sort Data into an Ordered Array. Tabulating Numerical Data: The Frequency Distribution. Tabulating Numerical Data: The Relative Frequency Distribution and Percentage Distribution. Graphing Numerical Data: Histograms and Polygons. Cumulative Distributions and Cumulative Polygons. Using Microsoft Excel to Obtain Tables and Charts for Numerical Variables. Organizing and Tabulating Categorical Data: The Summary Table. Graphing Categorical Data: Bar and Pie Charts. Graphing Categorial Data: The Pareto Diagram. Tabularizing Categorical Data Using Contingency Tables. Using Microsoft Excel to Obtain Tables and Charts for Categorical Variables. Proper Tabular and Chart Presentation and Ethical Issues. Data Presentation: A Review and Preview. 3. Summarizing and Describing Numerical Data. Exploring the Data. Properties of Numerical Data. Measures of Central Tendency. Using Microsoft Excel Functions to Obtain Measures of Central Tendency. Measures of Variation. Using Microsoft Excel Functions to Obtain Measures of Variation. Shape. Using the Data Analysis Tool to Obtain Descriptive Statistics. The Five-Number Summary and the Box-and-Wisker Plot. Simulating a Box-and-Whisker Plot Using Microsoft Excel. Calculating Descriptive Summary Measures from a Population. Obtaining the Population Standard Deviation and Variance from Microsoft Excel. Recognizing and Practicing Proper Descriptive Summarization and Exploring Ethical Issues. Summarizing and Describing Numerical Data: A Review. 4. Basic Probability and Discrete Probability Distributions. Objective and Subjective Probability. Basic Probability Concepts. Simple (Marginal) Probability. Joint Probability. Addition Rule. Conditional Probability. Multiplication Rule. Using Microsoft Excel to Obtain Probabilities. The Probability Distribution for a Discrete Random Variable. Mathematical Expectation and Expected Monetary Value. Using Microsoft Excel to Obtain Expected Values and Variances. Discrete Probability Distribution Functions. Binomial Distribution. Using Microsoft Excel to Obtain Binomial Probabilities. Other Discrete Probability Distributions. Using Microsoft Excel to Obtain Hypergeometric, Negative Binomial, and Poisson Probabilities. Ethical Issues and Probability. Basic Probability: A Review and Preview. 5. The Normal Distribution and Sampling Distributions. Models of Continuous Random Variables. The Normal Distribution. Applications. Using Microsoft Excel to Obtain Normal Probabilities. Asessing the Normality Assumption: Evaluating Properties and Constructing Probability Plots. Using Microsoft Excel to Obtain a Normal Probability Plot. Other Continuous Distributions. Using Microsoft Excel to Obtain Exponential Probabilities. Introduction to Sampling Distributions. Sampling Distribution of the Mean. Sampling Distribution of the Proportion. Using Microsoft Excel to Select Random Samples and Simulate Sampling Distributions. Sampling from Finite Populations. Continuous Distributions and Sampling Distributions: A Review. 6. Estimation. Confidence Interval Estimation of the Mean (...sx Known). Confidence Interval Estimation of the Mean (...sx Unknown). Estimation Through Bootstrapping. Confidence Interval Estimation for the Proportion. Sample Size Determination for the Mean. Sample Size Determination for a Proportion. Estimation and Sample Size Determination for Finite Populations. Applications of Estimation in Auditing. Using Microsoft Excel for Confidence Intervals and Sample Size Determination. Estimation, Sample Size Determination, and Ethical Issues. Estimation and Statistical Inference: Review and a Preview. 7. Fundamentals of Hypothesis Testing. Hypothesis-Testing Methodology. Z Test of Hypothesis for the Mean (...sx Known). Summarizing the Steps of Hypothesis Testing. The p-Value Approach to Hypothesis Testing: Two-Tailed Tests. A Connection Between Confidence Interval Estimation and Hypothesis Testing. One-Tailed Tests. The p-Value Approach to Hypothesis Testing: One-Tailed Tests. t Test of Hypothesis for the Mean (...sx Unknown). One Sample Z Test for the Proportion. Using Microsoft Excel for One-Sample Tests. Potential Hypothesis-Testing Pitfalls and Ethical Issues. Hypothesis-Testing Methodology: A Review and a Preview. 8. Two-Sample and C-Sample Tests with Numerical Data. Choosing the Appropriate Test Procedure. Pooled-Variance t Test for Differences in Two Means. Using Microsoft Excel for the Pooled- Variance t Test. Wilcoxon Rank Sum Test for the Difference Between Two Medians. Using Microsoft Excel for the Wilcoxon Rank Sum Test. F Test for Differences in Two Variances. Using Microsoft Excel for the F Test for Differences in Two Variances. One-Way ANOVA F Test for the Difference in c Means. Using Microsoft Excel for the One-Way ANOVA F Test. Using Microsoft Excel for the Tukey-Kramer Multiple Comparisons Procedure. Kruskal-Wallis Rank Test for the Difference in c Medians. Using Microsoft Excel for the Kruskal-Wallis Rank Test for the Difference in c Medians. Hypothesis-Testing Based on Two and c Samples of Numerical Data: A Review. 9. Two-Sample and C-Sample Tests with Categorical Data. Z Test for the Difference Between Two Proportions. Using Microsoft Excel for the Z Test for the Difference Between Two Proportions. ...c2 Test for Difference in Two Proportions. ...c2 Test for Difference in c Proportions. ...c2 Test of Independence. Using Microsoft Excel for ...c2 Tests. Potential Hypothesis-Testing Pitfalls and Exploring Ethical Issues. Hypothesis-Testing Based Categorical Data: A Review. 10. Statistical Applications in Quality and Productivity Management. Quality and Productivity: A Historical Perspective. The Theory of Control Charts. Some Tools for Studying a Processs: Fishbone (Ishikawa) and Process Flow Diagrams. Deming's Fourteen Points: A Theory of Management by Process. Control Charts for the Proportion and Number of Nonconforming Items-The p and np Charts. Using Microsoft Excel for p and np Charts. The Red Bead Experiment: Understanding Process Variability. Control Charts for the Mean X and the Range. Using Microsoft Excel for R and X Charts. 11. Simple Linear Regression and Correlation. The Scatter Diagram. Types of Regression Models. Determining the Simple Linear Regression Equation. Standard Error of the Estimate. Measures of Variation in Regression and Correlation. Using the Microsoft Excel Chart Wizard and TREND Function for Regression Analysis. Using the Data Analysis Tool for Regression. Correlation - Measuring the Strength of the Association. Using the Microsoft Excel CORREL Function for Correlation Analysis. Assumptions of Regression and Correlation. Residual Analysis. Using Microsoft Excel for Residual Analysis. Measuring Autocorrelation: The Dubrin-Watson Statistic. Using Microsoft Excel to Study Autocorrelation. Confidence Interval Estimate for Predicting ...mYX. Using Microsoft Excel to Obtain a Confidence Interval Estimate for ...mYX. Prediction Interval Estimate for an Individual Response YI. Using Microsoft Excel to Obtain a Prediction Interval Estimate for YI. Inferences about the Population Parameters in Regression. Pitfalls in Regression and Ethical Issues. 12. Multiple Regression. Developing the Multiple Regression Model. Prediction of the Department Variable Y for Given Values of the Explanatory Variable. Measuring Association in the Multiple Regression Model. Residual Analysis in Multiple Regression. Testing for the Significance of the Relationship Between the Dependent Variable and the Independent Variable. Testing Portions of a Multiple Regression Model. Inferences Concerning the Population Regression Coefficients. Coefficient of Partial Determination. The Curvilinear Regression Model. Dummy Variable Models. Other Types of Regression Models. Multcollinearity. Using Microsoft Excel for Multiple Regression. Pitfalls in Multiple Regression and Ethical Issues. 13. Time-Series Forecasting for Annual Data. The Importance of Business Forecasting. Component Factors of the Classical Multiplicative Time-Series Model. Smoothing the Annual Time Series: Moving Averages and Exponential Smoothing. Using Microsoft Excel for Moving Averages and Exponential Smoothing. Time-Series Analysis of Annual Data: Least Squares Trend Fitting and Forecasting. Using Microsoft Excel for Least Squares Trend Fitting. Autoregressive Modeling for Trend Fitting and Forecasting. Using Microsoft Excel for Autoregressive Modeling. Choosing an Appropriate Forecasting Model. Using Microsoft Excel to Obtain the Mean Absolute Deviation (MAD). Pitfalls Concerning Time-Series Analysis. 14. Decision Analysis. Answers to Selected Problems. Appendix A: Review of Arithmetic and Algebra. Appendix B: Summation Notation. Appendix C: Statistical Symbols and Greek Alphabet. Appendix D: Special Data Sets. Appendix E: Tables. Appendix F: Documentation for Diskette Files. Appendix G: Excel 97 (8.0).show more

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