Gerald Keller's new APPLIED STATISTICS WITH MICROSOFT(r) EXCEL integrates Excel into the general introductory statistics course. Keller, the co-author of the market-leading STATISTICS FOR MANAGEMENT AND ECONOMICS, Fifth Edition, incorporates his proven three-step problem-solving process throughout this book. The first step, "Identify," is the work a statistician does before the calculations are performed, which entails organizing the experiment, gathering the data, and deciding which statistical techniques to employ. The second step, "Compute," is the computation with Excel. In this step, Keller shows the manual calculation for the simplest of techniques only. For example, he describes how to calculate the sample mean, variance, and standard deviation, how to compute the z-interval estimate of, and the z-test of. The third step, "Interpret," is the interpretation of the computer output, which requires an understanding of statistical concepts.
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Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
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Höhe: 234 mm
Breite: 206 mm
Dicke: 36 mm
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ISBN-13
978-0-534-37112-8 (9780534371128)
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Schweitzer Klassifikation
1. INTRODUCTION TO STATISTICS Introduction / Key Statistical Concepts / Statistics and the Computer / World Wide Web and Learning Center / Appendix 1: An Introduction to Microsoft Excel 2. GRAPHICAL TECHNIQUES Introduction / Types of Data / Graphically Describing Interval Data: Frequency Distributions and Histograms / Graphically Describing Nominal Data: Bar and Pie Charts / Describing Time-Series Data: Line Charts / Describing the Relationship between Two Interval Variables: Scatter Diagram / Summary 3. NUMERICAL DESCRIPTIVE TECHNIQUES FOR INTERVAL DATA Introduction / Measures of Central Location / Measures of Variability / Other Measures of Shape / Measures of Relative Standing and Box Plots / Measures of Linear Relationship / General Guidelines for Exploring Data / Summary 4. PROBABILITY Introduction / Assigning Probability to Events / JOint, Marginal, and Conditional Distributions / Probability Rules and Trees / Summary 5. RANDOM VARIABLE AND DISCRETE PROBABILITY DISTRIBUTIONS Introduction / Random Variables and Probability Distributions / Describing the Population/Probability Distribution / Binomial Distribution / Poisson Distribution / Summary 6. CONTINUOUS PROBABILITY DISTRIBUTIONS Introduction / Continuous Probability Distributions / Normal Distribution / Other Continuous Distributions / Summary 7. DATA COLLECTION AND SAMPLING Sources of Data / Sampling / Sampling Plans / Errors Involved in Sampling / Summary 8. SAMPLING DISTRIBUTIONS Introduction / Sampling Distribution of Mean / Creating the Sampling Distribution by Computer Simulation / Sampling Distribution of a Proportion / Sampling Distribution of the Difference between Two Means / From Here to Inference / Summary 9. INTRODUCTION TO ESTIMATION Introduction / Concepts in Estimation / Estimating the Population Mean When the Population Standard Deviation is Known / Selecting the Sample Size / Simulation Experiments / Summary 10. INTRODUCTION TO HYPOTHESIS TESTING Introduction / Concepts of Hypothesis Testing / Testing the Population Mean When the Population Standard Deviation Is Known / Calculating the Probability of a Type II Error / The Road Ahead / Summary 11. INFERENCE ABOUT ONE POPULATION Introduction / Inference About a Population Mean When the Standard Deviation Is Unknown / Inference About a Population Variance / Inference About a Population Proportion / Summary 12. INFERENCE ABOUT TWO POPULATIONS Introduction / Inference about the Difference between Two Means: Independent Samples / Observational and Experimental Data / Inference About the Difference between Two Means: Matched Pairs Experiment / Inference About the Ratio of Two Variances / Inference About the Difference Between Two Population Proportions / Summary 13. REVIEW OF CHAPTERS 11 AND 12 Introduction / Guide to Identifying the Correct Technique: Chapters 11 and 12 14. ANALYSIS OF VARIANCE Introduction / Single-Factor (One-Way) Analysis of Variance: Independent Samples / Analysis of Variance Models / Single-Factor Analysis of Variance: Randomized Blocks / Two-Factor Analysis of Variance: Independent Samples / Multiple Comparisons / Bartlett's Test / Summary 15. CHI-SQUARED TESTS Introduction / Chi-Squared Goodness-of-Fit Test / Chi-Squared Test of a Contingency Table / Summary of Tests on Nominal Data / Chi-Squared Test for Normality / Summary 16. NONPARAMETRIC STATISTICS TECHNIQUES Introduction / Wilcoxon Rank Sum Test / Sign Test and Wilcoxon Signed Rank Sum Test / Kruskal-Wallis Test / Friedman Test / Choosing Between a Parametric and a Nonparametric Technique / Testing for Normality / Summary 17. SIMPLE LINEAR REGRESSION Introduction / Model / Estimating the Coefficients / Error Variable: Required Conditions / Assessing the Model / Using the Regression Equation / Coefficients of Correlation / Regression Diagnostics: I / Summary 18. MULTIPLE REGRESSION Introduction / Model and Required Conditions / Estimating the Coefficients and Assessing the Model / Regression Diagnostics: II / Regression Diagnostics: III (Time Series) / Nominal Independent Variables / Regression and the Analysis of Variance (Optional) / Stepwise Regression / Summary 19. STATISTICAL INFERENCE Introduction / Identifying the Correct Technique: Summary of Statistical Inference / The Last Word
