Statistics / Edition 7 available in Hardcover
- Pub. Date:
- Prentice Hall Professional Technical Reference
This hands-on introduction to statistics presents balanced coverage of both the theory and application of statistics, showing readers how to analyze data that appear in situations in the world around them. It features an abundance of examples and exercises--nearly all based on current, real-world applications pulled from journals, magazines, news articles, and commerce. TI-83 boxes throughout describe the keystrokes and output of TI-83 graphing calculator, where appropriate, and an Exploring Data with a Computer feature provides 5 large "real" datasets (available on disk and referenced in an Appendix) as the basis for statistical explorations using a statistical software package. Computer output from SPSS, Minitab, SAS, and ASP are integrated throughout.Statistics, Data, and Statistical Thinking. Methods for Describing Sets of Data. Probability. Discrete Random Variables. Continuous Random Variables. Sampling Distributions. Inferences Based on a Single Sample: Estimation with Confidence Intervals. Inferences Based on a Single Sample: Tests of Hypotheses. Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses. Analysis of Variance: Comparing More Than Two Means. Simple Linear Regression. Multiple Regression and Model Building. Categorical Data Analysis. Nonparametric Statistics.For anyone needing a contemporary, applications-oriented introduction to statistics.
|Publisher:||Prentice Hall Professional Technical Reference|
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About the Author
Dr. Jim McClave is currently President and CEO of Info Tech, Inc., a statistical consulting and software development firm with an international clientele. He is also currently an Adjunct Professor of Statistics at the University of Florida, where he was a full-time member of the faculty for twenty years.
Dr. Terry Sincich obtained his PhD in Statistics from the University of Florida in 1980. He is an Associate Professor in the Information Systems & Decision Sciences Department at the University of South Florida in Tampa. Dr. Sincich is responsible for teaching basic statistics to all undergraduates, as well as advanced statistics to all doctoral candidates, in the College of Business Administration. He has published articles in such journals as the Journal of the American Statistical Association, International Journal of Forecasting, Academy of Management Journal, and Auditing: A Journal of Practice & Theory. Dr. Sincich is a co-author of the texts Statistics, Statistics for Business & Economics, Statistics for Engineering & the Sciences, and A Second Course in Statistics: Regression Analysis.
Table of Contents1. Statistics, Data, and Statistical Thinking.
The Science of Statistics. Types of Statistical Applications.Fundamental Elements of Statistics. Types of Data. Collecting Data.The Role of Statistics in Critical Thinking.
2. Methods for Describing Sets of Data.
Describing Qualitative Data. Graphical Methods for DescribingQuantitative Data. Summation Notation. Numerical Measures of CentralTendency. Numerical Measures of Variability. Interpreting the StandardDeviation. Numerical Measures of Relative Standing. Methods for DetectingOutliers (Optional). Graphing Bivariate Relationships (Optional).Distorting the Truth with Descriptive Techniques.
Events, Sample Spaces, and Probability. Unions and Intersections.Complementary Events. The Additive Rule and Mutually Exclusive Events.Conditional Probability. Random Sampling. Some Counting Rules (Optional).
4. Discrete Random Variables.
Two Types of Random Variables. Probability Distributionsfor Discrete Random Variables. Expected Values of Discrete RandomVariables. The Binomial Random Variable. The Poisson Random Variable(Optional). The Hypergeometric Random Variable (Optional).
5. Continuous Random Variables.
Continuous Probability Distributions . The Uniform Distribution.The Normal Distribution. Descriptive Methods for Assessing Normality.The Exponential Distribution (Optional).
6. Sampling Distributions.
What Is a Sampling Distribution? Properties of SamplingDistributions: Unbiasedness and Minimum Variance. The Central LimitTheorem.
7. Inferences Based on a Single Sample: Estimation withConfidence Intervals.
Large-Sample Confidence Interval for a Population Mean.Small-Sample Confidence Interval for a Population Mean. Large-SampleConfidence Interval for a Population Proportion. Determining the SampleSize.
8. Inferences Based on a Single Sample: Tests of Hypotheses.
The Elements of a Test of Hypothesis. Large-Sample Testof Hypothesis About a Population Mean. Observed Significance Levels:p-Values. Small-Sample Test of Hypothesis About a PopulationMean. Large-Sample Test of Hypothesis About a Population Proportion.Calculating Type II Error Probabilities: More About b (Optional).Inferences About a Population Variance (Optional).
9. Inferences Based on Two Samples: Confidence Intervalsand Tests of Hypotheses.
Comparing Two Population Means: Independent Sampling. ComparingTwo Population Means: Paired Difference Experiments. Comparing TwoPopulation Proportions: Independent Sampling. Determining the SampleSize. Comparing Two Population Variances: Independent Sampling (Optional).
10. Analysis of Variance: Comparing More Than Two Means.
Elements of a Designed Experiment. The Completely RandomizedDesign. Multiple Comparisons of Means. The Randomized Block Design.Factorial Experiments. Checking ANOVA Assumptions.
11. Simple Linear Regression.
Probabalistic Models. Fitting the Model: The Least SquaresApproach. Model Assumptions. An Estimator of s2. Assessingthe 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. Residual Analysis:Checking the Regression Assumptions. Simple Linear Regression: AnExample.
12. Multiple Regression and Model Building.
Multiple Regression Models. Fitting the Model: The LeastSquares Approach. Model Assumptions. Inferences About the Individual b Parameters. Checking the Overall Utility of a Model. Using theModel for Estimation and Prediction. Multiple Regression: An Example.Model Building: Interaction Models. Model Building: Second-Order (Quadratic)Models. Model Building: Qualitative (Dummy) Variable Models. ModelBuilding: Comparing Nested Models. Some Pitfalls: Estimability, Multicollinearity,and Extrapolation. Stepwise Regression.
13. The Chi-Square Test and the Analysis of ContingencyTables.
One-Dimensional Count Data: The Multinomial Distribution.Contingency Tables. A Word of Caution About Chi-Square Tests.
14. Nonparametric Statistics.
Single Population Inferences: The Sign Test. Comparing TwoPopulations: The Wilcoxon Rank Sum Test for Independent Samples. ComparingTwo Populations: The Wilcoxon Signed Rank Test for the Paired DifferenceExperiment. The Kruskal-Wallis H-Test for a Completely RandomizedDesign. The Friedman F
Appendix A. Tables.
Random Numbers. Binomial Probabilities. Poisson Probabilities.Normal Curve Areas. Exponentials. Critical Values of t. Critical valuesof c2. Percentage Points of the F Distribution, a= .10.Percentage Points of the F Distribution, a=.05. Percentage Pointsof the F Distribution, a=.025. Percentage Points of the F Distribution, a=.01. Critical Values of T
Appendix B. Data Sets.
Coronary Artery Patients' Blood Loss Data. Car & DriverData. Starting Salaries of USF Graduates. Sealed Milk Bids Data. FederalTrade Commission Rankings of Domestic Cigarette Brands.
Appendix C. Calculation Formulas for Analysis of Variance.
Short Answers to Selected Odd-Numbered Exercises.