Synopses & Reviews
Many statistics texts lack well-defined connections among materials presented, as if the different topics were disjointed. In this new text, George Woodbury successfully illustrates the natural connections between probability and inferential statistics and between confidence intervals and hypothesis testing, for example. Throughout the text, the author provides explanations that are easy to follow and examples that are concept-based.
Table of Contents
Unit 1: Descriptive Statistics. 1. Introduction to Statistics--Analyzing Data. An Introduction to Data Collection. Graphical Representation of Data. 2. Measures of Central Tendency and Dispersion. Measures of Central Tendency. Measures of Dispersion. Extra--Using Samples to Estimate a Population Mean. Unit 1 Summary. Unit 2: Probability. 3. Counting and Probability. Counting. Classical Probability. Table Probabilities, Conditional Probability. Independent Events, Multiplication Rule, Law of Large Numbers. Unit 2 Summary. Unit 3: Probability Distributions. 4. Discrete Probability Distributions. Probability Distributions. Binomial Probabilities. The Poisson Distribution. 5. Normal Probability Distributions. The Normal Distribution. Discrete Random Variables that are Approximately Normal; Normal Approximation to the Binomial Distribution. Finding Values for Given Probabilities. Unit 3 Summary. Unit 4: One-Sample Confidence Intervals and Hypothesis Tests. 6. The Central Limit Theorem and Confidence Intervals. The Sampling Distribution of the Sample Mean: The Central Limit Theorem. Confidence Intervals for a Population Mean (Large Samples). Confidence Intervals for a Population Mean (Small Samples). Confidence Intervals for a Population Proportion (Large Samples). 7. One-Sample Hypothesis Test. Hypothesis Test for a Single Population Mean (Large Samples). Hypothesis Test for a Single Population Mean (Small Samples). Hypothesis Test for a Single Population Proportion (Large Sample). Choosing the Appropriate Tool. Unit 4 Summary. Unit 5: Two-Sample Inferences, F-Tests, Chi-Square Tests. 8. Two-Sample Hypothesis Tests. Hypothesis Test for Two Population Means (Large Sample). Hypothesis Test for Two Population Means (Small Samples). Paired Difference Test for Dependent Samples. Hypothesis Test for Two Population Proportions (Large Samples). 9. Other Hypothesis Tests. Hypothesis Test for Two Population Variances. Analysis of Variance (ANOVA). The Goodness of Fit Test. Contingency Tables and the Hypothesis Test for Independence. Unit 5 Summary. Unit 6: Linear Correlation and Regression. 10. Linear Correlation and Regression. Correlation. Linear Regression. Inference for Regression. Unit 6 Summary. Unit 7: Nonparametric Tests. 11. Nonparametric Tests. Sign Test. Wilcoxon Signed-Rank Test. Wilcoxon Rank-Sum Test. Kruskal-Wallis Test. Rank Correlation. Unit 7 Summary. TABLES: A. Binomial Probabilities. B. Poisson Probabilities. C. Standard Normal Distribution. D. Students t-Distribution. E. Right-Tailed Critical Values for the F-Distribution. F. Right-Tailed Critical Values for the Chi-Square Distribution. G. Critical Values for the Wilcoxon Signed-Rank Test. H. Critical Values for Spearmans Rank Correlation Coefficient.