30.00$ - Purchase this E-book
Category : Higher Education
1: Introduction to Data 1.1 What Are Data? 1.2 Classifying and Storing Data 1.3 Investigating Data 1.4 Organizing Categorical Data 1.5 Collecting Data to Understand Causality 2: Picturing Variation with Graphs 2.1 Visualizing Variation in Numerical Data 2.2 Summarizing Important Features of a Numerical Distribution 2.3 Visualizing Variation in Categorical Variables 2.4 Summarizing Categorical Distributions 2.5 Interpreting Graphs 3: Numerical Summaries of Center and Variation 3.1 Summaries for Symmetric Distributions 3.2 What's Unusual? The Empirical Rule and z-Scores 3.3 Summaries for Skewed Distributions 3.4 Comparing Measures of Center 3.5 Using Boxplots for Displaying Summaries 4: Regression Analysis: Exploring Associations between Variables 4.1 Visualizing Variability with a Scatterplot 4.2 Measuring Strength of Association with Correlation 4.3 Modeling Linear Trends 4.4 Evaluating the Linear Model 5: Modeling Variation with Probability 5.1 What Is Randomness? 5.2 Finding Theoretical Probabilities 5.3 Associations in Categorical Variables 5.4 Finding Empirical Probabilities 6: Modeling Rando Events: The Normal and Binomial Models 6.1 Probability Distributions Are Models of Random Experiments 6.2 The Normal Model 6.3 The Binomial Model (Optional) 7: Survey Sampling and Inference 7.1 Learning about the World through Surveys 7.2 Measuring the Quality of a Survey 7.3 The Central Limit Theorem for Sample Proportions 7.4 Estimating the Population Proportion with Confidence Intervals 7.5 Comparing Two Population Proportions with Confidence 8: Hypothesis Testing for Population Proportions 8.1 The Essential Ingredients of Hypothesis Testing 8.2 Hypothesis Testing in Four Steps 8.3 Hypothesis Tests in Detail 8.4 Comparing Proportions from Two Populations 9: Inferring Population Means 9.1 Sample Means of Rando Samples 9.2 The Central Limit Theorem for Sample Means 9.3 Answering Questions about the Mean of a Population 9.4 Hypothesis Testing for Means 9.5 Comparing Two Population Means 9.6 Overview of Analyzing Means 10: Associations between Categorical Variables 10.1 The Basic Ingredients for Testing with Categorical Variables 10.2 The Chi-Square Test for Goodness of Fit 10.3 Chi-Square Tests for Associations between Categorical Variables 10.4 Hypothesis Tests When Sample Sizes Are Small 11: Multiple Comparisons and Analysis of Variance 11.1 Multiple Comparisons 11.2 The Analysis of Variance 11.3 The ANOVA Test 11.4 Post-Hoc Procedures 12: Experimental Design: Controlling Variation 12.1 Variation Out of Control 12.2 Controlling Variation in Surveys 12.3 Reading Research Papers 13: Inference without Normality 13.1 Transforming Data 13.2 The Sign Test for Paired Data 13.3 Mann-Whitney Test for Two Independent Groups 13.4 Randomization Tests 14: Inference for Regression 14.1 The Linear Regression Model 14.2 Using the Linear Model 14.3 Predicting Values and Estimating Means Table of Contents
Get Introductory Statistics: Exploring the World Through Data, 3rd Edition by Robert Gould, University of California, Los Angeles Colleen N. Ryan, California Lutheran University Rebecca Wong, West Valley College
0 commentaires:
Enregistrer un commentaire