{"product_id":"student-solutions-manual-for-introductory-statistics-9780135189238","title":"Student Solutions Manual for Introductory","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp style=\"margin:0px;\"\u003e\u003cb\u003eRobert L. Gould \u003c\/b\u003e(Ph.D., University of CaliforniaSan Diego) is a leader in the statistics education community. He has served as chair of the AMATYC\/ASA joint committee, was co-leader of the Two-Year College Data Science Summit hosted by the American Statistical Association, served as chair of the ASA's Statistics Education Section, and was a co-author of the 2005 \u003ci\u003eGuidelines for Assessment in Instruction on Statistics Education (GAISE) College Report\u003c\/i\u003e. While serving as the Associate Director of Professional Development for CAUSE (Consortium for the Advancement of Undergraduate Statistics Education), he worked closely with the American Mathematical Association of Two-Year Colleges (AMATYC) to provide traveling workshops and summer institutes in statistics. He was the lead principal investigator of the NSF-funded Mobilize Project, which developed and implemented the first high-school level data science course. For over twenty years, he has served as Vice-C\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction to Data  \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e1.1 What Are Data?\u003c\/li\u003e\n\u003cli\u003e1.2 Classifying and Storing Data\u003c\/li\u003e\n\u003cli\u003e1.3 Investigating Data\u003c\/li\u003e\n\u003cli\u003e1.4 Organizing Categorical Data\u003c\/li\u003e\n\u003cli\u003e1.5 Collecting Data to Understand Causality\u003c\/li\u003e\n\u003c\/ul\u003e  2. Picturing Variation with Graphs  \u003cul\u003e\n\u003cli\u003e2.1 Visualizing Variation in Numerical Data\u003c\/li\u003e\n\u003cli\u003e2.2 Summarizing Important Features of a Numerical Distribution\u003c\/li\u003e\n\u003cli\u003e2.3 Visualizing Variation in Categorical Variables\u003c\/li\u003e\n\u003cli\u003e2.4 Summarizing Categorical Distributions\u003c\/li\u003e\n\u003cli\u003e2.5 Interpreting Graphs\u003c\/li\u003e\n\u003c\/ul\u003e  3. Numerical Summaries of Center and Variation  \u003cul\u003e\n\u003cli\u003e3.1 Summaries for Symmetric Distributions\u003c\/li\u003e\n\u003cli\u003e3.2 What's Unusual? The Empirical Rule and z-Scores\u003c\/li\u003e\n\u003cli\u003e3.3 Summaries for Skewed Distributions\u003c\/li\u003e\n\u003cli\u003e3.4 Comparing Measures of Center\u003c\/li\u003e\n\u003cli\u003e3.5 Using Boxplots for Displaying Summaries\u0026lt;\u003c\/li\u003e\n\u003c\/ul\u003e  4. Regression Analysis: Exploring Associations between Variables  \u003cul\u003e\n\u003cli\u003e4.1 Visualizing Variability with a Scatterplot\u003c\/li\u003e\n\u003cli\u003e4.2 Measuring Strength of Association with Correlation\u003c\/li\u003e\n\u003cli\u003e4.3 Modeling Linear Trends\u003c\/li\u003e\n\u003cli\u003e4.4 Evaluating the Linear Model\u003c\/li\u003e\n\u003c\/ul\u003e  5. Modeling Variation with Probability  \u003cul\u003e\n\u003cli\u003e5.1 What Is Randomness?\u003c\/li\u003e\n\u003cli\u003e5.2 Finding Theoretical Probabilities\u003c\/li\u003e\n\u003cli\u003e5.3 Associations in Categorical Variables\u003c\/li\u003e\n\u003cli\u003e5.4 Finding Empirical Probabilities\u003c\/li\u003e\n\u003c\/ul\u003e  6. Modeling Rando Events: The Normal and Binomial Models  \u003cul\u003e\n\u003cli\u003e6.1 Probability Distributions Are Models of Random Experiments\u003c\/li\u003e\n\u003cli\u003e6.2 The Normal Model\u003c\/li\u003e\n\u003cli\u003e6.3 The Binomial Model (Optional)\u003c\/li\u003e\n\u003c\/ul\u003e  7. Survey Sampling and Inference  \u003cul\u003e\n\u003cli\u003e7.1 Learning about the World through Surveys\u003c\/li\u003e\n\u003cli\u003e7.2 Measuring the Quality of a Survey\u003c\/li\u003e\n\u003cli\u003e7.3 The Central Limit Theorem for Sample Proportions\u003c\/li\u003e\n\u003cli\u003e7.4 Estimating the Population Proportion with Confidence Intervals\u003c\/li\u003e\n\u003cli\u003e7.5 Comparing Two Population Proportions with Confidence\u003c\/li\u003e\n\u003c\/ul\u003e  8. Hypothesis Testing for Population Proportions  \u003cul\u003e\n\u003cli\u003e8.1 The Essential Ingredients of Hypothesis Testing\u003c\/li\u003e\n\u003cli\u003e8.2 Hypothesis Testing in Four Steps\u003c\/li\u003e\n\u003cli\u003e8.3 Hypothesis Tests in Detail\u003c\/li\u003e\n\u003cli\u003e8.4 Comparing Proportions from Two Populations\u003c\/li\u003e\n\u003c\/ul\u003e  9. Inferring Population Means  \u003cul\u003e\n\u003cli\u003e9.1 Sample Means of Rando Samples\u003c\/li\u003e\n\u003cli\u003e9.2 The Central Limit Theorem for Sample Means\u003c\/li\u003e\n\u003cli\u003e9.3 Answering Questions about the Mean of a Population\u003c\/li\u003e\n\u003cli\u003e9.4 Hypothesis Testing for Means\u003c\/li\u003e\n\u003cli\u003e9.5 Comparing Two Population Means\u003c\/li\u003e\n\u003cli\u003e9.6 Overview of Analyzing Means\u003c\/li\u003e\n\u003c\/ul\u003e  10. Associations between Categorical Variables  \u003cul\u003e\n\u003cli\u003e10.1 The Basic Ingredients for Testing with Categorical Variables\u003c\/li\u003e\n\u003cli\u003e10.2 The Chi-Square Test for Goodness of Fit\u003c\/li\u003e\n\u003cli\u003e10.3 Chi-Square Tests for Associations between Categorical Variables\u003c\/li\u003e\n\u003cli\u003e10.4 Hypothesis Tests When Sample Sizes Are Small\u003c\/li\u003e\n\u003c\/ul\u003e  11. Multiple Comparisons and Analysis of Variance  \u003cul\u003e\n\u003cli\u003e11.1 Multiple Comparisons\u003c\/li\u003e\n\u003cli\u003e11.2 The Analysis of Variance\u003c\/li\u003e\n\u003cli\u003e11.3 The ANOVA Test\u003c\/li\u003e\n\u003cli\u003e11.4 Post-Hoc Procedures\u003c\/li\u003e\n\u003c\/ul\u003e  12. Experimental Design: Controlling Variation  \u003cul\u003e\n\u003cli\u003e12.1 Variation Out of Control\u003c\/li\u003e\n\u003cli\u003e12.2 Controlling Variation in Surveys\u003c\/li\u003e\n\u003cli\u003e12.3 Reading Research Papers\u003c\/li\u003e\n\u003c\/ul\u003e  13. Inference without Normality  \u003cul\u003e\n\u003cli\u003e13.1 Transforming Data\u003c\/li\u003e\n\u003cli\u003e13.2 The Sign Test for Paired Data\u003c\/li\u003e\n\u003cli\u003e13.3 Mann-Whitney Test for Two Independent Groups\u003c\/li\u003e\n\u003cli\u003e13.4 Randomization Tests\u003c\/li\u003e\n\u003c\/ul\u003e  14. Inference for Regression  \u003cul\u003e\n\u003cli\u003e14.1 The Linear Regression Model\u003c\/li\u003e\n\u003cli\u003e14.2 Using the Linear Model\u003c\/li\u003e\n\u003cli\u003e14.3 Predicting Values and Estimating Means\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":49524408648023,"sku":"9780135189238","price":58.36,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780135189238.jpg?v=1731856662","url":"https:\/\/bookcurl.com\/products\/student-solutions-manual-for-introductory-statistics-9780135189238","provider":"Book Curl","version":"1.0","type":"link"}