Introductory statistics: exploring the world through data/ (Record no. 176622)

MARC details
000 -LEADER
fixed length control field 00358nam a2200133Ia 4500
040 ## - CATALOGING SOURCE
Transcribing agency CUS
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.5
Item number ROB/I
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Gould, Robert
245 #0 - TITLE STATEMENT
Title Introductory statistics: exploring the world through data/
Statement of responsibility, etc. Robert Gould
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. Boston:
Name of publisher, distributor, etc. Pearson,
Date of publication, distribution, etc. 2013.
300 ## - PHYSICAL DESCRIPTION
Extent 736 p.
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note <br/>CASE STUDY • Deadly Cell Phones?<br/>1.1 What Are Data? 4<br/>1.2 Classifying and Storing Data<br/>1.3 Organizing Categorical Data<br/>1.4 Collecting Data To Understand Causality 14<br/>EXPLORING STATISTiCS > Collecting a Table of Different Kinds of Data 24<br/>Picturing Variation with Graphs 34<br/>CASE STUDY • Student-to-Teacher Ratio at Private and Public Colleges 35<br/>2.1 Visualizing Variation in Numerical Data 36<br/>2.2 Summarizing Important Features ofa Numerical Distribution 4l<br/>2.3 Visualizing Variation in Categorical Variables 49<br/>2.4 Summarizing Categorical Distributions 52<br/>2.5 Interpreting Graphs 55<br/>EXPLORING STATISTICS > Personal Distance 58<br/>Numerical Summaries of Center and Variation 76<br/>CASE STUDY • Living in a Risky World 77<br/>3.1 Summaries for Symmetric Distributions 78<br/>3.2 What's Unusual? The Empirical Rule and z-Scores 88<br/>3.3 Summaries for Skewed Distributions 93<br/>3.4 Comparing Measures of Center 100<br/>3.5 Using Boxplots for Displaying Summaries 104<br/>EXPLORING STATISTICS > Does Reaction Distance Depend on Gender? 110<br/>Regression Analysis: Exploring Associations<br/>between Variables 132<br/>CASE STUDY • Catching Meter Thieves 133<br/>4.1 Visualizing Variability with a Scatterplot 134<br/>4.2 Measuring Strength of Association with Correlation 138<br/>4.3 Modeling Linear Trends 146<br/>4.4 Evaluating the Linear Model 161<br/>EXPLORING STATISTICS Guessing the Age of Famous People <br/>CASE STUDY • SIDS or Murder? 195<br/>5.1 What is Randomness? 196<br/>5.2 Finding Theoretical Probabilities 200<br/>5.3 Associations in Categorical Variables 209<br/>5.4 Finding Empirical Probabilities with<br/>Simulations 220<br/>EXPLORING STATISTICS > "Let's Make a Deal": Stay or Switch? 228<br/>Modeling Random Events: The Normal and<br/>Binomial Models 242<br/>CASE STUDY • You Sometimes Get More Than You Pay For 243<br/>6.1 Probability Distributions Are Models of Random<br/>Experiments 244<br/>6.2 The Normal Model 249<br/>6.3 The Binomial Model 262<br/>EXPLORING STATISTICS > ESP with Coin Flipping 277<br/>CHAPTER y? Survey Sampling and Inference 294<br/>CHAPTER<br/>CASE STUDY • Spring Break Fever: Just What the Doctors Ordered? 295<br/>7.1 Learning about the World through Surveys 296<br/>7.2 Measuring the Quality of a Survey 302<br/>7.3 The Central Limit Theorem for Sample Proportions 311<br/>7.4 Estimating the Population Proportion with Confidence<br/>Intervals 317<br/>7.5 Margin of Error and Sample Size for Proportions 323<br/>EXPLORING STATISTICS Simple Random Sampling Prevents Bias 325<br/>Hypothesis Testing for Population Proportions 338<br/>CASE STUDY • Violence on TV 339<br/>8.1 The Main Ingredients of Hypothesis Testing 340<br/>8.2 Characterizing p-values 346<br/>8.3 Hypothesis Testing in Four Steps 349<br/>8.4 Comparing Proportions from Two Populations 358<br/>8.5 Understanding Hypothesis Testing 363<br/>EXPLORING STATISTICS > Identifying Flavors of Gum through Smell 370<br/>CHAPTER<br/>Inferring Population Means 386<br/>CASE STUDY • Epilepsy Drugs and Children 387<br/>9.1 Sample Means of Random Samples 388<br/>9.2 The Central Limit Theorem for Sample Means 392<br/>9.3 Answering Questions about the Mean ofa Population<br/>9.4 Comparing Two Population Means 411<br/>9.5 Overview of Analyzing Means 421<br/>EXPLORING STATISTICS !> Pulse Rates 425<br/>CHAPTER U) Associations between Categorical Variables 448<br/>CASE STUDY • Popping Better Popcorn 449<br/>10.1 The Basic Ingredients for Testing with Categorical Variables<br/>10.2 The Chi-Square Test for Goodness of Fit 460<br/>CONTENTS<br/>10.3 Chi-Square Tests for Associations between Categorical Variables<br/>10.4 Hypothesis Tests When Sample Sizes Are Small 473<br/>EXPLORING STATISTICS > Skittles 479<br/>Multiple Comparisons and Analysis of Variance 498<br/>CASE STUDY • Seeing Red 499<br/>11.1 Multiple Comparisons 500<br/>11.2 The Analysis of Variance 506<br/>11.3 The ANOVA Test 513<br/>11.4 Post-Hoc Procedures 518<br/>EXPLORING STATISTICS > Recovery Heart Rate 525<br/>Experimental Design: Controlling Variation 540<br/>CASE STUDY • Does Stretching Improve Athletic Performance? 541<br/>12.1 Variation Out of Control 542<br/>12.2 Controlling Variation in Surveys 550<br/>12.3 Reading Research Papers 555<br/>EXPLORING STATISTICS > Reporting on Research Articles 563<br/>Inference without NormaLIty 574<br/>CASE STUDY • Electric Rays 575<br/>13.1 Transforming Data 576<br/>13.2 The Sign Test for Paired Data 584<br/><br/>XII CONTENTS<br/>13.3 Mann-Whitney Test for Two Independent Groups 588<br/>13.4 Randomization Tests 593<br/>EXPLORING STATISTICS • Balancing on One Foot 600<br/>CHAPTER Inference for Regression 622<br/>CASE STUDY • Building a Better Oyster Shucker 623<br/>14.1 The Linear Regression Model 624<br/>14.2 Using the Linear Model 633<br/>14.3 Predicting Values and Estimating Means 641<br/>EXPLORING STATISTICS >• Older and Slower? 650<br/>
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        Central Library, Sikkim University Central Library, Sikkim University General Book Section 29/08/2016 519.5 ROB/I P31623 29/08/2016 General Books
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