Essential Statistics

ISBN-10: 0321836987

ISBN-13: 9780321836984

Edition: 2014

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Description: We live in a data-driven world, and this is a book about understanding and working with that data. In order to be informed citizens, authors Rob Gould and Colleen Ryan believe that learning statistics extends beyond the classroom to an essential life skill. They teach students of all math backgrounds how to think about data, how to reason using data, and how to make decisions based on data.

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Book details

List price: $155.33
Copyright year: 2014
Publisher: Pearson Education
Publication date: 12/21/2012
Binding: Mixed Media
Pages: 552
Size: 8.50" wide x 11.00" long x 0.75" tall
Weight: 2.486
Language: English

Robert L. Gould (PhD, University of California San Diego) is a leader in the statistics education community. He has served as a chair of the American Statistical Association's (ASA) Committee on Teacher Enhancement, chair of the ASA's Statistics Education Section, and was a co-author on the Guidelines for Assessment in Instruction on Statistics Education (GAISE) College Report. For more than ten years, he has served as Vice-Chair of Undergraduate Studies at the UCLA Department of Statistics, and is the Director of the UCLA Center for Teaching Statistics.Colleen Ryan has taught statistics to diverse community college students for more than three decades, having served at Oxnard College from 1975 to 2006, where she earned the Teacher of the Year Award in her final year. She currently teaches at California Lutheran University.

Introduction to Data
What Are Data?
Classifying and Storing Data
Organizing Categorical Data
Collecting Data to Understand Causality
Picturing Variation with Graphs
Visualizing Variation in Numerical Data
Summarizing Important Features of a Numerical Distribution
Visualizing Variation in Categorical Variables
Summarizing Categorical Distributions
Interpreting Graphs
Numerical Summaries of Center and Variation
Summaries for Symmetric Distributions
What's Unusual? The Empirical Rule and z-Scores
Summaries for Skewed Distributions
Comparing Measures of Center
Using Boxplots for Displaying Summaries
Regression Analysis: Exploring Associations between Variables
Visualizing Variability with a Scatterplot
Measuring Strength of Association with Correlation
Modeling Linear Trends
Evaluating the Linear Model
Modeling Variation with Probability
What is Randomness?
Finding Theoretical Probabilities
Associations in Categorical Variables
The Law of Large Numbers
Modeling Random Events: The Normal and Binomial Models
Probability Distributions Are Models of Random Experiments
The Normal Model
The Binomial Model
Survey Sampling and Inference
Learning about the World through Surveys
Measuring the Quality of a Survey
The Central Limit Theorem for Sample Proportions
Estimating the Population Proportion with Confidence Intervals
Hypothesis Testing for Population Proportions
The Main Ingredients of Hypothesis Testing
Characterizing p-values
Hypothesis Testing in Four Steps
Comparing Proportions from Two Populations
Understanding Hypothesis Testing
Inferring Population Means
Sample Means of Random Samples
The Central Limit Theorem for Sample Means
Answering Questions about the Mean of a Population
Comparing Two Population Means
Overview of Analyzing Means
Associations between Categorical Variables
The Basic Ingredients for Testing with Categorical Variables
Chi-Square Tests for Associations between Categorical Variables
Reading Research Papers
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