Lock R.H., Lock P.F., Morgan K.L., Lock E.F., Lock D.F. Statistics: Unlocking the Power of Data

Hoboken: Wiley, 2012. — 741 p. — ISBN: 978-0-470-60187-7.This 1st edition of Statistics moves the curriculum in innovative ways while still looking relatively familiar. Statistics, 1e utilizes intuitive methods to introduce the fundamental idea of statistical inference. These intuitive methods are enabled through statistical software and are accessible at very early stages of a course. The text also includes the more traditional methods such as t-tests, chi-square tests, etc., but only after students have developed a strong intuitive understanding of inference through randomization methods. The text is designed for use in a one-semester introductory statistics course. The focus throughout is on data analysis and the primary goal is to enable students to effectively collect data, analyze data, and interpret conclusions drawn from data. The text is driven by real data and real applications. Students completing the course should be able to accurately interpret statistical results and to analyze straightforward data sets.Data
Collecting Data
The Structure of Data
Sampling from a Population
Experiments and Observational Studies
Describing Data
Categorical Variables
One Quantitative Variable: Shape and Center
One Quantitative Variable: Measures of Spread
Outliers, Boxplots, and Quantitative/Categorical Relationships
Two Quantitative Variables: Scatterplot and Correlation
Two Quantitative Variables: Linear Regression
Essential Synthesis
Review Exercises
Projects
Understanding Inference
Confidence Intervals
Sampling Distributions
Understanding and Interpreting Confidence Intervals
Constructing Bootstrap Confidence Intervals
Bootstrap Confidence Intervals using Percentiles
Hypothesis Tests
Introducing Hypothesis Tests
Measuring Evidence with P-values
Determining Statistical Significance
Creating Randomization Distributions
Confidence Intervals and Hypothesis Tests
Essential Synthesis
Review Exercises
Projects
Inference with Normal and t-Distributions
Approximating with a Distribution
Normal Distributions
Confidence Intervals and P-values Using Normal Distributions
Inference for Means and Proportions
Distribution of a Sample Proportion
Confidence Interval for a Single Proportion
Test for a Single Proportion
Distribution of a Sample Mean
Confidence Interval for a Single Mean
Test for a Single Mean
Distribution of Differences in Proportions
Confidence Interval for a Difference in Proportions
Test for a Difference in Proportions
Distribution of Differences in Means
Confidence Interval for a Difference in Means
Test for a Difference in Means
Paired Difference in Means
Essential Synthesis
Review Exercises
Projects
Inference for Multiple Parameters
Chi-Square Tests for Categorical Variables
Testing Goodness-of-Fit for a Single Categorical Variable
Testing for an Association between Two Categorical Variables
ANOVA to Compare Means
Analysis of Variance
Pairwise Comparisons and Inference after ANOVA
Inference for Regression
Inference for Slope and Correlation
ANOVA for Regression
Confidence and Prediction Intervals
Multiple Regression
Multiple Predictors
Checking Conditions for a Regression Model
Using Multiple Regression
Essential Synthesis
Review Exercises
Projects
The Big Picture: Essential Synthesis
Exercises for the Big Picture: Essential Synthesis
Probability Basics
Probability Rules
Tree Diagrams and Bayes’ Rule
Random Variables and Probability Functions
Binomial Probabilities
Appendix A. Chapter Summaries
Appendix B. Selected Dataset Descriptions
Partial Answers