# Introduction to the Practice of Statistics

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## Efnisyfirlit

• Title
• Brief Contents
• Contents
• To Students: What Is Statistics?
• Data Table Index
• Beyond the Basics Index
• CHAPTER 1 Looking at Data—Distributions
• Introduction
• 1.1 Data
• Key characteristics of a data set
• Section 1.1 Summary
• Section 1.1 Exercises
• 1.2 Displaying Distributions with Graphs
• Categorical variables: Bar graphs and pie charts
• Quantitative variables: Stemplots and histograms
• Histograms
• Data analysis in action: Don’t hang up on me
• Examining distributions
• Dealing with outliers
• Time plots
• Section 1.2 Summary
• Section 1.2 Exercises
• 1.3 Describing Distributions with Numbers
• Measuring center: The mean
• Measuring center: The median
• Mean versus median
• The five-number summary and boxplots
• The 1.5 × IQR rule for suspected outliers
• Measuring spread: The standard deviation
• Properties of the standard deviation
• Choosing measures of center and spread
• Changing the unit of measurement
• Section 1.3 Summary
• Section 1.3 Exercises
• 1.4 Density Curves and Normal Distributions
• Density curves
• Measuring center and spread for density curves
• Normal distributions
• The 68–95–99.7 rule
• Standardizing observations
• Normal distribution calculations
• Using the standard Normal table
• Inverse Normal calculations
• Normal quantile plots
• Beyond the Basics: Density estimation
• Section 1.4 Summary
• Section 1.4 Exercises
• Chapter 1 Exercises
• CHAPTER 2 Looking at Data—Relationships
• Introduction
• 2.1 Relationships
• Examining relationships
• Section 2.1 Summary
• Section 2.1 Exercises
• 2.2 Scatterplots
• Interpreting scatterplots
• The log transformation
• Adding categorical variables to scatterplots
• Scatterplot smoothers
• Categorical explanatory variables
• Section 2.2 Summary
• Section 2.2 Exercises
• 2.3 Correlation
• The correlation r
• Properties of correlation
• Section 2.3 Summary
• Section 2.3 Exercises
• 2.4 Least-Squares Regression
• Fitting a line to data
• Prediction
• Least-squares regression
• Interpreting the regression line
• Correlation and regression
• Another view of r2
• Section 2.4 Summary
• Section 2.4 Exercises
• 2.5 Cautions about Correlation and Regression
• Residuals
• Outliers and influential observations
• Beware of the lurking variable
• Beware of correlations based on averaged data
• Beware of restricted ranges
• Beyond the Basics: Data mining
• Section 2.5 Summary
• Section 2.5 Exercises
• 2.6 Data Analysis for Two-Way Tables
• The two-way table
• Joint distribution
• Marginal distributions
• Describing relations in two-way tables
• Conditional distributions
• Section 2.6 Summary
• Section 2.6 Exercises
• 2.7 The Question of Causation
• Explaining association
• Establishing causation
• Section 2.7 Summary
• Section 2.7 Exercises
• Chapter 2 Exercises
• CHAPTER 3 Producing Data
• Introduction
• 3.1 Sources of Data
• Anecdotal data
• Available data
• Sample surveys and experiments
• Section 3.1 Summary
• Section 3.1 Exercises
• 3.2 Design of Experiments
• Comparative experiments
• Randomization
• Randomized comparative experiments
• How to randomize
• Randomization using software
• Randomization using random digits
• Matched pairs designs
• Block designs
• Section 3.2 Summary
• Section 3.2 Exercises
• 3.3 Sampling Design
• Simple random samples
• How to select a simple random sample
• Stratified random samples
• Multistage random samples
• Beyond the Basics: Capture-recapture sampling
• Section 3.3 Summary
• Section 3.3 Exercises
• 3.4 Ethics
• Institutional review boards
• Informed consent
• Confidentiality
• Clinical trials
• Behavioral and social science experiments
• Section 3.4 Summary
• Section 3.4 Exercises
• Chapter 3 Exercises
• CHAPTER 4 Probability: The Study of Randomness
• Introduction
• 4.1 Randomness
• The language of probability
• The uses of probability
• Section 4.1 Summary
• Section 4.1 Exercises
• 4.2 Probability Models
• Sample spaces
• Probability rules
• Assigning probabilities: Finite number of outcomes
• Assigning probabilities: Equally likely outcomes
• Independence and the multiplication rule
• Applying the probability rules
• Section 4.2 Summary
• Section 4.2 Exercises
• 4.3 Random Variables
• Discrete random variables
• Continuous random variables
• Normal distributions as probability distributions
• Section 4.3 Summary
• Section 4.3 Exercises
• 4.4 Means and Variances of Random Variables
• The mean of a random variable
• Statistical estimation and the law of large numbers
• Thinking about the law of large numbers
• Beyond the Basics: More laws of large numbers
• Rules for means
• The variance of a random variable
• Rules for variances and standard deviations
• Section 4.4 Summary
• Section 4.4 Exercises
• 4.5 General Probability Rules
• Conditional probability
• General multiplication rules
• Tree diagrams
• Bayes’s rule
• Independence again
• Section 4.5 Summary
• Section 4.5 Exercises
• Chapter 4 Exercises
• CHAPTER 5 Sampling Distributions
• Introduction
• 5.1 Toward Statistical Inference
• Sampling variability
• Sampling distributions
• Bias and variability
• Sampling from large populations
• Why randomize?
• Section 5.1 Summary
• Section 5.1 Exercises
• 5.2 The Sampling Distribution of a Sample Mean
• The mean and standard deviation of x¯
• The central limit theorem
• A few more facts
• Beyond the Basics: Weibull distributions
• Section 5.2 Summary
• Section 5.2 Exercises
• 5.3 Sampling Distributions for Counts and Proportions
• The binomial distributions for sample counts
• Binomial distributions in statistical sampling
• Finding binomial probabilities
• Binomial mean and standard deviation
• Sample proportions
• Normal approximation for counts and proportions
• The continuity correction
• Binomial formula
• The Poisson distributions
• Section 5.3 Summary
• Section 5.3 Exercises
• Chapter 5 Exercises
• CHAPTER 6 Introduction to Inference
• Introduction
• Overview of inference
• 6.1 Estimating with Confidence
• Statistical confidence
• Confidence intervals
• Confidence interval for a population mean
• How confidence intervals behave
• Choosing the sample size
• Some cautions
• Section 6.1 Summary
• Section 6.1 Exercises
• 6.2 Tests of Significance
• The reasoning of significance tests
• Stating hypotheses
• Test statistics
• P-values
• Statistical significance
• Tests for a population mean
• Two-sided significance tests and confidence intervals
• The P-value versus a statement of significance
• Section 6.2 Summary
• Section 6.2 Exercises
• 6.3 Use and Abuse of Tests
• Choosing a level of significance
• What statistical significance does not mean
• Don’t ignore lack of significance
• Statistical inference is not valid for all sets of data
• Beware of searching for significance
• Section 6.3 Summary
• Section 6.3 Exercises
• 6.4 Power and Inference as a Decision
• Power
• Increasing the power
• Inference as decision
• Two types of error
• Error probabilities
• The common practice of testing hypotheses
• Section 6.4 Summary
• Section 6.4 Exercises
• Chapter 6 Exercises
• CHAPTER 7 Inference for Means
• Introduction
• 7.1 Inference for the Mean of a Population
• The t distributions
• The one-sample t confidence interval
• The one-sample t test
• Matched pairs t procedures
• Robustness of the t procedures
• Beyond the Basics: The bootstrap
• Section 7.1 Summary
• Section 7.1 Exercises
• 7.2 Comparing Two Means
• The two-sample z statistic
• The two-sample t procedures
• The two-sample t confidence interval
• The two-sample t significance test
• Robustness of the two-sample procedures
• Inference for small samples
• Software approximation for the degrees of freedom
• The pooled two-sample t procedures
• Section 7.2 Summary
• Section 7.2 Exercises
• 7.3 Additional Topics on Inference
• Choosing the sample size
• Inference for non-Normal populations
• Section 7.3 Summary
• Section 7.3 Exercises
• Chapter 7 Exercises
• CHAPTER 8 Inference for Proportions
• Introduction
• 8.1 Inference for a Single Proportion
• Large-sample confidence interval for a single proportion
• Beyond the Basics: The plus four confidence interval for a single proportion
• Significance test for a single proportion
• Choosing a sample size for a confidence interval
• Choosing a sample size for a significance test
• Section 8.1 Summary
• Section 8.1 Exercises
• 8.2 Comparing Two Proportions
• Large-sample confidence interval for a difference in proportions
• Beyond the Basics: The plus four confidence interval for a difference in proportions
• Significance test for a difference in proportions
• Choosing a sample size for two sample proportions
• Beyond the Basics: Relative risk
• Section 8.2 Summary
• Section 8.2 Exercises
• Chapter 8 Exercises
• CHAPTER 9 Inference for Categorical Data
• Introduction
• 9.1 Inference for Two-Way Tables
• The hypothesis: No association
• Expected cell counts
• The chi-square test
• Computations
• Computing conditional distributions
• The chi-square test and the z test
• Beyond the Basics: Meta-analysis
• Section 9.1 Summary
• Section 9.1 Exercises
• 9.2 Goodness of Fit
• Section 9.2 Summary
• Section 9.2 Exercises
• Chapter 9 Exercises
• CHAPTER 10 Inference for Regression
• Introduction
• 10.1 Simple Linear Regression
• Statistical model for linear regression
• Preliminary data analysis and inference considerations
• Estimating the regression parameters
• Checking model assumptions
• Confidence intervals and significance tests
• Confidence intervals for mean response
• Prediction intervals
• Transforming variables
• Beyond the Basics: Nonlinear regression
• Section 10.1 Summary
• Section 10.1 Exercises
• 10.2 More Detail about Simple Linear Regression
• Analysis of variance for regression
• The ANOVA F test
• Calculations for regression inference
• Inference for correlation
• Section 10.2 Summary
• Section 10.2 Exercises
• Chapter 10 Exercises
• CHAPTER 11 Multiple Regression
• Introduction
• 11.1 Inference for Multiple Regression
• Population multiple regression equation
• Data for multiple regression
• Multiple linear regression model
• Estimation of the multiple regression parameters
• Confidence intervals and significance tests for regression coefficients
• ANOVA table for multiple regression
• Squared multiple correlation R2
• Section 11.1 Summary
• Section 11.1 Exercises
• 11.2 A Case Study
• Preliminary analysis
• Relationships between pairs of variables
• Regression on high school grades
• Interpretation of results
• Examining the residuals
• Refining the model
• Regression on SAT scores
• Regression using all variables
• Test for a collection of regression coefficients
• Beyond the Basics: Multiple logistic regression
• Section 11.2 Summary
• Section 11.2 Exercises
• Chapter 11 Exercises
• CHAPTER 12 One-Way Analysis of Variance
• Introduction
• 12.1 Inference for One-Way Analysis of Variance
• Data for one-way ANOVA
• Comparing means
• The two-sample t statistic
• An overview of ANOVA
• The ANOVA model
• Estimates of population parameters
• Testing hypotheses in one-way ANOVA
• The ANOVA table
• The F test
• Software
• Beyond the Basics: Testing the equality of spread
• Section 12.1 Summary
• Section 12.1 Exercises
• 12.2 Comparing the Means
• Contrasts
• Multiple comparisons
• Power
• Section 12.2 Summary
• Section 12.2 Exercises
• Chapter 12 Exercises
• CHAPTER 13 Two-Way Analysis of Variance
• Introduction
• 13.1 The Two-Way ANOVA Model
• The two-way ANOVA model
• Main effects and interactions
• 13.2 Inference for Two-Way ANOVA
• The ANOVA table for two-way ANOVA
• Chapter 13 Summary
• Chapter 13 Exercises
• Tables
• Notes and Data Sources
• Index

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Vörumerki: Macmillan
Vörunúmer: 9781319183073
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# Introduction to the Practice of Statistics

Vörumerki: Macmillan
Vörunúmer: 9781319183073
Rafbók

5.690 kr.
5.690 kr.