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Introduction to the Practice of Statistics TI-83 Manual

ISBN-10: 0716796570
ISBN-13: 9780716796572
Edition: 4th 2002
List price: $132.99 Buy it from $3.00
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Description: The bestselling 'Introduction to the Practice of Statistics' set a new standard for introductory statistics courses by focusing on data analysis, statistical reasoning and the way statistics are used in everyday life. This edition features over 400  More...

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Book details

List price: $132.99
Edition: 4th
Copyright year: 2002
Publisher: W. H. Freeman & Company
Publication date: 7/19/2002
Binding: Box or Slipcased 
Pages: 830
Size: 8.00" wide x 10.00" long x 1.50" tall
Weight: 4.532
Language: English

The bestselling 'Introduction to the Practice of Statistics' set a new standard for introductory statistics courses by focusing on data analysis, statistical reasoning and the way statistics are used in everyday life. This edition features over 400 new exercises.

David S. Moore is a professor of psychology at Pitzer College and at Claremont Graduate University. He received his doctorate in developmental psychology from Harvard University and did his postdoctoral work at the City University of New York.

DAVID S. MOORE Shanti S. Gupta Distinguished Professor of Statistics at Purdue University, USA, and was 1998 president of the American Statistical Association. He is an elected fellow of the American Statistical Association and of the Institute of Mathematical Statistics and an elected member of the International Statistical Institute. He has served as program director for statistics and probability at the National Science Foundation. He has also served as president of the International Association for Statistical Education and has received the Mathematical Association of America's national award for distinguished college or university teaching of mathematics. GEORGE P. McCABE Associate Dean for Academic Affairs and Professor of Statistics at Purdue University, USA. His entire professional career has been spent at Purdue, with sabbaticals at Princeton, the Commonwealth Scientific and Industrial Research Organization (CSIRO) in Melbourne, Australia, the University of Berne, Switzerland, the National Institute of Standards and Technology (NIST) in Boulder, Colorado, USA, and the National University of Ireland in Galway. He is an elected fellow of the American Statistical Association and was 1998 chair of its section on Statistical Consulting. He has served on the editorial boards of several statistics journals. He has consulted with many major corporations and has testified as an expert witness on the use of statistics in several legal cases. BRUCE CRAIG Director of the Statistical Consultingnbsp;Service at Purdue University, USA (following George McCabe). This along with his strong academic research credentials and his many years of teaching statistics at all levels provide the academic experience and contemporary real data from the center's consulting that lecturers have grown to expect in this textbook.

To Teachers: About This Book
To Students: What Is Statistics?
About the Authors
Looking at Data--Distributions
Displaying Distributions with Graphs
Graphs for categorical variables
Measuring the speed of light
Examining distributions
Dealing with outliers
Time plots
Beyond the basics: Decomposing time series
Section 1.1 Exercises
Describing Distributions with Numbers
Measuring center: the mean
Measuring center: the median
Mean versus median
Measuring spread: the quartiles
The five-number summary and boxplots
The 1.5 X IQR criterion 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 1.2 Exercises
The Normal Distributions
Density curves
Measuring center and spread for density curves
Normal distributions
The 68-95-99.7 rule
Standardizing observations
The standard normal distribution
Normal distribution calculations
Normal quantile plots
Beyond the basics: Density estimation
Section 1.3 Exercises
Chapter 1 Exercises
Looking at Data--Relationships
Examining relationships
Interpreting scatterplots
Adding categorical variables to scatterplots
More examples of scatterplots
Beyond the basics: Scatterplot smoothers
Categorical explanatory variables
Section 2.1 Exercises
The correlation r
Properties of correlation
Section 2.2 Exercises
Least-Squares Regression
Fitting a line to data
Least-squares regression
Interpreting the regression line
Correlation and regression
Understanding r[superscript 2]
Section 2.3 Exercises
Cautions about Regression and Correlation
Lurking variables
Outliers and influential observations
Beware the lurking variable
Beware correlations based on averaged data
The restricted-range problem
Beyond the basics: Data mining
Section 2.4 Exercises
The Question of Causation
Explaining association: causation
Explaining association: common response
Explaining association: confounding
Establishing causation
Section 2.5 Exercises
Transforming Relationships
First steps in transforming
The ladder of power transformations
Exponential growth
The logarithm transformation
Prediction in the exponential growth model
Power law models
Prediction in power law models
Section 2.6 Exercises
Chapter 2 Exercises
Producing Data
First Steps
Where to find data: the library and the Internet
Section 3.1 Exercises
Design of Experiments
Comparative experiments
Randomized comparative experiments
How to randomize
Cautions about experimentation
Matched pairs designs
Block designs
Section 3.2 Exercises
Sampling Design
Simple random samples
Stratified samples
Multistage samples
Cautions about sample surveys
Section 3.3 Exercises
Toward Statistical Inference
Sampling variability
Sampling distributions
Bias and variability
Sampling from large populations
Why randomize?
Beyond the basics: Capture-recapture sampling
Section 3.4 Exercises
Chapter 3 Exercises
Probability and Inference
Probability--The Study of Randomness
The language of probability
Thinking about randomness
The uses of probability
Section 4.1 Exercises
Probability Models
Sample spaces
Intuitive probability
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 Exercises
Random Variables
Discrete random variables
Continuous random variables
Normal distributions as probability distributions
Section 4.3 Exercises
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
Section 4.4 Exercises
General Probability Rules
General addition rules
Conditional probability
General multiplication rules
Tree diagrams
Bayes's rule
Independence again
Decision analysis
Section 4.5 Exercises
Chapter 4 Exercises
Sampling Distributions
Sampling Distributions for Counts and Proportions
The binomial distributions for sample counts
Binomial distributions in statistical sampling
Finding binomial probabilities: tables
Binomial mean and standard deviation
Sample proportions
Normal approximation for counts and proportions
The continuity correction
Binomial formulas
Section 5.1 Exercises
The Sampling Distribution of a Sample Mean
The mean and standard deviation of x
The sampling distribution of x
The central limit theorem
Beyond the basics: Weibull distributions
Section 5.2 Exercises
Chapter 5 Exercises
Introduction to Inference
Estimating with Confidence
Statistical confidence
Confidence intervals
Confidence interval for a population mean
How confidence intervals behave
Choosing the sample size
Some cautions
Beyond the basics: The bootstrap
Section 6.1 Exercises
Tests of Significance
The reasoning of significance tests
Stating hypotheses
Test statistics
Statistical significance
Tests for a population mean
Two-sided significance tests and confidence intervals
P-values versus fixed [alpha]
Section 6.2 Exercises
Use and Abuse of Tests
Choosing a level of significance
What statistical significance doesn't 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 Exercises
Power and Inference as a Decision
Increasing the power
Inference as decision
Two types of error
Error probabilities
The common practice of testing hypotheses
Section 6.4 Exercises
Chapter 6 Exercises
Inference for Distributions
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
The power of the t test
Inference for nonnormal populations
Section 7.1 Exercises
Comparing Two Means
The two-sample z statistic
The two-sample t procedures
The two-sample t significance test
The two-sample t confidence interval
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 Exercises
Optional Topics in Comparing Distributions
Inference for population spread
The F test for equality of spread
Robustness of normal inference procedures
The power of the two-sample t test
Section 7.3 Exercises
Chapter 7 Exercises
Inference for Proportions
Inference for a Single Proportion
Confidence interval for a single proportion
Significance test for a single proportion
Confidence intervals provide additional information
Choosing a sample size
Section 8.1 Exercises
Comparing Two Proportions
Confidence intervals
Significance tests
Beyond the basics: Relative risk
Section 8.2 Exercises
Chapter 8 Exercises
Topics in Inference
Analysis of Two-Way Tables
Data Analysis for Two-Way Tables
The two-way table
Marginal distributions
Describing relations in two-way tables
Conditional distributions
Simpson's paradox
The perils of aggregation
Inference for Two-Way Tables
The hypothesis: no association
Expected cell counts
The chi-square test
The chi-square test and the z test
Beyond the basics: Meta-analysis
Formulas and Models for Two-Way Tables
Computing conditional distributions
Computing expected cell counts
Computing the chi-square statistic
Models for two-way tables
Concluding remarks
Chapter 9 Exercises
Inference for Regression
Simple Linear Regression
Statistical model for linear regression
Data for simple linear regression
Estimating the regression parameters
Confidence intervals and significance tests
Confidence intervals for mean response
Prediction intervals
Beyond the basics: Nonlinear regression
More Detail about Simple Linear Regression
Analysis of variance for regression
The ANOVA F test
Calculations for regression inference
Inference for correlation
Chapter 10 Exercises
Multiple Regression
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 R[superscript 2]
A Case Study
Preliminary analysis
Relationships between pairs of variables
Regression on high school grades
Interpretation of results
Refining the model
Regression on SAT scores
Regression using all variables
Test for a collection of regression coefficients
Beyond the basics: Multiple logistic regression
Chapter 11 Exercises
One-Way Analysis of Variance
Inference for One-Way Analysis of Variance
Data for a one-way ANOVA
Comparing means
The two-sample t statistic
ANOVA hypotheses
The ANOVA model
Estimates of population parameters
Testing hypotheses in one-way ANOVA
The ANOVA table
The F test
Comparing the Means
Multiple comparisons
Chapter 12 Exercises
Two-Way Analysis of Variance
The Two-Way ANOVA Model
Advantages of two-way ANOVA
The two-way ANOVA model
Main effects and interactions
Inference for Two-Way ANOVA
The ANOVA table for two-way ANOVA
Chapter 13 Exercises
Data Appendix
Solutions to Selected Exercises
Nonparametric Tests
The Wilcoxon Rank Sum Test
The rank transformation
The Wilcoxon rank sum test
The normal approximation
What hypotheses does Wilcoxon test?
Limitations of nonparametric tests
Section 14.1 Exercises
The Wilcoxon Signed Rank Test
The normal approximation
Section 14.2 Exercises
The Kruskal-Wallis Test
Hypotheses and assumptions
The Kruskal-Wallis test
Section 14.3 Exercises
Chapter 14 Exercises
Logistic Regression
The Logistic Regression Model
Binomial distributions and odds
Model for logistic regression
Fitting and interpreting the logistic regression model
Inference for Logistic Regression
Confidence intervals and significance tests
Multiple logistic regression
Chapter 15 Exercises

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