# Introduction to Statistical Methods and Data Analysis

## Edition: 5th 2001 (Revised)

### Authors: Michael T. Longnecker, R. Lyman Ott

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### Description:

Statistics is a thought process. In this comprehensive introduction to statistical methods and data analysis, the process is presented utilizing a four-step approach: 1) gathering data, 2) summarizing data, 3) analyzing data, and 4) communicating the results of data analyses.
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### Book details

List price: \$356.95
Edition: 5th
Publisher: Brooks/Cole
Publication date: 12/20/2000
Binding: Hardcover
Pages: 1184
Size: 8.25" wide x 10.00" long x 1.75" tall
Weight: 5.082
Language: English

 Introduction What Is Statistics? Introduction Why Study Statistics? Some Current Applications of Statistics What Do Statisticians Do? Quality and Process Improvement A Note to the Student Summary Supplementary Exercises Collecting The Data Using Surveys And Scientific Studies To Collect Data Introduction Surveys Scientific Studies Observational Studies Data Management: Preparing Data for Summarization and Analysis Summary Summarizing Data Data Description Introduction Describing Data on a Single Variable: Graphical Methods Describing Data on a Single Variable: Measures of Central Tendency Describing Data on a Single Variable: Measures of Variability The Box Plot Summarizing Data from More Than One Variable Calculators, Computers, and Software Systems Summary Key Formulas Supplementary Exercises Tools And Concepts Probability And Probability Distributions How Probability Can Be Used in Making Inferences Finding the Probability of an Event Basic Event Relations and Probability Laws Conditional Probability and Independence Bayes''''s Formula Variables: Discrete and Continuous Probability Distributions for Discrete Random Variables A Useful Discrete Random Variable: The Binomial Probability Distributions for Continuous Random Variables A Useful Continuous Random Variable: The Normal Distribution Random Sampling Sampling Distributions Normal Approximation to the Binomial Summary Key Formulas Supplementary Exercises Analyzing Data: Central Values, Variances, And Proportions Inferences On A Population Central Value Introduction and Case Study Estimation of ï¿½ Choosing the Sample Size for Estimating ï¿½ A Statistical Test for ï¿½ Choosing the Sample Size for Testing ï¿½ The Level of Significance of a Statistical Test Inferences about ï¿½ for Normal Population, s Unknown Inferences about the Population Median Summary Key Formulas Supplementary Exercises Comparing Two Population Central Values Introduction and Case Study Inferences about ï¿½1 - ï¿½2: Independent Samples A Nonparametric Alternative: The Wilcoxon Rank Sum Test Inferences about ï¿½1 - ï¿½2: Paired Data A Nonparametric Alternative: The Wilcoxon Signed-Rank Test Choosing Sample Sizes for Inferences about ï¿½1 - ï¿½2 Summary Key Formulas Supplementary Exercises Inferences About Population Variances Introduction and Case Study Estimation and Tests for a Population Variance Estimation and Tests for Comparing Two Population Variances Tests for Comparing k >2 Population Variances Summary Key Formulas Supplementary Exercises Inferences About Population Central Values Introduction and Case Study A Statistical Test About More Than Two Population Variances Checking on the Assumptions Alternative When Assumptions are Violated: Transformations A Nonparametric Alternative: The Kruskal-Wallis Test Summary Key Formulas Supplementary Exercises Multiple Comparisons Introduction and Case Study Planned Comparisons Among Treatments: Linear Contrasts Which Error Rate Is Controlled Multiple Comparisons with the Best Treatment Comparison of Treatments to a Control Pairwise Comparison on All Treatments Summary Key Formulas Supplementary Exercises Categorical Data Introduction and Case Study Inferences about a Population Proportion p Comparing Two Population Proportions p1 - p2 Probability Distributions for Discrete Random Variables The Multinomial Experiment and Chi-Square Goodness-of-Fit Test The Chi-Square Test of Homogeneity of Proportions The Chi-Square Test of Independence of Two Nominal Level Variables Fisher''''s Exact Test, a Permutation Test Measures of Association Combining Sets of Contingency Tables Summary Key Formulas Supplementary Exercises PART VI: Analyzing Data: Regression Methods, Model Building Simple Linear Regression And Correlation Linear Regression a
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