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# Introduction to Statistical Methods and Data Analysis

## Edition: 5th 2001

### Authors: Longnecker Ott, Micheal Longnecker, R. Lyman Ott

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### Description:

Provides worked-out solutions to odd-numbered exercises.
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### Book details

List price: \$74.95
Edition: 5th
Copyright year: 2001
Publisher: Brooks/Cole
Publication date: 1/30/2002
Binding: Paperback
Pages: 208
Size: 7.50" wide x 9.00" long x 0.50" tall
Weight: 1.034
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 ? Choosing the Sample Size for Estimating ? A Statistical Test for ? Choosing the Sample Size for Testing ? The Level of Significance of a Statistical Test Inferences about ? 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 ?1 - ?2: Independent Samples A Nonparametric Alternative: The Wilcoxon Rank Sum Test Inferences About ?1 - ?2: Paired Data A Nonparametric Alternative: The Wilcoxon Signed-Rank Test Choosing Sample Sizes for Inferences About ?1 - ?2 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 More Than Two Population Central Values Introduction and Case Study A Statistical Test About More Than Two Population Means Checking on the Assumptions Alternative When Assumptions are Violated: Transformations A Nonparametric Alternative: The Kruskal-Wallis test Summary Key Formulas Supplementary Exercises Mulitple Comparisons Introduction and Case Study Planned Comparisons Among Treatments: Linear Contrasts Which Error Rate is Controlled Mulitple 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 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 Analyzing Data: Regression Methods, Model Building Simple Linear Regression and Correlation Linear Regression and the Method of Least Squares Transformations to Linearize Data Correlation A Look Ahead: Multiple Regression Summary of Key Formulas Supplementary Exercises Inferences Related to Linear Regression and Correlation Introduction and Case Study Diagnostics for Detecting Violations of Model Conditions Inferences About the Intercept and Slope of the Regression Line Inferences About the Population Mean for a Specified Value of the Explanatory Variable Predications and Prediction Intervals Examining Lack of Fit in the Model The Inverse Regression Problem (Calibration): Predicting Values for x for a Specified Value of y Multiple Regression and the General Linear Model The General Linear Model Least Squares Estimates of Parameters in the General Linear Model Inferences About the Parameters in the General Linear Model Inferences About the Population Mean and Predictions from the General Linear Model Comparing the Slope of Several Regression Lines Logistic Regression Matrix Formulation of the General Linear Model Building Regression Models with Diagnostics Selecting the Variables (Step 1) Model Formulation (Step 2) Checking Model Conditions (Step 3) Analyzing Data: Design of Experiments and Anova Design Concepts for Experiments and Studies Experiments, Treatments, Experimental Units, Blocking, Randomization, and Measurement Units How Many Replications Studies for Comparing Means Versus Studies for Comparing Variances Analysis of Variance for Standard Designs Completely Randomized Design with Single Factor Randomized Block Design Latin Square Design Factorial Experiments in a Completely Randomized Design The Estimation of Treatment Differences and Planned Comparisons in the Treatment Means Checking Model Conditions Alternative Analyses: Transformation and Friedman's Rank Based Test Analysis of Covariance A Completely Randomized Design with One Covariate The Extrapolation Problem Multiple Covariates and More Complicated Designs Analysis of Variance for Some Unbalanced Designs A Randomized Block Design with One or More Mission Observations A Latin Square Design with Missing Data Incomplete Block Designs A Factorial Experiment with Missing Factors Analysis of Variance for Some Fixed Effects, Random Effects and Mixed Effects Models A One-Factor Experiment with Random Treatment Effects Extensions of Random-Effects Models A Mixed Model: Experiments with Both Fixed and Random Treatment Effects Models with Nested Factors Rules for Obtaining Expected Mean Squares Split-Plot Designs and Experiments with Repeated Measures Split-Plot Designs Single-Factor Experiments with Repeated Measures Two-Factor Experiments with Repeated Measures on One of the Factors Crossover Design Communicating and Documenting the Results of a Study or Experiment Communicating and Documenting the Results of a Study or Experiment Introduction The Difficulty of Good Communication Communication Hurdles: Graphical Distortions Communication Hurdles: Biased Samples Communication Hurdles Sample Size The Statistical Report Documentation and Storage of Results Summary Supplementary Exercises