Principles & methods of statistical analysis. (2017)

• Record Type: Book
• Further Information: Note: Jerome Frieman, Donald A. Saucier, Stuart S. Miller.
• Authors: Frieman, Jerome; Saucier, Donald A; Miller, Stuart S
• Contents: Preface; About the Authors; Prologue; PART I • GETTING STARTED; Chapter 1: The Big Picture; Models; The Classical Statistical Model; Designing Experiments and Analyzing Data; Summary; Questions Raised by the Use of the Classical Statistical Model; Conceptual Exercises; Chapter 2: Examining Our Data: An Introduction to Some of the Techniques of Exploratory Data Analysis; Descriptive Statistics; Histograms; Exploratory Data Analysis; Quantile Plots; Stem-and-Leaf Displays; Letter-Value Displays; Box Plots; Did My Data Come From a Normal Distribution?; Why Should We Care About Looking at Our Data?; Summary; Conceptual Exercises; PART II • THE BEHAVIOR OF DATA; Chapter 3: Properties of Distributions: The Building Blocks of Statistical Inference; The Effects of Adding a Constant or Multiplying by a Constant; The Standard Score Transformation; The Effects of Adding or Subtracting Scores From Two Different Distributions; The Distribution of Sample Means; The Central Limit Theorem; Averaging Means and Variances; Expected Value; Theorems on Expected Value; Summary; Conceptual Exercises; PART III • THE BASICS OF STATISTICAL INFERENCE: DRAWING CONCLUSIONS FROM OUR DATA; Chapter 4: Estimating Parameters of Populations From Sample Data; Statistical Inference With the Classical Statistical Model; Criteria for Selecting Estimators of Population Parameters; Maximum Likelihood Estimation; Confidence Intervals; Beyond Normal Distributions and Estimating Population Means; Summary; Conceptual Exercises; Chapter 5: Resistant Estimators of Parameters; A Closer Look at Sampling From Non-Normal Populations; The Sample Mean and Sample Median Are L-Estimators; Measuring the Influence of Outliers on Estimates of Location and Spread; ?-Trimmed Means as Resistant and Efficient Estimators of Location; Winsorizing: Another Way to Create a Resistant Estimator of Location; Applying These Resistant Estimators to Our Data; Resistant Estimators of Spread; Applying These Resistant Estimators to Our Data (Part 2); M-Estimators: Another Approach to Finding Resistant Estimators of Location; Which Estimator of Location Should I Use?; Resampling Methods for Constructing Confidence Intervals; A Final Caveat; Summary; Conceptual Exercises; Chapter 6: General Principles of Hypothesis Testing; Experimental and Statistical Hypotheses; Estimating Parameters; The Criterion for Evaluating Our Statistical Hypotheses; Creating Our Test Statistic; Drawing Conclusions About Our Null Hypothesis; But Suppose H0 Is False?; Errors in Hypothesis Testing; Power and Power Functions; The Use of Power Functions; p-Values, a, and Alpha (Type I) Errors: What They Do and Do Not Mean; A Word of Caution About Attempting to Estimate the Power of a Hypothesis Test After the Data Have Been Collected; Is It Ever Appropriate to Use a One-Tailed Hypothesis Test?; What Should We Mean When We Say Our Results Are Statistically Significant?; A Final Word; Summary; Conceptual Exercises; PART IV • SPECIFIC TECHNIQUES TO ANSWER SPECIFIC QUESTIONS; Chapter 7: The Independent Groups t-Tests for Testing for Differences Between Population Means; Student’s t-test; Distribution of the Independent Groups t-Statistic when H0 Is True; Distribution of the Independent Groups t-Statistic When H0 Is False; Factors That Affect the Power of the Independent Groups t-Test; The Assumption Behind the Homogeneity of Variance Assumption; Graphical Methods for Comparing Two Groups; Suppose the Population Variances Are Not Equal?; Standardized Group Differences as Estimators of Effect Size; Robust Hypothesis Testing; Resistant Estimates of Effect Size; Summary; Conceptual Exercises; Chapter 8: Testing Hypotheses When the Dependent Variable Consists of Frequencies of Scores in Various Categories; Classifying Data; Testing Hypotheses When the Dependent Variable Consists of Only Two Possibilities; The Binomial Distribution; Testing Hypotheses About the Parameter p in a Binomial Experiment; The Normal Distribution Approximation to the Binomial Distribution; Testing Hypotheses About the Difference Between Two Binomial Parameters (p1 – p2); Testing Hypotheses in Which the Dependent Variable Consists of Two or More Categories; Summary; Conceptual Exercises; Chapter 9: The Randomization/Permutation Model: An Alternative to the Classical Statistical Model for Testing Hypotheses About Treatment Effects; The Assumptions Underlying the Classical Statistical Model; The Assumptions Underlying the Randomization Model; Hypotheses for Both Models; The Exact Randomization Test for Testing Hypotheses About the Effects of Different Treatments on Behavior; The Approximate Randomization Test for Testing Hypotheses About the Effects of Different Treatments on Behavior; Using the Randomization Model to Investigate Possible Effects of Treatments; Single-Participant Experimental Designs; Summary; Conceptual Exercises; Additional Resources; Chapter 10: Exploring the Relationship Between Two Variables: Correlation; Measuring the Degree of Relationship Between Two Interval-Scale Variables; Randomization (Permutation) Model for Testing Hypotheses About the Relationship Between Two Variables; The Bivariate Normal Distribution Model for Testing Hypotheses About Population Correlations; Creating a Confidence Interval for the Population Correlation Using the Bivariate Normal Distribution Model; Bootstrap Confidence Intervals for the Population Correlation; Unbiased Estimators of the Population Correlation; Robust Estimators of Correlation; Assessing the Relationship Between Two Nominal Variables; The Fisher Exact Probability Test for 2 x 2 Contingency Tables With Small Sample Sizes; Correlation Coefficients for Nominal Data in Contingency Tables; Summary; Conceptual Exercises; Chapter 11: Exploring the Relationship Between Two Variables: The Linear Regression Model; Assumptions for the Linear Regression Model; Estimating Parameters With the Linear Regression Model; Regression and Prediction; Variance and Correlation; Testing Hypotheses With the Linear Regression Model; Summary; Conceptual Exercises; Chapter 12: A Closer Look at Linear Regression; The Importance of Looking at Our Data; Using Residuals to Check Assumptions; Testing Whether the Relationship Between Two Variables Is Linear; The Correlation Ratio: An Alternate Way to Measure the Degree of Relationship and Test for a Linear Relationship; Where Do We Go From Here?; When the Relationship Is Not Linear; The Effects of Outliers on Regression; Robust Alternatives to the Method of Least Squares; A Quick Peek at Multiple Regression; Summary; Conceptual Exercises; Chapter 13: Another Way to Scale the Size of Treatment Effects; The Point Biserial Correlation Coefficient and the t-Test; Advantages and Disadvantages of Estimating Effect Sizes With Correlation Coefficients or Standardized Group Difference Measures; Confidence Intervals for Effect Size Estimates; Final Comments on the Use of Effect Size Estimators; Summary; Conceptual Exercises; Chapter 14: Analysis of Variance for Testing for Differences Between Population Means; What Are the Sources of Variation in Our Experiments?; Experimental and Statistical Hypotheses; Estimating Variances; When There Are More Than Two Conditions in Your Experiment; Assumptions for Analysis of Variance; Testing Hypotheses About Differences Among Population Means With Analysis of Variance; Factors That Affect the Power of the F-Test in Analysis of Variance; Relational Effect Size Measures for Analysis of Variance; Randomization Tests for Testing for Differential Effects of Three or More Treatments; Using ANOVA to Study the Effects of More Than One Factor on Behavior; Partitioning Variance for a Two-Factor Analysis of Variance; Testing Hypotheses With Two-Factor Analysis of Variance; Testing Hypotheses About Differences Among Population Means With Analysis of Variance; Dealing With Unequal Sample Sizes
• Edition: 1st
• Publisher Details: Los Angeles : SAGE
• Publication Date: 2017
• Extent: 1 online resource
• Subjects: 001.422; Statistics -- Methodology
• Languages: English
• ISBNs: 9781483358604
• Related ISBNs: 9781483358598
• Notes: Note: Description based on CIP data; item not viewed.
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