{"product_id":"statistics-a-gentle-introduction-9781506368436","title":"Statistics: A Gentle Introduction","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThe Fourth Edition of \u003cstrong\u003e\u003cem\u003eStatistics: A Gentle Introduction\u003c\/em\u003e\u003c\/strong\u003e shows students that an introductory statistics class doesn’t need to be difficult or dull. This text minimizes students’ anxieties about math by explaining the concepts of statistics in plain language first, before addressing the math. Each formula within the text has a step-by-step example to demonstrate the calculation so students can follow along. Only those formulas that are important for final calculations are included in the text so students can focus on the concepts, not the numbers. A wealth of real-world examples and applications gives a context for statistics in the real world and how it helps us solve problems and make informed choices. \u003c\/p\u003e  \u003cp\u003eNew to the \u003cstrong\u003eFourth Edition \u003c\/strong\u003eare sections on working with big data, new coverage of alternative non-parametric tests, beta coefficients, and the \"nocebo effect,\" discussions of p values in the context of research, an expanded discussion of confidence intervals, and more exercises and homework options under the new feature \"Test Yourself.\"\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cstrong\u003eIncluded with this title: \u003c\/strong\u003e\u003cbr\u003e  \u003cstrong\u003e\u003cbr\u003e  The password-protected Instructor Resource Site (formally known as SAGE Edge) \u003c\/strong\u003eoffers access to all text-specific resources, including a test bank and editable, chapter-specific PowerPoint® slides.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eStatistics is generally not a dynamic topic. But Coolidge is able to break it down in a way that is manageable. His discussion of each type of analyses is easily accessed by the table of contents and accurately depicted in the index. This is especially important for this generation of learners who want easy access to the specific information that is necessary without waiting through extraneous concepts. Coolidge also describes contemporary and specific examples of how miss use of data can have an impact in real world circumstances. This is beneficial because it makes a true connection with the power that a statistical researcher holds.\u003c\/p\u003e \u003cbr\u003e \u003cbr\u003e -- Dr. Lynn DeSpain\u003cbr\u003eIt is the only book on the market that covers important advanced techniques such as repeated measures ANOVA and multiple regressions, using SPSS. -- Abby Heckman Coats\u003cbr\u003eThe book is written to address a broad range of student ability. It is helpful to students without a strong background in mathematics.\u003cbr\u003e -- Andrew Zekeri\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface Acknowledgments About the Author Chapter 1: A Gentle Introduction    How Much Math Do I Need to Do Statistics?    The General Purpose of Statistics: Understanding the World    What Is a Statistician?    Liberal and Conservative Statisticians    Descriptive and Inferential Statistics    Experiments Are Designed to Test Theories and Hypotheses    Oddball Theories    Bad Science and Myths    Eight Essential Questions of Any Survey or Study    On Making Samples Representative of the Population    Experimental Design and Statistical Analysis as Controls    The Language of Statistics    On Conducting Scientific Experiments    The Dependent Variable and Measurement    Operational Definitions    Measurement Error    Measurement Scales: The Difference Between Continuous and Discrete Variables    Types of Measurement Scales    Rounding Numbers and Rounding Error    Statistical Symbols    Summary    History Trivia: Achenwall to Nightingale    Key Terms    Chapter 1 Practice Problems    Chapter 1 Test Yourself Questions    SPSS Lesson 1 Chapter 2: Descriptive Statistics: Understanding Distributions of Numbers    The Purpose of Graphs and Tables: Making Arguments and Decisions    A Summary of the Purpose of Graphs and Tables    Graphical Cautions    Frequency Distributions    Shapes of Frequency Distributions    Grouping Data Into Intervals    Advice on Grouping Data Into Intervals    The Cumulative Frequency Distribution    Cumulative Percentages, Percentiles, and Quartiles    Stem-and-Leaf Plot    Non-normal Frequency Distributions    On the Importance of the Shapes of Distributions    Additional Thoughts About Good Graphs Versus Bad Graphs    History Trivia: De Moivre to Tukey    Key Terms    Chapter 2 Practice Problems    Chapter 2 Test Yourself Questions    SPSS Lesson 2 Chapter 3: Statistical Parameters: Measures of Central Tendency and Variation    Measures of Central Tendency    Choosing Among Measures of Central Tendency    Klinkers and Outliers    Uncertain or Equivocal Results    Measures of Variation    Correcting for Bias in the Sample Standard Deviation    How the Square Root of x2 Is Almost Equivalent to Taking the Absolute Value of x    The Computational Formula for Standard Deviation    The Variance    The Sampling Distribution of Means, the Central Limit Theorem, and the Standard Error of the Mean    The Use of the Standard Deviation for Prediction    Practical Uses of the Empirical Rule: As a Definition of an Outlier    Practical Uses of the Empirical Rule: Prediction and IQ Tests    Some Further Comments    History Trivia: Fisher to Eels    Key Terms    Chapter 3 Practice Problems    Chapter 3 Test Yourself Questions    SPSS Lesson 3 Chapter 4: Standard Scores, the z Distribution, and Hypothesis Testing    Standard Scores    The Classic Standard Score: The z Score and the z Distribution    Calculating z Scores    More Practice on Converting Raw Data Into z Scores    Converting z Scores to Other Types of Standard Scores    The z Distribution    Interpreting Negative z Scores    Testing the Predictions of the Empirical Rule With the z Distribution    Why Is the z Distribution So Important?    How We Use the z Distribution to Test Experimental Hypotheses    More Practice With the z Distribution and T Scores    Summarizing Scores Through Percentiles    History Trivia: Karl Pearson to Egon Pearson    Key Terms    Chapter 4 Practice Problems    Chapter 4 Test Yourself Questions    SPSS Lesson 4 Chapter 5: Inferential Statistics: The Controlled Experiment, Hypothesis Testing, and the z Distribution    Hypothesis Testing in the Controlled Experiment    Hypothesis Testing: The Big Decision    How the Big Decision Is Made: Back to the z Distribution    The Parameter of Major Interest in Hypothesis Testing: The Mean    Nondirectional and Directional Alternative Hypotheses    A Debate: Retain the Null Hypothesis or Fail to Reject the Null Hypothesis    The Null Hypothesis as a Nonconservative Beginning    The Four Possible Outcomes in Hypothesis Testing    Significance Levels    Significant and Nonsignificant Findings    Trends, and Does God Really Love the .05 Level of Significance More Than the .06 Level?    Directional or Nondirectional Alternative Hypotheses: Advantages and Disadvantages    Did Nuclear Fusion Occur?    Baloney Detection    Conclusions About Science and Pseudoscience    The Most Critical Elements in the Detection of Baloney in Suspicious Studies and Fraudulent Claims    Can Statistics Solve Every Problem?    Probability    History Trivia: Egon Pearson to Karl Pearson    Key Terms    Chapter 5 Practice Problems    Chapter 5 Test Yourself Questions    SPSS Lesson 5 Chapter 6: An Introduction to Correlation and Regression    Correlation: Use and Abuse    A Warning: Correlation Does Not Imply Causation    Another Warning: Chance Is Lumpy    Correlation and Prediction    The Four Common Types of Correlation    The Pearson Product–Moment Correlation Coefficient    Testing for the Significance of a Correlation Coefficient    Obtaining the Critical Values of the t Distribution    If the Null Hypothesis Is Rejected    Representing the Pearson Correlation Graphically: The Scatterplot    Fitting the Points With a Straight Line: The Assumption of a Linear Relationship    Interpretation of the Slope of the Best-Fitting Line    The Assumption of Homoscedasticity    The Coefficient of Determination: How Much One Variable Accounts for Variation in Another Variable—The Interpretation of r2    Quirks in the Interpretation of Significant and Nonsignificant Correlation Coefficients    Linear Regression    Reading the Regression Line    Final Thoughts About Multiple Regression Analyses: A Warning About the Interpretation of the Significant Beta Coefficients    Spearman’s Correlation    Significance Test for Spearman’s r    Ties in Ranks    Point-Biserial Correlation    Testing for the Significance of the Point-Biserial Correlation Coefficient    Phi (F) Correlation    Testing for the Significance of Phi    History Trivia: Galton to Fisher    Key Terms    Chapter 6 Practice Problems    Chapter 6 Test Yourself Questions    SPSS Lesson 6 Chapter 7: The t Test for Independent Groups    The Statistical Analysis of the Controlled Experiment    One t Test but Two Designs    Assumptions of the Independent t Test    The Formula for the Independent t Test    You Must Remember This! An Overview of Hypothesis Testing With the t Test    What Does the t Test Do? Components of the t Test Formula    What If the Two Variances Are Radically Different From One Another?    A Computational Example    Marginal Significance    The Power of a Statistical Test    Effect Size    The Correlation Coefficient of Effect Size    Another Measure of Effect Size: Cohen’s d    Confidence Intervals    Estimating the Standard Error    History Trivia: Gosset and Guinness Brewery    Key Terms    Chapter 7 Practice Problems    Chapter 7 Test Yourself Questions    SPSS Lesson 7 Chapter 8: The t Test for Dependent Groups    Variations on the Controlled Experiment    Assumptions of the Dependent t Test    Why the Dependent t Test May Be More Powerful Than the Independent t Test    How to Increase the Power of a t Test    Drawbacks of the Dependent t Test Designs    One-Tailed or Two-Tailed Tests of Significance    Hypothesis Testing and the Dependent t Test: Design 1    Design 1 (Same Participants or Repeated Measures): A Computational Example    Design 2 (Matched Pairs): A Computational Example    Design 3 (Same Participants and Balanced Presentation): A Computational Example    History Trivia: Fisher to Pearson    Key Terms    Chapter 8 Practice Problems    Chapter 8 Test Yourself Questions    SPSS Lesson 8 Chapter 9: Analysis of Variance (ANOVA): One-Factor Completely Randomized Design    A Limitation of Multiple t Tests and a Solution    The Equally Unacceptable Bonferroni Solution    The Acceptable Solution: An Analysis of Variance    The Null and Alternative Hypotheses in ANOVA    The Beauty and Elegance of the F Test Statistic    The F Ratio    How Can There Be Two Different Estimates of Within-Groups Variance?    ANOVA Designs    ANOVA Assumptions    Pragmatic Overview    What a Significant ANOVA Indicates    A Computational Example    Degrees of Freedom for the Numerator    Degrees of Freedom for the Denominator    Determining Effect Size in ANOVA: Omega Squared (w2)    Another Measure of Effect Size: Eta (h)    History Trivia: Gosset to Fisher    Key Terms    Chapter 9 Practice Problems    Chapter 9 Test Questions    Chapter 9 Test Yourself Questions    SPSS Lesson 9 Chapter 10: After a Significant ANOVA: Multiple Comparison Tests    Conceptual Overview of Tukey’s Test    Computation of Tukey’s HSD Test    What to Do If the Number of Error Degrees of Freedom Is Not Listed in the Table of Tukey’s q Values    Determining What It All Means    Warning!    On the Importance of Nonsignificant Mean Differences    Final Results of ANOVA    Quirks in Interpretation    Tukey’s With Unequal Ns    Key Terms    Chapter 10 Practice Problems    Chapter 10 Test Yourself Questions    SPSS Lesson 10 Chapter 11: Analysis of Variance (ANOVA): One-Factor Repeated-Measures Design    The Repeated-Measures ANOVA    Assumptions of the One-Factor Repeated-Measures ANOVA    Computational Example    Determining Effect Size in ANOVA    Key Terms    Chapter 11 Practice Problems    Chapter 11 Test Yourself Questions    SPSS Lesson 11 Chapter 12: Factorial ANOVA: Two-Factor Completely Randomized Design    Factorial Designs    The Most Important Feature of a Factorial Design: The Interaction    Fixed and Random Effects and In Situ Designs    The Null Hypotheses in a Two-Factor ANOVA    Assumptions and Unequal Numbers of Participants    Computational Example    Key Terms    Chapter 12 Practice Problems    Chapter 12 Test Yourself Problems    SPSS Lesson 12 Chapter 13: Post Hoc Analysis of Factorial ANOVA    Main Effect Interpretation: Gender    Why a Multiple Comparison Test Is Unnecessary for a Two-Level Main Effect, and When Is a Multiple Comparison Test Necessary?    Main Effect: Age Levels    Multiple Comparison Test for the Main Effect for Age    Warning: Limit Your Main Effect Conclusions When the Interaction Is Significant    Multiple Comparison Tests    Interpretation of the Interaction Effect    Final Summary    Writing Up the Results Journal Style    Language to Avoid    Exploring the Possible Outcomes in a Two-Factor ANOVA    Determining Effect Size in a Two-Factor ANOVA    History Trivia: Fisher and Smoking    Key Terms    Chapter 13 Practice Problems    Chapter 13 Test Yourself Questions    SPSS Lesson 13 Chapter 14: Factorial ANOVA: Additional Designs    The Split-Plot Design    Overview of the Split-Plot ANOVA    Computational Example    Two-Factor ANOVA: Repeated Measures on Both Factors Design    Overview of the Repeated-Measures ANOVA    Computational Example    Key Terms and Definitions    Chapter 14 Practice Problems    Chapter 14 Test Yourself Questions    SPSS Lesson 14 Chapter 15: Nonparametric Statistics: The Chi-Square Test and Other Nonparametric Tests    Overview of the Purpose of Chi-Square    Overview of Chi-Square Designs    Chi-Square Test: Two-Cell Design (Equal Probabilities Type)    The Chi-Square Distribution    Assumptions of the Chi-Square Test    Chi-Square Test: Two-Cell Design (Different Probabilities Type)    Interpreting a Significant Chi-Square Test for a Newspaper    Chi-Square Test: Three-Cell Experiment (Equal Probabilities Type)    Chi-Square Test: Two-by-Two Design    What to Do After a Chi-Square Test Is Significant    When Cell Frequencies Are Less Than 5 Revisited    Other Nonparametric Tests    History Trivia: Pearson and Biometrika    Key Terms    Chapter 15 Practice Problems    Chapter 15 Test Yourself Questions    SPSS Lesson 15 Chapter 16: Other Statistical Topics, Parameters, and Tests    Big Data    Health Science Statistics    Additional Statistical Analyses and Multivariate Statistics    A Summary of Multivariate Statistics    Coda    Key Terms    Chapter 16 Practice Problems    Chapter 16 Test Yourself Questions Appendix A: z Distribution Appendix B: t Distribution Appendix C: Spearman’s Correlation Appendix D: Chi-Square ?2 Distribution Appendix E: F Distribution Appendix F: Tukey’s Table Appendix G: Mann–Whitney U Critical Values Appendix H: Wilcoxon Signed-Rank Test Critical Values Appendix I: Answers to Odd-Numbered Test Yourself Questions Glossary References Index","brand":"SAGE Publications Inc","offers":[{"title":"Default Title","offer_id":51019967037783,"sku":"9781506368436","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781506368436.jpg?v=1750781909","url":"https:\/\/bookcurl.com\/products\/statistics-a-gentle-introduction-9781506368436","provider":"Book Curl","version":"1.0","type":"link"}