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Serious stats : a guide to advanced statistics for the behavioral sciences / Thom Baguley.

By: Material type: TextTextPublisher: Houndmills, Basingstoke, Hampshire ; New York : Palgrave Macmillan, 2012Description: xxiii, 830 pages : illustrations ; 25 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 0230577172
  • 9780230577176
  • 0230577180
  • 9780230577183
Other title:
  • Serious stats : A guide to advanced statistics for the behavioural sciences
Subject(s): DDC classification:
  • 300.15195 23
LOC classification:
  • BF39 .B3175 2012
Contents:
Data, samples and statistics -- 1.1. Chapter overview -- 1.2. What are data? -- 1.3. Samples and populations -- 1.4. Central tendency -- 1.5. Dispersion within a sample -- 1.6. Description, inference and bias -- 1.7. R code for Chapter 1 -- 1.8. Notes on SPSS syntax for Chapter 1 -- 1.9. Bibliography and further reading -- 2. Probability distributions -- 2.1. Chapter overview -- 2.2. Why are probability distributions important in statistics? -- 2.3. Discrete distributions -- 2.4. Continuous distributions -- 2.5. R code for Chapter 2 -- 2.6. Notes on SPSS syntax for Chapter 22.7 Bibliography and further reading -- 3. Confidence intervals -- 3.1. Chapter overview -- 3.2. From point estimates to interval estimates -- 3.3. Confidence intervals -- 3.4. Confidence intervals for a difference -- 3.5. Using Monte Carlo methods to estimate confidence intervals -- 3.6. Graphing confidence intervals -- 3.7. R code for Chapter 3 -- 3.8. Notes on SPSS syntax for Chapter 3 -- 3.9. Bibliography and further reading -- 4. Significance tests -- 4.1. Chapter overview -- 4.2. From confidence intervals to significance tests -- 4.3. Null hypothesis significance tests -- 4.4. t tests -- 4.5. Tests for discrete data4.6 Inference about other parameters -- 4.7. Good practice in the application of significance testing -- 4.8. R code for Chapter 4 -- 4.9. Notes on SPSS syntax for Chapter 4 -- 4.10. Bibliography and further reading -- 5. Regression -- 5.1. Chapter overview -- 5.2. Regression models, prediction and explanation -- 5.3. Mathematics of the linear function -- 5.4. Simple linear regression -- 5.5. Statistical inference in regression -- 5.6. Fitting and interpreting regression models -- 5.7. Fitting curvilinear relationships with simple linear regression -- 5.8. R code for Chapter 5 -- 5.9. Notes on SPSS syntax for Chapter 55.10 Bibliography and further reading -- 6. Correlation and covariance -- 6.1. Chapter overview -- 6.2. Correlation, regression and association -- 6.3. Statistical inference with the product-moment correlation coefficient -- 6.4. Correlation, error and reliability -- 6.5. Alternative correlation coefficients -- 6.6. Inferences about differences in slopes -- 6.7. R code for Chapter 6 -- 6.8. Notes on SPSS syntax for Chapter 6 -- 6.9. Bibliography and further reading -- 7. Effect size -- 7.1. Chapter overview -- 7.2. The role of effect size in research -- 7.3. Selecting an effect size metric -- 7.4. Effect size metrics for continuous outcomes7.5 Effect size metrics for discrete variables -- 7.6. R code for Chapter 7 -- 7.7. Notes on SPSS syntax for Chapter 7 -- 7.8. Bibliography and further reading -- 7.9. Online supplement 1: Meta-analysis -- 8. Statistical power -- 8.1. Chapter overview -- 8.2. Significance tests, effect size and statistical power -- 8.3. Statistical power and sample size -- 8.4. Statistical power analysis -- 8.5. Accuracy in parameter estimation (AIPE) -- 8.6. Estimating? -- 8.7. R code for Chapter 8 -- 8.8. Notes on SPSS syntax for Chapter 8 -- 8.9. Bibliography and further reading.
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Includes bibliographical references and index.

g1. Data, samples and statistics -- 1.1. Chapter overview -- 1.2. What are data? -- 1.3. Samples and populations -- 1.4. Central tendency -- 1.5. Dispersion within a sample -- 1.6. Description, inference and bias -- 1.7. R code for Chapter 1 -- 1.8. Notes on SPSS syntax for Chapter 1 -- 1.9. Bibliography and further reading -- 2. Probability distributions -- 2.1. Chapter overview -- 2.2. Why are probability distributions important in statistics? -- 2.3. Discrete distributions -- 2.4. Continuous distributions -- 2.5. R code for Chapter 2 -- 2.6. Notes on SPSS syntax for Chapter 22.7 Bibliography and further reading -- 3. Confidence intervals -- 3.1. Chapter overview -- 3.2. From point estimates to interval estimates -- 3.3. Confidence intervals -- 3.4. Confidence intervals for a difference -- 3.5. Using Monte Carlo methods to estimate confidence intervals -- 3.6. Graphing confidence intervals -- 3.7. R code for Chapter 3 -- 3.8. Notes on SPSS syntax for Chapter 3 -- 3.9. Bibliography and further reading -- 4. Significance tests -- 4.1. Chapter overview -- 4.2. From confidence intervals to significance tests -- 4.3. Null hypothesis significance tests -- 4.4. t tests -- 4.5. Tests for discrete data4.6 Inference about other parameters -- 4.7. Good practice in the application of significance testing -- 4.8. R code for Chapter 4 -- 4.9. Notes on SPSS syntax for Chapter 4 -- 4.10. Bibliography and further reading -- 5. Regression -- 5.1. Chapter overview -- 5.2. Regression models, prediction and explanation -- 5.3. Mathematics of the linear function -- 5.4. Simple linear regression -- 5.5. Statistical inference in regression -- 5.6. Fitting and interpreting regression models -- 5.7. Fitting curvilinear relationships with simple linear regression -- 5.8. R code for Chapter 5 -- 5.9. Notes on SPSS syntax for Chapter 55.10 Bibliography and further reading -- 6. Correlation and covariance -- 6.1. Chapter overview -- 6.2. Correlation, regression and association -- 6.3. Statistical inference with the product-moment correlation coefficient -- 6.4. Correlation, error and reliability -- 6.5. Alternative correlation coefficients -- 6.6. Inferences about differences in slopes -- 6.7. R code for Chapter 6 -- 6.8. Notes on SPSS syntax for Chapter 6 -- 6.9. Bibliography and further reading -- 7. Effect size -- 7.1. Chapter overview -- 7.2. The role of effect size in research -- 7.3. Selecting an effect size metric -- 7.4. Effect size metrics for continuous outcomes7.5 Effect size metrics for discrete variables -- 7.6. R code for Chapter 7 -- 7.7. Notes on SPSS syntax for Chapter 7 -- 7.8. Bibliography and further reading -- 7.9. Online supplement 1: Meta-analysis -- 8. Statistical power -- 8.1. Chapter overview -- 8.2. Significance tests, effect size and statistical power -- 8.3. Statistical power and sample size -- 8.4. Statistical power analysis -- 8.5. Accuracy in parameter estimation (AIPE) -- 8.6. Estimating? -- 8.7. R code for Chapter 8 -- 8.8. Notes on SPSS syntax for Chapter 8 -- 8.9. Bibliography and further reading.

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