Statistics One
Lecture 24
Course Summary
1
Four segments
•  Research methods and descriptive statistics 
–  Lectures 1 – 6

•  Simple and multiple regression
–  Lect...
Four segments
•  Group comparisons with t-tests and ANOVA 
–  Lectures 15 – 18

•  Procedures for non-normal distributions...
Lecture 24 ~ Segment 1
Research Methods and
Descriptive Statistics
4
Research methods
•  Descriptive research
•  Experimental research
•  Correlational research
Descriptive statistics
•  Histograms
•  Summary statistics

•  Measures of central tendency
•  Mean
•  Median
•  Mode

•  ...
Descriptive statistics
•  Correlation
•  Covariance
•  Scatterplots
Descriptive statistics
•  Measurement
•  Classical true score theory
•  Reliability
•  Validity
END SEGMENT

9
Lecture 24 ~ Segment 2
Simple and multiple regression

10
Simple and multiple regression
•  Simple regression equation has only one predictor
variable (X)
•  Multiple regression eq...
NHST
•  NHST can be used to test statistical significance of
individual predictor variables and to test statistical
signif...
NHST
• 
• 
• 
• 
• 
• 

Sampling
Sampling error
Sampling distribution
Central limit theorem
Problems with NHST
Remedies
NHST
•  Problems with NHST
• 
• 
• 
• 
• 
• 

BAYES
Biased by sample size
Arbitrary decision rule
Yokel local test
Error p...
NHST
•  Remedies
• 
• 
• 
• 
• 

Effect size
Confidence intervals
Model comparison
Replications
Power
Simple regression
•  Regression equation
•  Regression constant
•  Regression coefficient (unstandardized and
standardized...
Mutiple regression
• 
• 
• 
• 
• 
• 

Matrix algebra
Regression equation (model)
Regression constant
Regression coefficien...
Mutiple regression
•  Moderation

•  Dummy coding
•  Centering

•  Mediation

•  Sobel test
END SEGMENT

19
Lecture 24 ~ Segment 3
Group Comparisons
t-tests and ANOVA
20
Group comparisons
•  z-test
•  Single sample t-test
•  Independent t-test
–  Homogeneity of variance assumption
–  Levene’...
Group comparisons
•  ANOVA: One-way between groups
–  F = MSA = MSS/A
–  Homogeneity of variance assumption
–  Levene’s te...
Group comparisons
•  Factorial ANOVA

–  Main effects
–  Interaction effect
–  Simple effects
•  Homogeneity of variance a...
Group comparisons
•  Repeated measures ANOVA
–  F = MSA = MSAxS
–  Sphericity assumption
–  Mauchly’s test
–  Post-hoc tes...
END SEGMENT

25
Lecture 24 ~ Segment 4
Procedures for non-normal distributions and
non-linear models
26
Categorical outcome variables
•  Chi-square tests
•  Logistic regression
Non-normal distributions
•  How to detect non-normal distributions
–  Histograms and scatterplots
–  Q-Q plots

•  Common ...
Non-parametric statistics
•  Wilcoxan’s ranking method
•  Mann-Whitney U
Non-linear models
•  Generalized Linear Model
–  Binomial
–  Multinomial
–  Poisson
END SEGMENT

31
END LECTURE 24

32
Upcoming SlideShare
Loading in …5
×

Lecture slides stats1.13.l24.air

433 views

Published on

Lecture slides stats1.13.l24.air

Published in: Education, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
433
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
10
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Lecture slides stats1.13.l24.air

  1. 1. Statistics One Lecture 24 Course Summary 1
  2. 2. Four segments •  Research methods and descriptive statistics  –  Lectures 1 – 6 •  Simple and multiple regression –  Lectures 7 – 14 2
  3. 3. Four segments •  Group comparisons with t-tests and ANOVA  –  Lectures 15 – 18 •  Procedures for non-normal distributions and non-linear models –  Lectures 19 – 23 3
  4. 4. Lecture 24 ~ Segment 1 Research Methods and Descriptive Statistics 4
  5. 5. Research methods •  Descriptive research •  Experimental research •  Correlational research
  6. 6. Descriptive statistics •  Histograms •  Summary statistics •  Measures of central tendency •  Mean •  Median •  Mode •  Measures of variability •  Standard deviation •  Variance
  7. 7. Descriptive statistics •  Correlation •  Covariance •  Scatterplots
  8. 8. Descriptive statistics •  Measurement •  Classical true score theory •  Reliability •  Validity
  9. 9. END SEGMENT 9
  10. 10. Lecture 24 ~ Segment 2 Simple and multiple regression 10
  11. 11. Simple and multiple regression •  Simple regression equation has only one predictor variable (X) •  Multiple regression equation has multiple predictor variables
  12. 12. NHST •  NHST can be used to test statistical significance of individual predictor variables and to test statistical significance of the model
  13. 13. NHST •  •  •  •  •  •  Sampling Sampling error Sampling distribution Central limit theorem Problems with NHST Remedies
  14. 14. NHST •  Problems with NHST •  •  •  •  •  •  BAYES Biased by sample size Arbitrary decision rule Yokel local test Error prone Shady logic
  15. 15. NHST •  Remedies •  •  •  •  •  Effect size Confidence intervals Model comparison Replications Power
  16. 16. Simple regression •  Regression equation •  Regression constant •  Regression coefficient (unstandardized and standardized) •  Residual •  Ordinary Least Squares
  17. 17. Mutiple regression •  •  •  •  •  •  Matrix algebra Regression equation (model) Regression constant Regression coefficients (unstandardized and standardized) Residual Model comparison •  Ordinary Least Squares
  18. 18. Mutiple regression •  Moderation •  Dummy coding •  Centering •  Mediation •  Sobel test
  19. 19. END SEGMENT 19
  20. 20. Lecture 24 ~ Segment 3 Group Comparisons t-tests and ANOVA 20
  21. 21. Group comparisons •  z-test •  Single sample t-test •  Independent t-test –  Homogeneity of variance assumption –  Levene’s test •  Dependent t-test (paired samples)
  22. 22. Group comparisons •  ANOVA: One-way between groups –  F = MSA = MSS/A –  Homogeneity of variance assumption –  Levene’s test –  Post-hoc tests
  23. 23. Group comparisons •  Factorial ANOVA –  Main effects –  Interaction effect –  Simple effects •  Homogeneity of variance assumption •  Levene’s test •  Post-hoc tests
  24. 24. Group comparisons •  Repeated measures ANOVA –  F = MSA = MSAxS –  Sphericity assumption –  Mauchly’s test –  Post-hoc tests
  25. 25. END SEGMENT 25
  26. 26. Lecture 24 ~ Segment 4 Procedures for non-normal distributions and non-linear models 26
  27. 27. Categorical outcome variables •  Chi-square tests •  Logistic regression
  28. 28. Non-normal distributions •  How to detect non-normal distributions –  Histograms and scatterplots –  Q-Q plots •  Common transformations –  Square root –  Logarithmic –  Inverse
  29. 29. Non-parametric statistics •  Wilcoxan’s ranking method •  Mann-Whitney U
  30. 30. Non-linear models •  Generalized Linear Model –  Binomial –  Multinomial –  Poisson
  31. 31. END SEGMENT 31
  32. 32. END LECTURE 24 32

×