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6. What standardised residual is worth checking as an outlier?
- They're all worth checking, you can never be too sure
- >2
- >3
- >1
7. What's a type 2 error
- Clara
- False positive
- False negative
- Elisa
8. What should you be careful of with hierarchical multiple regression
- It is not as good as backwards multiple regression
- Order of entry of predictors may affect the model
- It only works if a large number of predictors is entered
- I can't think of a 4th answer
9. What is an OK range for kurtosis and skew
- Less than plus or minus 1
- Less than plus or minus 2
- As close to 2 as possible
- Less than 1
10. Simpson's paradox, When a trend appears in several groups of data but disappears when they are combined, can be caused by
- The wrong analysis being performed
- Not having a control
- confounding variables
- Small sample size
11. PLus or minus 3.3 standard errors is equal to what percentage CI?
12. If ...% of residuals are >2 the model might be a poor fit
13. How many standard errors cover 99% CI?
- plus or minus 2.6
- plus or minus 3.3
- plus or minus 3
- plus or minus 1
14. What can't you predict wth multiple linear regression
- All of these
- Whether Maddie McCann will ever be found
- Whether I will pass my stats exam
- Categorical things such as alive vs dead
15. What is the problem with forwards and backwards variable selection?
- They both lead to false negatives, though forwards is a bit better
- They both lead to false positives, though forwards is a bit better
- They both lead to false positives, though backwards is a bit better
- They both lead to false negatives, though backwards is a bit better
16. If SEMs for two groups DO NOT overlap what can you conclude about p?
- It could be anything
- p is a man-made construct and is as futile as our existence
- It has to be smaller than 0.05
- It has to be bigger than 0.05
17. Which of these statements regarding colinearity is false?
- Tolerance factor of more than 0.01 is problematic
- When there is colinearity, very little variance is explained by one factor alone
- As colinearity increases so does the standard error of b coefficients
- VIF above 10 or average VIF above 1 helps you spot colinearity
18. What is the likelihood that the population mean lies within plus or minus 1 standard error
- 175%
- 68%
- Needs more information to be answered
- 95%
19. Model 1 has an R2 of 0.32 and model 2 has an R2 of 0.54, how much more of the variance can model 2 explain