Stats 2

Question 1

1) What does bivariate analysis mean?

Answer 1

1) looking at relationships between TWO variables

Question 2

2) What is the regression coefficient

Answer 2

2) y=a+bx. b is regression coefficient, measure of the gradient

Question 3

3) A regression coefficient of 0 means?

Answer 3

accept null hypothesis

Question 4

4) What is correlation ?

Answer 4

4) measures the degree of linear association, between -1 and +1

Question 5

5) What does a correlation coefficient of -0.8 mean

Answer 5

5) strong negative correlation

Question 6

6) One positive of regression coefficient over correlation coefficient

Answer 6

6) can directly predict the outcome given the exposure

Question 7

When would you use a correlation coefficient

Answer 7

7) when outcome and exposure cant be distinguished – one doesn’t cause other one e.g. weight and height

Question 8

8) Give an example of descriptive analysis

Answer 8

8) mean, median, standard deviation

Question 9

9) When do you need more complicated analysis

Answer 9

9) when you’re comparing more than two groups

Question 10

10) What is ANOVA

Answer 10

10) analysis of the variance – used when theres more than two groups (extension of t-test)

Question 11

11) When do you use multiple logistic regression and multiple linear regression

Answer 11

11) when theres more than two variables. Logistic when the outcomes are binary and linear when the outcomes are continuous.

Question 12

12) Describe adjusting for cofounders

Answer 12

12) Result of study is effected by result of relating factor. e.g. testing for coffee and lung cancer (lots of coffee drinkers smoke so this could effect results)

Question 13

13) What form does logistic regression give its output in and why ?

Answer 13

odds ratios as is categorical

Question 14

14) Describe type one error

Answer 14

14) false rejection of true null hypothesis (false positive)

Question 15

15) Describe type two error

Answer 15

15) wrong acceptance of false null hypothesis

Question 16

16) Why does type 2 error often occur

Answer 16

16) inadequate sample size . Not enough power for stat sig

Question 17

17) What is r(squared)

Answer 17

17) percentage of variation in outcome which can be accounted for by exposure variable

Question 18

18) How do you work out sample size and power

Answer 18

18) need to know variance and SD and what clinically significant effect size you’re looking for. Put into the formula and tells you power

Question 19

19) When would you lower the p value and why

Answer 19

19) if you do loads of stats test by chance 5/100 of them would be statistical significant so need to lower p incase occurs by chance – multiple outcome measures

Question 20

20) Why is multiple subgroup analysis bad

Answer 20

20) if you have lots of subgroups then more likely to get a statistically significant result as doing more separate tests

Question 21

21) what is the secondary hypothesis often related to

Answer 21

21) comparing subgroups

Question 22

22) what is measure of effect size also known as

Answer 22

odds ratio