# Lecture 8 and 9 (Correlation, Regression, CIs) Flashcards

1

Q

The dependent variable is on what axis?

A

Y-axis

2

Q

The independent variable is on what axis?

A

X-axis

3

Q

When should you use a correlation analysis?

A

- examine relationship between variables
- estimate strength of association between variables
- when independent and dependent variables are not clearly different
- when regression requirements not met

4

Q

A correlation coefficient of 0 means:

A

- there is no association between the two variables

5

Q

A regression is:

A

- how well data fits a line
- r-value close to 0 = no correlation
- r-value closer to 1 or -1 = high correlation
- r-squared tells you the amount of variation in Y that is contributed by variation in X.

6

Q

When should you use regression analysis?

A

- look for a trend in data between variables
- more than one X (independent) variable = multiple regression
- predict a dependent variable
- adjust for confounding variables
- curve fitting (pharmacokinetics)
- calibration and laboratory assays
- detect patterns in microarray data

7

Q

Regression r-value close to 0:

A

no association

8

Q

Regression r-value close to 1:

A

strong association

9

Q

Regression r-squared value tells you:

A

- the amount of variation in Y that is contributed by variation in X.

10

Q

Parametric test characteristics:

A

- assume variables are normally distributed with equal variances
- dependent on mean and variance
- susceptible to outliers
- requires continuous variables

11

Q

Non-parametric test characteristics:

A

- based on ranks
- distribution, variance, mean does not matter

12

Q

You can transform non-linear data to linear data by:

A

- taking logs

13

Q

Three ways you can control for outliers:

A

- using non-parametric test
- dropping the outlier(s)
- log transformation

14

Q

Multivariate regression:

A

- more than one X (independent) variable
- allows adjustment for confounders
- controls for variable interactions by multiplying variables together

15

Q

Stepwise regression:

A

- finds the top contributing variable, then the second, then the third, etc. until a point of diminishing returns is reached.
- a.k.a finds the group of variables that has the largest collective r-squared value.