Lecture 6: Regression Analyses

Question 1

Which parametric test do I do if both the dependent and the independent variables are continuous?

Answer 1

linear regression

Question 2

Which parametric test do I do if the dependent variable is categorical and the independent variables is continuous?

Answer 2

Logistic regression

Question 3

Which parametric test do I do if the dependent variable is continuous and the independent variables is categorical?

Answer 3

ANCOVA

Question 4

Which non-parametric test do I do if both the dependent and the independent variables are continuous?

Answer 4

Non-parametric regression

Question 5

Which non-parametric test do I do if the dependent variable is categorical and the independent variables is continuous?

Answer 5

Non-parametric regression

Question 6

Which non-parametric test do I do if the dependent variable is continuous and the independent variables is categorical?

Answer 6

Non-parametric regression

Question 7

Simple linear regression

Answer 7

one dependent variable and one independent variable

Question 8

Multiple linear regression

Answer 8

one dependent variable and one independent variable

Question 9

Bo (y-intercept)

Answer 9

point where regression line crosses the y-axis

Question 10

B1 (slope)

Answer 10

tells you how much y changes as you move along the values of x

Question 11

What is the null hypothesis in regression?

Answer 11

that B1=0

a straight line meaning that there is no relationship between your X and Y values

Question 12

What does the R squared value tell us?

Answer 12

The R squared value is telling a proportion of variance that is explained in your model. So how much variability in your outcome is explained by your X

For example, if the R squared value is 0.82 (82%), then the model is 82% accurate and this is a strong relationship. For 1 unit increase in the independent variable, the dependent variable changes by 0.82 units.

Question 13

What does the B value tell us?

Answer 13

B value is the slope so every 1 unit increase in independent variable there is an increase in the dependent variable of that B value.

Question 14

What is the least squares criterion?

Answer 14

method of minimizing the sum of squared residuals in a model

Question 15

What is the equation for total sum of squares?

Answer 15

Total sum of squares = explained sum of squares + residual sum of squares

Question 16

What are the assumptions of linear regression?

Answer 16

• residuals are normally distributed
• linear relationship between dependent and independent variables
• no extreme outliers
• ample sample size for variables in the model (at least 10 cases per independent variable)
• no multicollinearity between independent variables (predictors) (occurs when there is too strong correlation between independent variables (p>0.7)

Question 17

What are confounders?

Answer 17

a variable that is associated with an exposure, and independent of that association, is also a risk factor for an outcome. Distorts estimate of association between independent and dependent variable.
- can be controlled for in statistical analyses, but only if the variable has been measured during data collection!

Question 18

How to identify confounders?

Answer 18

General approach: conduct linear regression between a predictor variable and an outcome variable.
- repeat the regression with the potential confounder (covariate) now included in the model

Question 19

Variable considered a confounder if:

Answer 19

• a p-value that was initially significant is attenuated

- B coefficient for your main predictor variable changes by more than 10%

Question 20

What is residual confounding?

Answer 20

Residual confounding is the distortion that remains after controlling for confounding in the design and/or analysis of a study.

Question 21

Dummy variable

Answer 21

method of dichotomizing variables

Question 22

What is odds ratio?

Answer 22

This is a ratio of ratio (odds) it is the odds that someone who has been exposed to the risk factor actually has the disease compared to the odds that someone who has not been exposed to the risk factor has the disease

Question 23

What is multicolinearity? What are the consequences?

Answer 23

occurs when there is too strong correlation between independent variables (p>0.7). They are measuring the same thing
EX: BMI & waist circumference
we only adjust one of them in the model otherwise you are measuring the same thing and you will have inflated statistical results and maybe overestimating the significance of the relationship between your variables.

Question 24

Which test do I do if my main predictor variable is categorical?

Answer 24

ANCOVA

Question 25

Which test do I do if my outcome variable is categorical and dichotomous?

Answer 25

Binary logistic regression

Question 26

Which test do I do if my predictor is either categorical or continuous but my outcome is always continuous?

Answer 26

Linear regression

Question 27

What do I have to do to my independent categorical variable before I do a linear regression?

Answer 27

dichotomize the categorical variable

Question 28

What is least square mean?

Answer 28

Mean that has been adjusted for covariates.