Flashcards in Statistics 2 - Regression Deck (27):

1

## What is the basic linear regression equation?

### y = mx + c

2

## What does each variable in the basic linear regression equation stand for?

###
y = outcome variable

m = slope

x = predictor variable

c = y intercept when x = 0

3

## What is the basic linear regression transformation taught in this course?

### y = B0 + B1.x1

4

## What does each element in the transformed linear regression stand for?

###
y = outcome variable

B0 = intercept

B1 = slope

x1 = predictor variable

5

## How might the slope be defined narratively?

### The change in y, for a one unit change in x.

6

## How might the y intercept be defined narratively?

### The value of y when x = 0.

7

## When x is a continuous variable, what interpretation does B1 take on?

###
B1 takes on a mean difference interpretation.

That is, for a mean difference between two subjects of x = 1 unit, the mean difference in y between the subjects is the slope.

8

## What are you calculating when you calculate the confidence interval for slope (B1)?

### Confidence interval for mean differences.

9

## What are you calculating when you calculate the confidence interval for the intercept (B0)?

### The CI for the mean of a single group.

10

## For what type of data is the CI for the y intercept (B0) useful and for what data is it less useful?

###
Useful for binary and categorical data.

Less useful for continuous data ie B0 may be a placeholder eg cannot have height of 0cm.

11

## What is the formal title for R^2?

### Coefficient of determination.

13

## What does R^2 measure? (Give short and long definitions)

###
The strength of linear association between x and y.

Amount of variability of data points around the regression line explained by y-values.

14

## What is r and what does it measure?

###
r = correlation coefficient

Measures strength and direction of relationship between x and y.

15

## What must you do to a log odds result for logistic regression to get an understandable actionable figure?

### Exponentiate the log odds result to gain an odds ratio for outcome y using predictor x.

16

## What is effect modification (interaction)?

### Effect modification occurs when the relationship between two variables (x and y), depends on the level of a third variable z.

17

## Can you test for Effect Modification through adjustment?

### No. Adjustment is used to test for Confounding.

18

## How do you test for Effect Modification?

### By comparing separate x-y estimates for different groups or levels of z eg Male vs Female, low vs med vs high

19

## What’s the mnemonic for calculating Risk from logistic regression results?

###
LETS LOPP branches

20

## What does each letter of the mnemonic for calculating Risk from Logistic Regression results stand for?

###
LETS LOPP branches

L = log y (OR) calculation for given x-value

E = exponentiate from log to normal

T = transform OR into P (= p/1+p)

S = switch P to %

Log

Odds

Prob

Percent

21

## What does a Spline summarise?

###
A non-linear relationship (a dog leg relationship).

Spline is ‘split-line’

22

## What is a Lowess plot?

### A “LOcally WEighted Scatter plot Smoothing” line through a scatter plot to allow trends and relationships to be visualised.

23

## What is a Hazard Ratio?

### A measure reported in TTE analysis summarising the chance of an outcome in a treatment group, compared to the control group.

24

## What is a Propensity Score?

### PROBABILITY of being in the EXPOSED group, given certain CONFOUNDER values

25

## What must the predictor be in a PROPENSITY SCORE calculation?

### Binary eg Exposed/Unexposed

26

## What can you do with a PROPENSITY SCORE?

###
Create quartiles of B1.

ie Probability of being Exposed, given population characteristics

Eg Māori ethnicity could be associated with damp home, and hospital admission. But all you’d see is hospital admission and think that ethnicity was the driver.

27

##
Subtraction of results of comparison results on the log scale is the same as what on the normal scale?

i.e log odds (A) - log odds (B)

### Division

28