quiz 5

Question 1

heart disease description

Answer 1

• is the leading cause of death in the US
• risks vary by sex, age, and ethnicity
• linked to physical activity levels and diet

Question 2

when do you use a bivariate linear regression?

Answer 2

• when you have 2 variables
• should fit a generally linear relationship

Question 3

bivariate linear regression

Answer 3

• explains the sample by describing a line
• model is built by parameterizing, or calculating m and b

Question 4

what are some ways to model relationships between variables

Answer 4

• measure samples to understand population
• build model to explain relationship for sample
• use model to predict or extrapolate the values of untested individuals

Question 5

what are two linear regression techniques?

Answer 5

• bivariate
• multiple linear regression

Question 6

bivariate

Answer 6

• one predictor
• BMI
• used together to predict variation in a “response”

Question 7

multiple linear regression

Answer 7

• more than one predictor
• Age, BMI, LDL-C

Question 8

what are the 3 questions to ask about a regression model?

Answer 8

• What is the calculated model?
• How much variation in the data does it explain? (R square value)
• does it explain a significant amount of the variation? (F&p-value)

Question 9

more parameters=

Answer 9

model is better at predicting the noise in your data

Question 10

what does each parameter have associated with it?

Answer 10

error

Question 11

Real world dirty approach

Answer 11

includes only significant predictors

Question 12

real world good approach

Answer 12

Akaike Information Criterion (AIC)
- runs from 1 to -1
-1=did something wrong
1= great
0= not as efficient

Question 13

regression

Answer 13

• a model building technique that uses sample to build a mathematical model
• linear regression produces a model 0of a straight line

Question 14

R^2

Answer 14

a measure of model fit
- should use adjusted form if you have more than one predictor