Week 8

Question 1

What are the key features of correlation?

Answer 1

• has no response and explanatory variables
• has no model formula for predicting one
variable from the other
• both variables must be normally distributed
• ranges from -1 to 1 (with no units)

Question 2

What are the key features of regression?

Answer 2

• has a response variable (Y) and explanatory variable (X)
• has a model formula that predicts Y from X
• only Y must be normally distributed
• slope can be any value (in units of Y per X)

Question 3

Overall difference between correlation and regression?

Answer 3

correlation is simpler but regression is more useful and flexible

Question 4

What is α in linear regression?

Answer 4

is the line intercept (mean age when

black-on-nose is 0)

Question 5

What is β in linear regression?

Answer 5

is the line slope (change in age per

unit of change in black-on-nose)

Question 6

What is εi in linear regression?

Answer 6

εi (the residual) is the
difference between Yi
and the line

Question 7

What is the line of best fit?

Answer 7

line minimises differences, is called the line of best fit

Question 8

What is least squares?

Answer 8

is a method for estimating parameters of linear models

- by minimising the residual sum of squares (the sum of these green bits squared)

Question 9

What are the assumptions of linear regression?

Answer 9

A1. Y is linearly related to X (the linear model is appropriate)
A2. The distribution of Y values is normal at all values of X
A3. The variance of Y values is equal at all values of X
A4. Values of Y are randomly sampled at all values of X

Question 10

What are assumptions ANOVA?

Answer 10

same as linear but replace X with groups
A1. Y is linearly related to groups(the linear model is appropriate)
A2. The distribution of Y values is normal at all values of groups
A3. The variance of Y values is equal at all values of groups
A4. Values of Y are randomly sampled at all values of groups