# Week 8 Flashcards

1
Q

What are the key features of correlation?

A

• 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)

2
Q

What are the key features of regression?

A
• 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)
3
Q

Overall difference between correlation and regression?

A

correlation is simpler but regression is more useful and flexible

4
Q

What is α in linear regression?

A

is the line intercept (mean age when

black-on-nose is 0)

5
Q

What is β in linear regression?

A

is the line slope (change in age per

unit of change in black-on-nose)

6
Q

What is εi in linear regression?

A

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

7
Q

What is the line of best fit?

A

line minimises differences, is called the line of best fit

8
Q

What is least squares?

A

is a method for estimating parameters of linear models

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

9
Q

What are the assumptions of linear regression?

A

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

10
Q

What are assumptions ANOVA?

A

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