Simple Linear regression

Question 1

What is regression?

Answer 1

A way to study relationships between variables

Question 2

What is the assumption of linear models?

Answer 2

Constantly increasing or decreasing relationships between each explanatory variable and the response

Question 3

What is the general form of a simple linear model?

Answer 3

Y= Bo + B1X + E

Question 4

What is Bo in linear regression?

Answer 4

The intercept of the regression line

Question 5

What is B1 in linear regression?

Answer 5

The gradient of the regression line

Question 6

What is E in linear regression?

Answer 6

The Error term (assumed normally distributed in the y direction)

Question 7

What are the hypothesises of the simple linear model?

Answer 7

Ho: The gradient = 0
H1: The gradient does not = 0

Question 8

What is the name given to the vertical distances between observed data and the line of best fit?

Answer 8

Residuals

Question 9

What is the least squares criterion?

Answer 9

• A fitted line that minimises the summed squares of the residuals
• The value is given by the sum of the data value minus the model value all squared

Question 10

What is the model that can be used for predicting y given any x in simple linear regression?

Answer 10

Y = Bo + B1X