4.2 Linear regression

Question 1

What is regression?

Answer 1

» The act of setting paramaters on our model
» Such as y = a + bx
» Where a and b have been restricted to fit the model

Question 2

What is the least squares regression line?

Answer 2

» This minimises the sum of the square of these errors
» Squaring these values treat each erros as a positive value

Question 3

What do you do if it asks you to interpret the value of the gradient?

Answer 3

» Talk about what the gradient represents
» For example if the gradient was 2 on a distance, time graph
» The interpretation would be for every minute, the distance increases by 2 metres

Question 4

Justify the use of a linear regression line

Answer 4

» Data is represented in a linear way
» Therefore there is a linear relationship
» A stronger correlation means the more suitable a linear regression line is

Question 5

How can we interpret the y intercept?

Answer 5

» The amount of y value at 0 value on x
» Refer to context

Question 6

When should you only use the regression line to make predictions?

Answer 6

» For the values of the dependent variable that are within the range of the given data

Question 7

What is interpolating?

Answer 7

» Estimating a value inside the data range

Question 8

What is interpolating?

Answer 8

» Estimating a value inside the data range

Question 9

What is extrapolating?

Answer 9

» Esimating a value outside the data range