Regression

Question 1

Regression modelling steps

Answer 1

Define problem or question

Specify model

Collect data

Do descriptive data analysis

Estimate unknown parameters

Evaluate model

Use model for prediction

Question 2

Regression model is used to

Answer 2

Predict the value of a dependent variable based on the value of at least one
independent variable

Explain the impact of changes in an independent variable on the dependent
variable

Question 3

A low correlation coefficient

Answer 3

gives a flatter slope

Question 4

Large spread of Y, i.e. high standard deviation in y,

Answer 4

results in a
steeper slope (high value of a)

Question 5

Large spread of X, i.e. high standard deviation in x,

Answer 5

results in a
flatter slope (high value of a)

Question 6

The smaller the correlation,

Answer 6

the closer the
intercept is to the mean of y

Question 7

Assumption of regression line

Answer 7

Linearity

The relationship between X and Y is linear

Independence of Errors

Error values are statistically independent

Particularly important when data are collected over a period of time

Normality of Error

Error values are normally distributed for any given value of X

Equal Variance (also called homoscedasticity)

The probability distribution of the errors has constant variance

Question 8

General linear model

Answer 8

any model that describes the data in
terms of a straight line

Question 9

Akaike information criterion

Answer 9

based on the concept of selecting the
model that produces a probability distribution that is closest to the (yet unknown)
true distribution