Regression

-

100

This approach is used to fit the simple linear regression model

Least Squares Approach

200

It is assumed that the sum of the errors is equal to this value

0

300

This variable is assumed to be independently and identically distributed, following a normal distribution with mean 0 and constant standard deviation σ

ε

400

If the coefficient of correlation is negative this type of relationship exists between x and y

Negative Linear Relationship

-

100

This is a measure of the strength of the linear relationship between two variables x and y

Coefficient of Correlation

200

This variable represents the change in y for every one unit increase in x

β₁

300

The correlation coefficient can take values in this range

[-1, 1]

400

This variable represents the predicted value of y when x=0

β₀

-

100

This formula provides an estimate of the variance for a simple linear regression model

SSE / (n-2)

200

This represents the proportion of sample variation in y that is explained by the linear relationship between x and y

Coefficient of Determination

300

This is an estimate of the range in which a future observation will fall

Prediction Interval

400

The coefficient of determination can take values in this range

[0, 1]