• Bond University

• ECON 12-200

Definitions

Regression:

It is concerned with describing and evaluating the relationship between a given variable (usually

called the dependent variable) and one or more other variables (usually known as the independent

variable(s)).

Correlation:

Negative: relationship between 2 variables in which one variable increases as the other decreases.

Positive: as one increases the other increases

Zero correlation: there is no linear relationship between the two variables.

Deviation, mean, beta

´

´

=

X ,

Y

population mean

´

´

X

−

X ,Y

−

Y

=

Deviation

¿

mean

, sum of this is always = 0

^2 above and you get the mean squared error; mean squared error

is the measure of how actually

the predicted values are different from the actual values.

β

it doesn't really exist in practice

1

^

β

β

the estimate of

based on the sample.

1

1

Unbiased:

On average, the actual value of the and 's will be equal to the true values.

^

y

=

b

+

b

x

; for a given value of x = X what do we expect the Y value to be?

1

2

^

e errorterm

=

y

−

^

y

, sum of error = 0

^

Sum of

2

e

istheleast squareapproach

The Standard Error of a regression is a measure of its variability.

It can be used in a similar manner

to standard deviation, allowing for prediction intervals.

The proportion of total variation (SST) that is explained by the regression (SSR) is known as the

Coefficient of Determination, and is often referred to as R^2.

β

2