REGRESSION MODEL WITH TWO EXPLANATORY VARIABLES

MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
Y
X2
X1
β0
1
This sequence provides a geometrical interpretation of a multiple regression model with two explanatory variables.
Y – weekly salary ($)
X1 – length of employment (in months)
X2 – age (in years)
Specifically, we will look at weekly salary function model where weekly salary, Y, depend on length of employment X1, and age, X2.

MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
Y
X2
X1
β0
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The model has three dimensions, one each for Y, X1, and X2. The starting point for investigating the determination of Y is the intercept, β0.
Y – weekly salary ($)
X1 – length of employment (in months)
X2 – age (in years)

MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
Y
X2
X1
β0
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Literally the intercept gives weekly salary for those respondents who have no age (??) and no length of employment (??). Hence a literal interpretation of β0 would be unwise.
Y – weekly salary ($)
X1 – length of employment (in months)
X2 – age (in years)

MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
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Y
X2
The next term on the right side of the equation gives the effect of X1. A one month of employment increase in X1 causes weekly salary to increase by β1dollars, holding X2 constant.
X1
β0
pure X1 effect
β0 + β1X1
Y – weekly salary ($)
X1 – length of employment (in months)
X2 – age (in years)

pure X2 effect
MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
X1
β0
β0 + β2X2
Y
X2
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Similarly, the third term gives the effect of variations in X2. A one year of age increase in X2 causes weekly salary to increase by β2 dollars, holding X1 constant.

pure X2 effect
pure X1 effect
MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
X1
β0
β0 + β2X2
β0 + β1X1 + β2X2
Y
X2
β0 + β1X1
combined effect of X1 and X2
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Different combinations of X1 and X2 give rise to values of weekly salary which lie on the plane shown in the diagram, defined by the equation Y = β0 + β1X1 + β2X2. This is the nonrandom component of the model.

pure X2 effect
pure X1 effect
MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
X1
β0
β0 + β2X2
β0 + β1X1 + β2X2
β0 + β1X1 + β2X2+ ei
Y
X2
combined effect of X1 and X2
e
8
The final element of the model is the error term, e. This causes the actual values of Y to deviate from the plane. In this observation, e happens to have a positive value.
β0 + β1X1

pure X2 effect
pure X1 effect
MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
X1
β0
β0+ β1X1+ β2X2
β0 + β1X1 + β2X2 + e
Y
X2
combined effect of X1 and X2
e
9
A sample consists of a number of observations generated in this way. Note that the interpretation of the model does not depend on whether X1 and X2 are correlated or not.
β0 + β1X1
β0 + β2X2

pure X2 effect
pure X1 effect
MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
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X1
β0
β0 + β1X1+ β2X2
β0 + β1X1 + β2X2+ e
Y
X2
combined effect of X1 and X2
e
However we do assume that the effects of X1 and X2 on salary are additive. The impact of a difference in X1 on salary is not affected by the value of X2, or vice versa.
β0 + β1X1
β0 + β2X2

Slope coefficients are interpreted as partial slope/partial regression coefficients:
? b1 = average change in Y associated with a unit change in X1, with the other independent variables held constant (all else equal);
? b2 = average change in Y associated with a unit change in X2, with the other independent variables held constant (all else equal).
MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES

MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
The regression coefficients are derived using the same least squares principle used in simple regression analysis. The fitted value of Y in observation i depends on our choice of b0, b1, and b2.
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MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
The residual ei in observation i is the difference between the actual and fitted values of Y.
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MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
We define SSE, the sum of the squares of the residuals, and choose b0, b1, and b2 so as to minimize it.
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MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
First we expand SSE as shown, and then we use the first order conditions for minimizing it.
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MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
We thus obtain three equations in three unknowns. Solving for b0, b1, and b2, we obtain the expressions shown above.
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MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
The expression for b0 is a straightforward extension of the expression for it in simple regression analysis.
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MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
However, the expressions for the slope coefficients are considerably more complex than that for the slope coefficient in simple regression analysis.
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MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES
For the general case when there are many explanatory variables, ordinary algebra is inadequate. It is necessary to switch to matrix algebra.
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In matrix notation OLS may be written as:
Y = Xb + e
The normal equations in matrix form are now
XT Y = XTXb
And when we solve it for b we get:
b = (XTX)-1XTY
where Y is a column vector of the Y values and X is a matrix containing a column of ones (to pick up the intercept) followed by a column of the X variables containing the observations on them and b is a vector containing the estimators of regression parameters.
MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES

MATRIX ALGEBRA: SUMMARY
A vector is a collection of n numbers or elements, collected either in a column (a column vector) or in a row (a row vector).
A matrix is a collection, or array, of numbers of elements in which the elements are laid out in columns and rows. The dimension of matrix is n x m where n is the number of rows and m is the number of columns.
Types of matrices
A matrix is said to be square if the number of rows equals the number of columns.
A square matrix is said to be symmetric if its (i, j) element equals its (j, i) element.
A diagonal matrix is a square matrix in which all the off-diagonal elements equal zero, that is, if the square matrix A is diagonal, then aij =0 for i≠j.
The transpose of a matrix switches the rows and the columns. That is, the transpose of a matrix turns the n x m matrix A into the m x n matrix denoted by AT, where the (i, j) element of A becomes the (j, i) element of AT; said differently, the transpose of a matrix A turns the rows of A into the columns of AT. The inverse of the matrix A is defined as the matrix for which A-1A=1. If in fact the inverse matrix A-1 exists, then A is said to be invertible or nonsingular.
Vector and matrix multiplication
The matrices A and B can be multiplied together if they are conformable, that is, if the number of columns of A equals the number of rows of B. In general, matrix multiplication does not commute, that is, in general AB≠ BA.

MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES: EXAMPLE
Data for weekly salary based upon the length of employment and
age of employees of a large industrial corporation are shown in the table.
Calculate the OLS estimates for regression coefficients for the available sample. Comment on your results.

MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES: EXAMPLE
Y-weekly salary ($) X1 –length of employment (months) X2-age (years)

MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES: EXAMPLE

MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES: EXAMPLE
Y-weekly salary ($) X1 –length of employment (months) X2-age (years)
Our regression equation with two predictors (X1, X2):

MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES: EXAMPLE
These are our data points in 3dimensional space (graph drawn using Statistica 6.0)
X1
X1
Y
X2

MULTIPLE REGRESSION WITH TWO EXPLANATORY VARIABLES: EXAMPLE
Data points with the regression surface (Statistica 6.0)
X1
X2
Y
b0