For my regression I observed food consumption rates in dollars, plotted against income level as the explanatory variable. I used a measurement of income before taxes separated into 9 different brackets: <\$15,000, <\$30,000, <\$40,000, <\$50,000, <\$70,000, <\$100,000, <\$150,000, <\$200,000, and <\$250,000. I ran a cross sectional regression to observe changes in food consumption due to increases in annual income. For a second regression, I included alcohol consumption in dollars to observe its effect on food consumption under the same income brackets.

In my first test, I found that each additional dollar of income will lead to a \$0.04 dollar increase in food consumption. For my second model, I found that each additional dollar spent on alcohol consumption causes a \$1.95 decrease in spending on food, holding the other variable (income) constant. My R squared statistics of .993 and .995 (respectively) suggests this regression, and its independent variables, hold a high strength of explanation for the dependent variable (food consumption). These findings align with our common assumptions about food, that it is a highly normal good.

Output information is presented below.

 The SAS System

The REG Procedure
Model: MODEL1
Dependent Variable: Food
 Number of Observations Read 9 9

Analysis of Variance
Source DF Sum of
Squares
Mean
Square
F Value Pr > F
Model 1 133051467 133051467 1059.56 <.0001
Error 7 879010 125573
Corrected Total 8 133930477

 Root MSE R-Square 354.363 0.9934 8111.89 0.9925 4.36844

Parameter Estimates
Variable DF Parameter
Estimate
Standard
Error
t Value Pr > |t|
Intercept 1 3126.16650 193.42381 16.16 <.0001
Inc 1 0.04958 0.00152 32.55 <.0001
 The SAS System
The REG Procedure
Model: model1
Dependent Variable: Food
 Number of Observations Read 9 9

Analysis of Variance
Source DF Sum of
Squares
Mean
Square
F Value Pr > F
Model 2 133279356 66639678 614.08 <.0001
Error 6 651121 108520
Corrected Total 8 133930477

 Root MSE R-Square 329.424 0.9951 8111.89 0.9935 4.061

Parameter Estimates
Variable DF Parameter
Estimate
Standard
Error
t Value Pr > |t|
Intercept 1 3154.80047 180.89387 17.44 <.0001
Inc 1 0.06212 0.00877 7.08 0.0004
Alc 1 -1.95538 1.34935 -1.45 0.1975
SAS Assignment observing food consumption patterns in an Engel Curve