The Theory Of Regression Results

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In order to empirically examine the extent to which changes in price causes changes in broiler (chicken) consumption, time-series data was collected on 40 observations extending from 1960 to 1999 on nine explanatory variables in total. I regressed this data using an Ordinary-Least Squares (OLS) approach. The main explanatory variable used for my analysis was the consumer price index (CPI) for whole fresh chicken with the base years denoted as the 1982 to 1984 time period. The econometric model for my regression analysis is listed below:

Broiler Consumption = a+β_(1 ) (CPI of broilers)+ β_2 (income)+ β_3 (population)+β_(4 ) (CPI for beef)+β_(5 ) (PPI for corn)+ β_(6 ) (NPI for broiler feed)+β_7 (CPI)+β_8 (AP of Broilers)+β_9 (population)+β_(10 ) (exports of beef,veal and pork)+ ε

Data regarding my dependent variable was originally collected as pounds consumed per capita. However, I multiplied the per capita broiler consumption data by 100,000 to obtain broiler consumption in pounds per 100,000 people. In this way, I was able to obtain a larger coefficient on a few of the variables, which allowed for a more suitable display and discussion of the results in the paper. Further, the objective of this analysis was to explain to what extent price of broilers (CPI on whole fresh chicken) impacts broiler consumption in pounds. Specifically, the coefficients on the explanatory variables illustrate their respective effects on the number
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