Use computer software to obtain the regression equation. Use the estimated equation to find the predicted value
-A study of food consumption in the country related the level of food consumed to an index of food prices and an index of personal disposable income. $\begin{array}{ccc}
\hline\text { FOODCONS } & \text { INCOME } & \text { PRICE } \
\hline 98.6 & 87.4 & 108.5 \
101.2 & 97.6 & 110.1 \
102.4 & 96.7 & 110.4 \
100.9 & 98.2 & 104.3 \
102.3 & 99.8 & 107.2 \
101.5 & 100.5 & 105.8 \
101.6 & 103.2 & 107.8 \
101.6 & 107.8 & 103.4 \
99.8 & 96.6 & 102.7 \
100.3 & 88.9 & 104.1 \
97.6 & 75.1 & 99.2 \
97.2 & 76.9 & 99.7 \
97.3 & 84.6 & 102.0 \
96.0 & 90.6 & 94.3 \
99.2 & 103.1 & 97.7 \
100.3 & 105.1 & 101.1 \
100.3 & 96.4 & 102.3 \
104.1 & 104.4 & 104.4 \
105.3 & 110.7 & 108.5 \
107.6 & 127.1 & 111.3\\hline
\end{array}$
A) $ .912, .901, .010 $
B) $ .867, .852, .000 $
C) $ .843, .799, .005 $
D) $ .855, .844, .002 $

Question

Quizplus · Accepted Answer

Given the data and the task of using computer software to obtain a regression equation, the correct answer would typically involve statistical analysis to determine the relationship between food consumption (FOODCONS) and the two independent variables: income (INCOME) and price (PRICE). The coefficients provided in option B (.867 for INCOME and .852 for PRICE, with a p-value of .000) suggest a statistically significant relationship between both independent variables and the dependent variable (food consumption), assuming these values are interpreted as regression coefficients and a p-value respectively. The exact values and their interpretation would depend on the output of the regression analysis performed by the software, which is not provided here. However, option B suggests a strong and statistically significant relationship, which is why it is chosen based on the typical output of regression analysis in statistical software.

Use Computer Software to Obtain the Regression Equation A) .912,.901,.010 .912, .901, .010 .912,.901,.010

Use Computer Software to Obtain the Regression Equation A) $.912, .901, .010$