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You own a company that raises cattle to sell for beef. Yourcompany needs to forecast sales for the next year to purchase rawmaterials and plan production. You have a pretty good qualitativegrasp of the key causal variables that influence sales quantity butlack quantitative estimates of each variable’s impact on sales. So,you collect historical data on monthly per capita beef consumption(dependent variable) and the causal variables you have identified(price of beef and related meats, household income, price). Usingregression analysis, you calculate this relationship. For salesquantity, Q, your data represents pounds per capita; for price, P,its the unit price in dollars; income (I) is the average householdincome in $1000s (e.g., I = 10 implies average income of $10,000).You generate the following regression equation: Q = 1.24 – 0.23 PB+ 0.24 PP + 1.18 PC + 0.24 Y (0.34) (-0.14) (0.11) (0.42) (0.09)where the standard errors are in parentheses. PB is the price ofbeef, PP is the price of pork, PC is the price of chicken, and Y ishousehold income. The R-square value for this regression estimationis 0.83. You should use a critical value of t = 1.96 in thefollowing questions. a. What does the regression equation tell you?Why is it used in economics? b. Are the above regressioncoefficients significant? Explain. c. Interpret the R-square valueof the regression. What does it imply?