1. For this series of questions you will use the data in the Excel file included to estimate a linear regression model in excel and interpret the results. The spreadsheet named sales data contains information about monthly sales revenue for a franchised chain of 75 food trucks named The Cheddar Chariot in the greater mid-Atlantic region along with monthly sales revenue per location (in 000s of US dollars), average retail price of their most popular retail item (The Cheese Louise), and advertising dollars spent (in 000s of US $s) in the region. I have included a codebook in the excel file describing what each variable is which should help. b. Interpret the coefficients you estimated in the model (constant, price of a cheese louise, and advertising dollars). For each of the dependent variables, tell me what a 1 unit increase in the value would cause to happen to the dependent variable. Be sure to take into account whether the coefficient is statistically significant in your description of the effect

c. Write the implied demand curve below based on your regression results (Monthly sales revenue = Estimated constant (Coefficient)p_cheeselouise).[1] d. Using the equation from the linear regression above, create a table containing price, quantity, total revenue, total cost, and profit that begins at cheese louise price = $0.50 and increases in $0.50 increments until it reaches $15.00. You believe that the cost function for your food truck business is approximately C(Q) = 100 + 0.5Q² (cost stated in thousands). Format this table to look professional, give it a title, and use consistent decimal places. Paste it here. e. What price/quantity combination will maximize profit? Inherently, given the structure of your problem your quantity sold is stated in thousands. What is the maximum total monthly profit? In dollars, what were you monthly sales and monthly cost of goods sold that generated that profit? f. Given what you know about the relationship between elasticity and profit maximization are you operating in the elastic, unit elastic, or inelastic portion of the demand curve? Why would a business always want to produce in this part of the demand curve?

\begin{tabular}{|l|l|c|c|c|c|} \hline Summary Statistics & & & & & \\ \hline Variable & Description & Mean & Std Dev & Min & Max \\ \hline Sales & in \$000s & 77.37467 & 6.488537 & 62.4 & 91.2 \\ \hline Goods sold price index & in \$'s & 5.6872 & 0.518432 & 4.83 & 6.49 \\ \hline Advertising expenditure & in \$000s & 1.844 & 0.831677 & 0.5 & 3.1 \\ \hline \end{tabular} Linear Regression Results Dependent Variable: Sales Revenue (\$) \begin{tabular}{|l|c|c|c|c|} \hline & \multicolumn{2}{|c|}{ Coefficients tandard Erro } & t Stat & P-value \\ \hline Sales & 118.9136 & 6.351638 & 18.72172 & 2.21E29 \\ \hline Goods sold price index & 7.907854 & 1.095993 & 7.215241 & 4.42E10 \\ \hline Adveritisng expenditure & 1.862584 & 0.683195 & 2.726283 & 0.008038 \\ \hline \end{tabular} \begin{tabular}{|l|l|c|c|c|c|} \hline Summary Statistics & & & & & \\ \hline Variable & Description & Mean & Std Dev & Min & Max \\ \hline Sales & in \$000s & 77.37467 & 6.488537 & 62.4 & 91.2 \\ \hline Goods sold price index & in \$'s & 5.6872 & 0.518432 & 4.83 & 6.49 \\ \hline Advertising expenditure & in \$000s & 1.844 & 0.831677 & 0.5 & 3.1 \\ \hline \end{tabular} Linear Regression Results Dependent Variable: Sales Revenue (\$) \begin{tabular}{|l|c|c|c|c|} \hline & \multicolumn{2}{|c|}{ Coefficients tandard Erro } & t Stat & P-value \\ \hline Sales & 118.9136 & 6.351638 & 18.72172 & 2.21E29 \\ \hline Goods sold price index & 7.907854 & 1.095993 & 7.215241 & 4.42E10 \\ \hline Adveritisng expenditure & 1.862584 & 0.683195 & 2.726283 & 0.008038 \\ \hline \end{tabular}