Problem description:
If you are the manager of a grocery company with 50 stores across the city. The Excel file contains data on the demand for grocery delivery:

• Delivery (Y) is the number of delivery per month
• Price (X1) is the average price of a delivery
• Income (X2) is the average income in the neighborhood

You are required to use the Microsoft Excel package (the data analysis section) to finish the specific ask below. Data on income of neighborhood is taken from the ABS.

Task 1: Trend Line (Curve-Fitting) Analysis 
Plot the scatter plot for #Delivery (y-axis) and Income (x-axis). Add the trendline with the equation and R-square. Interpret the R-square. comment on the slope coefficient. Is the estimated slope coefficient expected and consistent with economic principles? Suggests one use or application for
this information. You are considering opening another store at a location where the average income is $50,000, how many deliveries per month can you expected at this new location? What is the income elasticity of this new location?

Task 2: Simple Regression 
Estimate the regression model for #delivery (Y) against price (X1). Interpret the estimated value of the coefficient for price. Is estimated slope coefficient expected and consistent with economic principles? What are the values of the 95% confidence interval for price and what does it mean? Plot and comment on the residuals

Task 3: Multiple Regression 
Estimate the regression model for #delivery (Y) against price (X1) and income (x2). What does the Rsquare say about the model? Why is the R-square in this model higher than the model in task 2 (both in terms of statistical and economics principles? Comment on the coefficients of price and income.
Why is the coefficient of price different in this model than the one in task 2 (both in terms of statistical and economics principles? What is the substitution rate/ratio or trade-off between income and price, keeping quantity demanded constant? Besides price and income, suggest one additional variable that you may use to estimate #delivery based on economic principles? Where or how can you get the data for this new variable?