Task 1: Trend Line 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 expect 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 the 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 economic 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 economic 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?

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