1. A travel agency has offices in 12 cities. Management believes that monthly airline bookings are related to the mean income in those cities ("Travel agency data.xlsx")
2. Fit a regression model to forecast bookings. Provide the equation of the fit line and interpret the model parameters in the context of the problem.
3. Evaluate the regression model
4. What are the forecasted monthly bookings for a city with an average income of $47,250?

1. Quarterly sales ($ thousands) and income ($ billions) were tracked between March 2007 and December 2016 ("Sales income data.xlsx").
2. Using the first 32 observations as a training set, fit a regression model to forecast quarterly sales for next year using income. Provide the equation of the fit line
3. Evaluate the regression model
4. Check the regression model assumptions
5. How well is the model fit?
6. How accurate is the model?

1. Monthly shoe sales (millions of dollars) were collected between January 2002 and December 2005 ("Shoe sales data.xlsx")
2. Using the appropriate plots, how would you characterize the data? Explain
3. Build a regression model to forecast sales
4. Deseasonalize sales and build a regression model to forecast sales
5. What is the seasonal index of the month of August? Interpret its meaning
6. Using R², MAPE and RMSE, compare the fit of the models in part (b) and (c)