NOTE: Submit Excel/SAS file for your computation AND a Word file summarizing your answers to the questions as a managerial report (that includes your comments, graphs and decisions).

Explore the relationship between the selling prices (Y) and the appraised values (X) of the 148 homes in the file by estimating a simple linear regression model. Interpret the standard error of estimate and and the least squares line for these data.

Is there evidence of a linear relationship between the selling price and appraised value? If so, characterize the relationship. Is it positive or negative? Is it weak or strong?

For which of the three remaining variables, the size of the home, the number of bedrooms, and the number of bathrooms, is the relationship with the home's selling price stronger? Justify your choice with additional simple linear regression models. (Page 441 Q1)

Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for several randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file.

Estimate an appropriate multiple regression equation to predict the cost of shipping a given package.

Interpret the estimated regression coefficients. You should find that the estimated intercept and slope of the equation are sample means. Which sample means are they?

According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship?

How well does the estimated equation fit the given sample data? How do you think the model's goodness of fit could be improved?

Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo. (Page 468-469 Q28)

Does a linear trend appear to fit these data well? If so, estimate and interpret the linear trend model for this time series. Also, interpret the and values.

Provide an indication of the typical forecast error generated by the estimated model in part 1.

Is there evidence of some seasonal pattern in these sales data? If so, characterize the seasonal pattern. (Pg. ?? Q9)

Consider the airline ticket data in the file P12_01.xlsx.Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think should be used for forecasting? Why?

Use simple exponential smoothing to forecast these data, using no holdout period and requesting 12 months of future forecasts. Use the default smoothing constant of 0.1.

Repeat part 1, optimizing the smoothing constant. Does it make much of an improvement?

Write a short report to summarize your results. (Pg. ?? Q32)