Time Series Analysis Powerpoint (Nordstrom)

Part 1: Time Series analysis: discuss the revenue data similar  to what I have showed in the Sear example is expected: scatter plot of  revenue; linear trend; moving average; structural break. Your company  revenue may not exhibit strong seasonality. For practice purpose, you  are required to fit a moving average to your revenue data nevertheless. 

First explain what the graph tells you as a manager about the trend,  seasonality, and structural break in this company data. Then conduct  research to find out how management actually made the decisions in the  data.

 Note: you do not need to provide the graphs that cannot be obtained  using Excel (eg. the four figures shown in the second to last slide in  the Time Series Analysis ppt file)

Part 2: Linear regression using Macro data:

a. Select one variable from the macro data file that you believe can be highly correlated with the revenue data. 

b. Run a simple linear regression using the macro variable as the  independent variable and the revenue data as the dependent variable.  Report the regression results: show the excel output.

 c. Note that the dates for the revenue data and macro variables do  not necessarily match each other. You will need to determine the date  range that has available data for all variables and drop the extra data.  For example, if the date for GDP ranges from 2000 to 2015 and the  revenue data ranges from 1995 to 2016. Then you would only keep data  from 2000 to 2015.

 d. Comment on the significance of the slope coefficient. Briefly  discuss the relationship (positive or negative) between the dependent  variable and the independent variable. Is the relationship expected?

 e. Report R-squared and briefly comment on the fitness of the model based on your opinion.