Scenario: In this scenario, we return to the data from the American Time Use Survey (TimeUse). Our data table contains observations from approximately 16,000 respondents in 2003 and 2007. Use the Data Filter to select and include just the 2003 responses.

a. Complete the steps necessary to create a shadowgram of the Sleeping data

(that is, time spent sleeping). Paste a copy of the graph in your report and comment on what you see.

b. Construct a normal quantile plot for the Sleeping column. Do you think that a normal model is suitable for this variable? Explain.

c. Repeat steps a and b for the column containing the ages of the respondents.

6. Scenario: In this scenario, we return to the FTSE100 data table, with values of the London Stock Exchange index. We’ll focus on the daily closing value of the index, the daily percentage change, and the daily volume (number of shares traded).

a. Use the Distribution platform to summarize the daily closing values and the daily percentage change. Comment on interesting features of these two distributions.

b. The daily percentage changes are calculated from the data in the Close column, and clearly are much better approximated by a normal model than are the daily closing figures. In fact, computing percentage changes is one strategy that analysts use to transform time series into a nearly normal variable. Construct NPPs for both Close and Change%, and report on your findings.

c. Would a normal model be an apt description of the daily volume variable?

If so, which normal model fits this variable?