The focus of this project is to download a data set and manipulate it to work around missing data.

a. First, download Data Set 3 “Body Temperatures” in Appendix B from TriolaStats.com. Choose the download format that matches your technology.
b. Some statistical procedures, such as those involved with correlation and regression (discussed in later chapters) require data that consist of matched pairs of values, and those procedures ignore pairs in which at least one of the data values in a matched pair is missing. Assume that we want to conduct analyses for correlation and regression on the last two columns of data in Data Set 3: body temperatures measured at 8 AM on day 2 and again at 12 AM on day 2. For those last two columns, identify the rows with at least one missing value. Note that in some technologies, such
as TI-83>84 Plus calculators, missing data must be represented by a constant such as -9 or 999.
c. Here are two di¥erent strategies for reconfiguring the data set to work around the missing data in the last two columns (assuming that we need matched pairs of data with no missing values):

i. Manual Deletion Highlight rows with at least one missing value in the last two columns, then delete those rows. This can be tedious if there are many rows with missing data and those rows are interspersed throughout instead of being adjacent rows.

ii. Sort Most technologies have a Sort feature that allows you to rearrange all rows using one particular column as the basis for sorting (TI-83>84 Plus calculators do not have this type of sort feature). The result is that all rows remain the same but they are in a di¥erent order. First use the technology’s Sort feature to rearrange all rows using the “8 AM day 2” column as the basis for sorting (so that all missing values in the “8 AM day 2” column are at the beginning); then highlight and delete all of those rows with missing values in the “8 AM day 2” column. Next, use the technology’s Sort feature to rearrange all rows using the “12 AM day 2” column as the basis for sorting (so that all missing values in the “12 AM day 2” column are at the beginning); then highlight and delete all of those rows with missing values in the “12 AM day 2” column. The remaining rows will include matched pairs of body temperatures, and those rows will be suitable for analyses such as correlation and regression. Print the resulting reconfigured data set.