GCU MIS660 Week 2 Discussion (dq1+dq2) Latest

GCU MIS660 Week 2 Discussion

DQ 1

Suppose you had daily temperature data indicating the “high” point of each day for 2015. If you want to show how the high differs over time, what are some of the plot types that will allow you do this? What are some benefits to binning the data into one of 52 weeks and plotting the average high for each week? Would it make sense to do something similar for the four quarters in the year? Why or why not?

DQ 2

Many times, data are missing because of various reasons. This poses some challenges when doing data analysis. For example, suppose you wanted to do some analysis of the yearly incomes of the faculty at GCU. When asked for their incomes, 25% of the faculty did not participate in the survey; therefore, their incomes are missing from the dataset. How would you summarize the income data in this case? Is it appropriate to ignore the missing incomes and summarize the data without them? Should you estimate the missing incomes, perhaps with the overall average, to complete the data set?