MIS660 Grand Canyon University Measure of Central Tendency Reporting Paper I have a total of 16 questions that need a 150 word answer to each question. the

MIS660 Grand Canyon University Measure of Central Tendency Reporting Paper I have a total of 16 questions that need a 150 word answer to each question. they must be original answers and not copied from other sites. Topic 1 DQ 1
Description:
Suppose you wanted to estimate the average household income of all Grand Canyon University (GCU) students.
To expedite the process, you only gather household income data from all your friends who major in business at
GCU. You then calculate the average income among your friends and report that it represents the average
income of all GCU students. Is this a good approach? If not, how would you gather data to derive a better
estimate? Explain your answer.
Topic 1 DQ 2
Description:
Income data typically have some outliers. For example, Tim Cook, CEO of Apple, Inc., had a salary of about
400 million in 2011. Suppose you had a data set of incomes in 2011 for all GCU faculty and Tim Cook. Which
measure of central tendency would you use when reporting on the incomes in your data set if you do not want
outliers to have much effect? Explain your answer.
Topic 2 DQ 1
Description:
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?
Topic 2 DQ 2
Description:
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?
Topic 3 DQ 1
Description:
Data summarization is usually not enough when performing analysis. Most of the time, adding context by
telling a story about the data is necessary to describe the analysis to others, especially those who are not datasavvy. What are some general guidelines to follow to tell a good data story? What story elements or structure
should be used to organize the presentation?
Topic 3 DQ 2
Description:
Consider your organization, or an organization you are most familiar with. Explain the general process of data
aggregation for a typical metric (e.g., sales revenue, cost per unit, etc.) used in the organization. What specific
charts are commonly used to visually depict the data? What might be some areas for improvement regarding
how the data is visually presented?
Topic 4 DQ 1
Description:
What are some of the limitations of using Excel for pivot tables/charts? Why does that make software like
Tableau more appealing in the workplace?
Topic 4 DQ 2
Description:
Plotting summarized data will almost always help to convey results more easily. However, there are situations
where plotting the summarized data instead of creating a simple table makes data interpretation more difficult.
Provide two examples of poor charts/graphs and explain why they are difficult to interpret.
Topic 5 DQ 1
Description:
Data is useless without the skills to manipulate, summarize, and analyze it. In fact, even after data is
summarized into a reporting format such as graphs and tables, it still requires someone to add context and
describe the results to fully explain the data. This can be difficult, especially if data is being presented to
nontechnical individuals. Describe two techniques that can be used to better describe analysis results to
nontechnical individuals.
Topic 5 DQ 2
When most people think about data reporting or visualization, they think about a nicely crafted graph that will
not be interactive with a user. Some new tools, such as Tableau, can create visualizations that can interact with a
user with informative pop-up information, more drill-down information, and the ability to export filtered results.
Describe two benefits to having a user interact with a standard report. Are there any drawbacks if the user
modifies the report?
Topic 6 DQ 1
Description:
Summarize key data distribution concepts including probability mass functions (PMF), probability density
functions (PDF), and cumulative distribution functions (CDF). Based on your organization or any organization
you are most familiar with, provide an example of a PMF, an example of a PDF, and an example of a CDF,
based on the type of data used in the organization. How would you summarize each of these to someone who is
not familiar with each of these functions?
Topic 6 DQ 2
Description:
Suppose you had a six-sided die where each number (1, 2, 3, 4, 5, and 6) has the same probability of showing
up (1/6). If the die is rolled an infinite number of times and the number recorded, what will be the average value
that shows up? Is the average value one of the actual possibilities (1, 2, 3, 4, 5, or 6)? Why or why not?
Topic 7 DQ 1
Description:
Suppose you wanted to understand the relationship between a customer’s yearly income (X) and the number of
movies (Y) the customer watched in a year. You then gather data on incomes and the number of movies
watched in a year. The range of incomes in your data set is $5K to $150K. After fitting a simple linear model
and performing all the appropriate diagnostics, the model showed that, on average, for every $10K in income,
the customer watched 1.5 movies in the year. So, for example, if a customer earned 60K in a year, he or she
would be expected to watch nine movies during the year. Now you want to apply this model to your very
wealthy friend who will earn $1 million in the next year. Is this an appropriate application of your model? Why
or why not? Provide specific examples to justify your opinion.
Topic 7 DQ 2
Description:
If you regress daily high temperature (Y) on the amount of ice cream sales (X), you will notice that there is a
strong positive correlation between the two. In other words, as daily ice cream sales increase, the daily high
temperature increases. This implies that if we knew the amount of ice cream sales in a particular day, we could
estimate, with a high level of accuracy, the high temperature in that day. Does this mean that if we wanted to
increase the daily temperature, we need to sell more ice cream? Explain why or why not?
Topic 8 DQ 1
Description:
Suppose you were asked to investigate which predictors explain the number of minutes that 10- to18-year-old
students spend on Twitter. To do so, you build a linear regression model with Twitter usage (Y) measured as the
number of minutes per week. The four predictors you include in the model are Height, Weight, Grade Level,
and Age of each student. You build four simple linear regression models with Y regressed separately on each
predictor, and each predictor is statistically significant. Then you build a multiple linear regression model with
Y regressed on all four predictors, but only one predictor, Age, is statistically significant, and the others are not.
What is likely going on among the four predictors? If you include more than one of these predictors in the
model, what are some problems that can result?
Topic 8 DQ 2
Description:
After building a regression model and performing residual diagnostics, you notice that the errors show severe
departures from normality and appear to have non-constant variance. What steps would you take in this case to
resolve the errors? If the problems are not corrected after all steps are taken, what does that imply about the
modeling approach you are taking? Explain in detail.

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