Identifying Patterns and Relationships

he purpose of this assignment is to filter and sort data, identify

patterns and relationships within data, and use those findings to

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address business needs.

For this assignment, you will use the “Adult Incomes” data set from the Topic Materials. Most data categories are self-explanatory. Clarifying notes are as follows.

Sex: Gender
Relationship: Relationship status
Education: Highest level of education completed
Hours _Per _Week: Number of weekly hours that the individual works
Capital _Gain: Amount of capital gains from tax records
Income: Income of the individual – either “>50K” or “<=50K”
The marketing department in your organization believes that customers with more education have higher incomes than those with less education, and the department director wants to formulate an ad campaign targeting customers with a specific income level. While the assumption stands to reason, having data to support this hypothesis is necessary in order for the department to justify the new advertising campaign strategy. The department director has asked you to determine several specific numbers and percentages along with calculating to what degree educational level is correlated with income. Based upon your finding, the marketing department will determine whether or not its idea for marketing specifically to those customers is likely to create a positive return on investment.

Question 1: Use Excel and various pivot tables and charts to calculate the following information. When summarizing categorical data, sometimes it is easier to create a new indicator (0/1) variable to obtain a percentage (the average of an indicator is a percent). Create a new indicator variable “Income_GT50” where the variable is equal to 1 when the income is greater than $50,000; otherwise use 0. Using this indicator variable, calculate the overall percent of individuals who earn more than $50,000 annually.

Question 2: You are interested in understanding the demographics of a particular cohort in your data set: those who have at least a “Bachelors,” “Masters,” or “Doctorate” degree whose occupation is also “Exec-managerial.” Filter the data to include only these individuals, and report the count of individuals who meet the criteria along with their average age and the percentage who earn more than $50,000 annually.

Question 3: Many times age does not have a purely linear relationship with the dependent variable, so it is separated into age brackets. Create a new variable categorizing age into brackets as follows. Include the pivot table output when submitting the answer.

“<25”: If age less than 25
“25-34”: If age between 25 and 34 inclusive
“35-44”: If age between 35 and 44 inclusive
“45-54”: If age between 45 and 54 inclusive
“55-64”: If age between 55 and 64 inclusive
“65+”: If age is 65 or more
Question 4: Calculate the percentage of customers who earn more than $50,000 annually for each age bracket. Create a pivot chart showing the results and include the chart when submitting the answer. Is the effect of age on income a linear or quadratic relationship?

Question 5: Using a pivot table, calculate the percent of customers earning more than $50,000 annually by education level? Include the pivot table and pivot chart outputs and refer to them as you address whether or not the marketing department’s belief that customers with more education have higher incomes than those with less education. Present your findings in the form of a 250-word executive summary that includes relevant data, charts, and tables.

General Requirements:

Submit the answers to Questions 1-4 and the executive summary as Word documents.

APA format is not required, but solid academic writing is expected.

This assignment uses a grading rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

Activity I – A manager claims that increases in advertising expenditure will surely raise the firm’s profits, citing his sense that people find the firm’s ads entertaining.

Sketch how you might refute this claim using:
A theoretical argument
Why might the refutation using data be more convincing?
Activity II – A grocery store manager is interested in the data-generating process for her store’s weekly soda sales. She believes factors impacting these sales include price, product placement, and whether the week contains a holiday. Write out a formal representation of the data-generation process for weekly soda sales that incorporates these and additional factors.

Activity III – Access the dataset Sales and Costs.xlsx (See the attached) and answer the following questions.

Calculate these descriptive statistics.
Mean of sales
Variance of materials costs
Covariance of labor costs and materials costs
Mean of labor costs
Total sales
Calculate at least two more descriptive statistics for this dataset.
Activity IV – Suppose you receive an e-mail from a stock broker who claims to be able to accurately predict whether any given stock will rise or fall in price during the subsequent month. To “prove” her claim, she makes a prediction about performance (higher price or lower price) for ten stocks over the next month. You are skeptical of the broker’s claim, and assume she simply guesses which stocks will improve or worsen in price over any given month. Put another way, you assume she has a 50% chance of being correct in her prediction for any given stock. Based on this assumption, you derive the following probabilities concerning her ten picks:

Number of correct picks
























What is the empirically testable conclusion resulting from your deductive reasoning?
How could you test your empirically testable conclusion using a data sample?
Outline the inductive and deductive reasoning you could use to evaluate whether or not the broker is simply guessing in her stock picks.
Note: Please refer to this textbox: Prince, J. (2018). Predictive Analytics for Business Strategy. McGraw-Hill Education

What is the difference between data, information and knowledge?

Explain in your own words how the data component related to the hardware and software components of information systems.

What is the difference between quantitative data and qualitative data? In what situations could the number 42 be considered qualitative data?

What are the characteristics of a relational database?

When would using a personal DBMS make sense?

What is the difference between a spreadsheet and a database? List three differences between them.

Describe what the term normalization means.

Why is it important to define the data type of a field when designing a rational database?

Name a database you interact with frequently. What would some of the field names be?

What is metadata?

Name three advantages of using a data warehouse.

What is data mining?

In your own words, explain the difference between supervised learning and unsupervised learning. Give an example of each (not from the book).


Need minimum 500 words

Need 3 APA References

Use side headings to address the content according to questions.HR Metrics and Workforce Analytics
Watch the video “The Power of HR Metrics: Growth, Performance, Sustainment” (3m 35s) on the importance of metrics.
Review the Case Study: Regional Hospital on page 420 of the textbook. Based on the video, your readings this week, and the case study, please respond to the following questions:
Do you believe that a program of HR metrics and workforce analytics might be useful in Regional Hospital?
What opportunities do you see regarding where and how metrics and analytics might be applied in this organization?
Identify three analyses and associated metrics you think might be useful for Regional Hospital to consider.
Leadership and Ethics
Evaluate the leadership of a leader in your organization, or prior organization, in terms of the seven habits of ethical leaders.
Determine one area in which this leader could improve upon, and suggest one action that this leader could take in order to do so.

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