Category: Analytics

Discuss the types of references you are finding for your paper.  What struggles are you having putting together a reference list for the project? What skills are necessary for doing this assignment and do you have those skills?  If not, how might you obtain them?

This week you are working with your data.  What are some of the issues involved with data collection and storage?  How do they directly apply to you on this project?  What types of data are you using and how are you having to take care in storing and using that data?

Create an 8-10 slide PPT, or some other presentation, that accomplishes the following:
Chose some ethical issue you feel is important (abortion, gun control, climate change, eating meat, anything that can be considered a moral matter – if you are unsure, be sure to clear with your instructor)
Present research on the situation being sure to clearly discuss both sides, as much as possible. Some conundrums will have much more weight on one side than the other such as climate change.
Reiterate the primary theories contained in your moral compass from week 3 and then explain the position on this issue your compass promotes.
Include a proper references/works cited slide (APA or MLA).
For example, say you want to address gender-neutral bathrooms in public buildings. First, present some research from sources promoting that they should exist and from sources promoting they should not. Then reiterate the components of your moral compass as stated in the week three paper. Note, your compass may have evolved and if so, work in the new components. Finally, state your position clearly (they should or should not exist) and how your compass justifies that position.
Keep in mind these best practices, please: 
Proper PPTs have bullets on the slide that are explained in the notes section (see video on how to do this if you do not know how). 
If you intend to narrate the presentation, be sure to include the transcript in the notes section (see PPT on how to insert audio if you do not know and want to give that a shot). 
If, for any reason you are unable to access the notes section, put the transcript/notes in a MS Word document in a numbered list with the numbers matching the slide.
Keep the viewer in mind (teacher). While you might work hard on a 20-minute presentation, few faculty members have the time to watch or listen to it.
Just like papers, clear citations must appear on the slide or in the notes to justify listing a resource as a reference.

Data Collection
Submit data for the project
In addition to submitting data for the project, provide the information listed below as applicable:
Primary/Secondary data
IRB approval if human subjects are involved in primary data collection
Source for secondary data

Regression analysis in Excel, Histograms & Scatterplot, etc.  
Case Study #4 will assess your ability to apply the
concepts of chapter 14 to conduct
simple and
multiple
regression analyses to create a prediction model for home prices based on
up to four
independent variables. You will calculate
various descriptive statistics, create summary tables,
create
various charts and develop five regression
prediction models. Finally, you will create a
written
report summarizing your findings. You will need to use the Data
Analysis ToolPak Add-in
as you did for the
previous two case studies.
The data
file contains data for a random sample of 1,000 houses
located in the greater
Wilmington,
DE area. The data fields included are as
follows:
· Home Price
· Living area (square feet)
· Number of bedrooms
· Number of bathrooms
· Age (years)
The data fields included are as follows:
Age (years)
In developing both your model and the report, address the items below.
1. There are numerous
variables that are believed to be predictors of housing prices, including the
ones in the data set for this project. Using the web, find the key variables that
determine home price including any not include in this data set.
2. Using
Data>Data Analysis>Descriptive Statistics in Excel, calculate the mean, median,
range and standard deviation of each variable and summarize the results in
table.
3. Using
Excel, create histograms for price of the home, living area (square feet) and
age of the home. Be sure to give each chart a title and label the axes clearly.
4. Using
Excel, create scatterplots of each variable with each other variable. Be sure
to give each chart a title and label the axes clearly.
5. Using
Data>Data Analysis>Correlation in Excel, calculate the correlation
coefficient each variable with each other variable.
6. Using
Data>Data Analysis>Regression in Excel, run 4 separate simple regression
models to predict the dependent variable (price of the home) with each of the
independent variables. Use an alpha level of 0.05 to determine significance.
7. Using
Data>Data Analysis>Regression in Excel, run a multiple regression model
to predict the dependent variable with all 4 independent variables. Use an
alpha level of 0.05 to determine significance.
8. In
Word, write a summary report of the findings that includes the tables, charts
and regression analyses from steps 1-7 and includes the following:
a. An
introductory paragraph summarizes the purpose of the analysis. Also include information
that found in your web search about the key variables that determine home price.
b. A
section (1 or more paragraphs) describing what the tabular data from step 2 indicate
about the central tendency, variability, and distribution of each variable. For
example, do the variables appear to be distributed in a symmetric or skewed pattern?
c. A
section (1 or more paragraphs) describing how the frequency histograms from step
3 support and clarify the findings of the tabular data. Include in this section
any evidence suggesting outliers in the data.
d. A
section (1 or more paragraphs) describing what the scatterplots from step 4 and
correlations from step 5 indicate about the relationship between the various
pairs of variables (e.g., are the variables related?, does the relationship
appear to be linear or nonlinear?, is the direction of the relationship
positive or negative?).
e. A
section (1 or more paragraphs) summarizing the findings of the 4 simple
regression
models from step 6. Which models (if any) show that the independent variable in
the model is a significant predictor of price of the home? Which models (if
any) show that the independent variable in the model is not a significant
predictor of price of the home? Which model is the best fitting? Which model is
the poorest fitting?
f. A
section (1 or more paragraphs) summarizing the findings of the multiple
regression model from step 7. Which variables in the model (if any) show that
are a significant predictor of price of the home? Which variables in the model
(if any) show that are not a significant predictor of price of the home? Does
the multiple regression model provide a better fit than the best fitting simple
regression model?
g. A
concluding paragraph summarizing the key findings of the analysis and making about
which model is the best fitting. Based on your web research, indicate any other
variables that are not included in the current best fitting model that might improve
the fit if they were included.
Submit a
single Excel workbook showing all work for Steps 2-7 and a Word document of
your summary report that addresses all parts of Step 8 and that also
includes/interweaves all supporting tables and charts from Steps 2-7 (to tell a
story with the data and through
visualization
means).

I have attached the Instructions.  The paper needs to include Excel charts and/or graphs    for analysis. I have included the original business idea. This paper requires knowledge of   business analytics and marketing research.

The main focus of this project is for you to find/gather,
organize, analyze and present data. You will be choosing a topic, designing and
carrying out a statistical study. Your study will include relevant descriptive
statistics that you have learned about in this course. 
Type of Project
First you will need to decide what type of data you will
collect for your project:
Primary
Data is information that you collect on your own. For example, this
could be obtained by having students at your day school complete a
questionnaire on paper or online (using survey monkey,  etc.). Or canvassing respondents in your neighborhood.
Secondary
Data is information that you are taking from another source. It is
important to use reliable sources. When choosing your topic, be sure that
you will be able to find good data. Some places that students often obtain
data from are provided in the “useful
data websites” handout
Sample Projects
This type of culminating
task is very common for the Data Management course. You will be able to find
many project exemplars online.  Be sure
to see what other students have done to get ideas on what works well and what
can be improved upon.
Attached project itself, rubric for it, textbook, ideas, and useful links

You are a business analyst at a private-equity investment organization. You are exploring potential new investment opportunities for your organization. One of the sectors your organization is looking to invest in is sustainable green-energy businesses. The chief investment officer (CIO) has handed you a historical data set on sustainable organizations. The CIO informs you that five new organizations in the sector are looking for investors. You must analyze the historical data to assess whether investing in one of these organizations would be a good decision.
A linear regression model provides ratio values as output. Using the Historical Green Energy Data Set, you will build and apply a linear regression model to predict the profit value for the five new organizations. If an organization’s predicted profit value is a negative ratio value, you may not recommend the organization as an investment opportunity. Similarly, if an organization’s predicted profit is a positive ratio value, you may recommend it as an investment opportunity.
Write a report with your analysis and recommendations about the investment potential of the five new organizations. Specifically, you must address the following rubric criteria:
Data Requirements: Using the given historical data set, identify the information you will need to create the predictive model.
Determine the dependent and independent variables.
Explain the relationship between the dependent and independent variables.
Linear Regression Model: Use the given historical data to build a linear regression equation.
Create a linear regression equation for multiple independent variables.
Strength of the Model: Use the results of the linear regression to assess the strength of the model.
Discuss the summary output, including multiple R, R square, adjusted R square, and sample error.
What do these results tell you about the variables and the data?
How will these results help you predict the investment potential of an organization?
Strength of the Independent Variables: Evaluate the strength of the independent variables to find out which variables have the most impact.
Discuss the degrees of freedom (df), sum of squares (ss), and mean square (MS).
What do these tell you about the historical data and the role of the different independent variables?
Discuss how the model’s coefficients can be used to select the optimal independent variables for the model.
Actual versus Predicted Model: Compare the predicted variable values with the actual variable values.
Explain whether this model is potentially useful for predicting an organization’s profitability.
Show the residual output from the model, and discuss how far the predicted variables are from the actual results.
Recommendation: Provide a recommendation based on the results of the predictive model.
Apply the model to the data from the five new organizations to determine if any of the organizations are profitable for investors.
Provide a recommendation for each of the five organizations.

Final: Integrating and Managing Data for Strategic Success
Background
XYZ Inc. is a medium-sized e-commerce company that has been operating for the past five years. The company has grown rapidly, but so has its data, which is now sprawled across various departments and systems. There are inconsistencies, data quality issues, and a lack of a centralized data governance strategy, leading to challenges in decision-making and business analytics.
Objective
Your task is to assist XYZ Inc. in establishing a comprehensive data management and governance framework to ensure data quality, consistency, and accessibility. This will involve various steps, from assessing the current state of data, initiating a data governance program, and implementing tools and artifacts to support data management.
Data Provided
You will be provided with three datasets:
1.    Customer Data: Information on customers, including their purchasing behavior and feedback.
2.    Product Data: Details of products, including pricing, categories, and stock levels.
3.    Sales Data: Records of sales transactions, including customer details, product purchased, and transaction dates.
Tasks
Task 1: Assessing Data Quality and Governance Maturity
Objective: Evaluate the current state of data quality and the company’s data governance maturity.
Deliverable: A report highlighting the findings of the data quality assessment and a scorecard for the company’s data governance maturity.
Task 2: Initiating Data Governance
Objective: Define the scope of the data governance program and identify the key stakeholders.
Deliverable: A document outlining the scope, objectives, and key stakeholders of the data governance program.
Task 3: Implementing Data Management Tools and Artifacts
Objective: Recommend and implement data management tools and develop key data governance artifacts.
Deliverable: A report outlining the recommended tools, and examples of data governance artifacts such as a data dictionary and data quality metrics.
Task 4: Building a Business Case
Objective: Develop a business case for the data governance program, showcasing the business value and expected ROI.
Deliverable: A comprehensive business case document.
Task 5: Evaluating Business Analytics Capabilities
Objective: Assess how the implemented data governance framework enhances the company’s business analytics capabilities.
Deliverable: A report evaluating the impact of the data governance program on the company’s ability to perform hypothesis testing, regression analysis, and risk assessment.
By completing this case study, you will have applied the principles and practices learned throughout the course, demonstrating your ability to address real-world data management challenges. This hands-on experience will solidify your skills in data governance, ensuring you are well-equipped to drive data-driven decision-making and contribute to the strategic success of any organization.

Case Study #1 is intended to test your knowledge of how to run and interpret the results of a moving average model, a Holt’s exponential smoothing model, and a Winter’s exponential smoothing model.
HeathCo is a manufacturing company that produces a line of skiwear that is sold under various brand names.  The Product Manager of HeathCo has contracted with you to develop a model to forecast company sales.  You will be supplied with quarterly sales data from 2007 through 2016.  They want a model that will allow them to forecast sales one year (four quarters) out.
Develop and interpret models using the methods below.  Use the data from the years 2007 to 2015 to develop models.  Evaluate the fit of your models to the data series used to create the model.  Reserve 2016 data as a holdout to evaluate the accuracy of your models.
Four-period moving average
Holt’s Exponential Smoothing
Winter’s Exponential Smoothing
Submit both the Instructions/Answer sheet and your detailed forecasting model runs. You will submit a single Excel workbook with all relevant sheets.  The first sheet in your workbook will be an answer sheet with clearly labeled answers to all questions and a guide to sheets where supporting Excel information can be found.
Submit the following two documents:
Completed Instructions/Answer sheet
Excel workbook showing forecasting models and formulas used for calculations
Resources:
Below is the data file and the Instructions/Answer sheet for the case study.
HeathCo_Sales.xlsx Download HeathCo_Sales.xlsx 
Week 2 Case Study Instructions and Answer Sheet – BBA 360.docx