deliverable 4

Deliverable 4 – Predictive Analysis for Datadriven DecisionsDeliverable 4 – Predictive Analysis for Datadriven Decisions
Assignment Content
1.
Competency
Determine business outcomes using predictive analysis techniques.
Student Success Criteria
View the grading rubric for this deliverable by selecting the “This item is graded with a rubric”
link, which is located in the Details & Information pane.
Scenario
You are the manager for a company that sells outdoor grills. You’ve recently earned your MBA,
and you want to apply what you’ve learned to your position to help with decision-making. You
have developed the following estimated regression equation to help make data-driven decisions
for the store. This will help you to better see how the unemployment rate, temperature, number
of activities, and gas prices impact weekly outdoor grill sales.
Y = 22,100 – 412×1 + 818×2 – 93×3 – 71×4
Where:
· Y = weekly sales
· x1 = local unemployment rate
· x2 = weekly average high temperature
· x3 = number of activities in the local community
· x4 = average price of gasoline per gallon
Instructions
Use the above equation and information to answer the following questions in a Word document,
and create a guideline to use for future business decisions:
1. Based on the equation above, please provide the value for x1, x2, x3, and x4. Also, explain
what these values mean in the context of this question. For example: What does the
value of 818 mean in the equation above (specify if it is x1 or x2 or x3 or x4, and explain
what those values mean based on the equation and context)?
2. What are the estimated weekly sales if the unemployment rate is 3.7%, the average high
temperature is 670, there are 10 activities, and the average price of gasoline is $3.39 per
gallon?
3. Evaluate data mining techniques that could be used to enhance manager’s decisionmaking to increase sales.
4. What recommendations or decisions could you make based on the predictive analysis in
question 2?
Resources
5. Rasmussen College Writing Guide: https://guides.rasmussen.edu/writing/professional
6. Grammar Checking – How do I create a Grammarly
account? https://rasmussen.libanswers.com/faq/32707
7. Live Help professional tutoring is available for statistics:
8. https://guides.rasmussen.edu/learningservices
Deliverable 4 – Predictive Analysis for Data-driven Decisions
Rubric Details
Maximum Score
4 points
•
Grade for Deliverable 4
100% of total grade
A – 4 – Mastery
4
B – 3 – Proficiency
3
C – 2 – Competence
2
F – 1 – No Pass
1
I – 0 – Not Submitted
0
•
Criterion 1
0% of total grade
A – 4 – Mastery
Clearly and strongly, used correlation and regression analysis techniques to examine
relationships (thoroughly explained values based on the equation and context).
0
B – 3 – Proficiency
Used correlation and regression analysis techniques to examine relationships (clearly explained
values based on the equation and context).
0
C – 2 – Competence
Used correlation and regression analysis techniques to examine relationships (somewhat
explained values based on the equation and context).
0
F – 1 – No Pass
Unclear correlation and regression analysis techniques used to examine relationships
(explanation of values based on the equation and context was unclear).
0
I – 0 – Not Submitted
Not Submitted
0
•
Criterion 2
0% of total grade
A – 4 – Mastery
Skillfully evaluated data mining techniques used to enhance managers’ decision-making
(estimated weekly sales based on values given).
0
B – 3 – Proficiency
Strongly evaluated data mining techniques used to enhance managers’ decision-making
(estimated weekly sales based on values given).
0
C – 2 – Competence
Moderately evaluated data mining techniques used to enhance managers’ decision-making
(estimated weekly sales based on values given).
0
F – 1 – No Pass
Used unclear verbiage used to evaluate data mining techniques used to enhance managers’
decision-making (unclearly estimated weekly sales based on values given).
0
I – 0 – Not Submitted
Not Submitted
0
•
Criterion 3
0% of total grade
A – 4 – Mastery
Clearly and strongly utilized predictive modeling to propose business recommendations, using
clear examples in a well-analyzed guideline, and based on predictive analysis in question 2.
0
B – 3 – Proficiency
Utilized predictive modeling to propose business recommendations, using some examples, and
based on predictive analysis in question 2.
0
C – 2 – Competence
Utilized predictive modeling to propose business recommendations; no examples provided;
loosely based on predictive analysis in question 2.
0
F – 1 – No Pass
Used unclear verbiage for predictive modeling to propose business recommendations, and
unclearly based on predictive analysis in question 2.
0
I – 0 – Not Submitted
Not Submitted
0