DATA-DRIVEN DECISION MAKING C207
C207 Task 1 Linear Regression Analysis on Nurse Attrition Rates | Accurate & Verified Answers to
Pass Actual Exam
Data-Driven Decision Making C207
Task 1 Linear Regression Analysis
March 03, 2025
A. Summarize the Business Scenario
1
, DATA-DRIVEN DECISION MAKING C207
A1. Describe a business question
A question that could answer the “Linear Regression Analysis” business scenario would
be, “Is there a significant relationship between the employee well-being program participation
rate and the nurse attrition rate? More so, the analysis is geared toward nurses' monthly
participation in the program to combat high turnover rates.
A2. State the Null Hypothesis
The null hypothesis for this linear regression analysis is “There is no significant
relationship between the employee well-being program participation rate and the nurse attrition
rate.”
A3. Why is Linear Regression the appropriate analysis technique
The linear regression technique is the appropriate analysis technique, along with the “null
hypothesis” in this business scenario, because the data contains one X independent variable and
one Y dependent variable, which are the program participation rate and the nurse attrition rate.
When nurses participate in the program, the attrition rate drops, reducing the turnover rate. The
linear regression analysis is the best technique because it allows you to forecast future
participation using the current data information.
Describe the Data Provided
B1. Independent Variable or Variables
The program participation rate is the independent variable in this scenario. It is
seen along the X-axis and is represented in the form of percentages.
The nurse attrition rate is represented on the Y-axis as the dependent variable
because it depends on whether nurses participate.
2
C207 Task 1 Linear Regression Analysis on Nurse Attrition Rates | Accurate & Verified Answers to
Pass Actual Exam
Data-Driven Decision Making C207
Task 1 Linear Regression Analysis
March 03, 2025
A. Summarize the Business Scenario
1
, DATA-DRIVEN DECISION MAKING C207
A1. Describe a business question
A question that could answer the “Linear Regression Analysis” business scenario would
be, “Is there a significant relationship between the employee well-being program participation
rate and the nurse attrition rate? More so, the analysis is geared toward nurses' monthly
participation in the program to combat high turnover rates.
A2. State the Null Hypothesis
The null hypothesis for this linear regression analysis is “There is no significant
relationship between the employee well-being program participation rate and the nurse attrition
rate.”
A3. Why is Linear Regression the appropriate analysis technique
The linear regression technique is the appropriate analysis technique, along with the “null
hypothesis” in this business scenario, because the data contains one X independent variable and
one Y dependent variable, which are the program participation rate and the nurse attrition rate.
When nurses participate in the program, the attrition rate drops, reducing the turnover rate. The
linear regression analysis is the best technique because it allows you to forecast future
participation using the current data information.
Describe the Data Provided
B1. Independent Variable or Variables
The program participation rate is the independent variable in this scenario. It is
seen along the X-axis and is represented in the form of percentages.
The nurse attrition rate is represented on the Y-axis as the dependent variable
because it depends on whether nurses participate.
2
