QUM 3, Task On e
Western Governors University C207: Data-
Driven Decision Making
A1: B USINESS QUESTION
“Linear regression is commonly used in mathematical research methods, where it is
possible to measure the predicted effects and model them against multiple input variables”
(Maulud & Abdulazeez, 2020). Linear regression analysis is a statistical instrument used to
evaluate the quantitative relationship between a dependent variable and one or more
independent variables. “It is a method of data evaluation and modeling that establishes linear
relationships between variables that are dependent and independent” (Maulud & Abdulazeez,
2020). In consideration of the contextual scenario provided and through the application of
linear regression analysis, an applicable business question arises. What is the nature of the
relationship between the nurse participation rates in the well-being program and the
commensurate nurse attrition rates over 36 months? This question seeks to determine among
nurses, whether increased participation in the well-being program is associated with decreased
attrition, a key goal of the hospital's initiative to reduce stress and improve job satisfaction.
A2: NULL H Y POTHESIS
, There is no statistically significant relationship between the rates of nurse participation
in the wellbeing program and the corresponding nurse attrition rates over 36 months?
A3: ANAL Y SIS TECHNIQUE JUSTIFICATION
This is a suitable method to measure the correlation between nurse well-being program
participation and attrition rates of nurses as formulated in the business question and null
hypothesis. It effectively simulates how two continuous variables are correlated over 36 months
whether participation rates and attrition rates are related. The null hypothesis states no
significant relationship exists between these variables, which regression can test by analyzing
the statistical significance of the regression coefficient. The scenario’s quantitative data and the
retroactive linkage of attrition rates to enrollment months further support the suitability of this
approach, as it captures temporal trends and predicts outcomes. By applying linear regression,
Western Governors University C207: Data-
Driven Decision Making
A1: B USINESS QUESTION
“Linear regression is commonly used in mathematical research methods, where it is
possible to measure the predicted effects and model them against multiple input variables”
(Maulud & Abdulazeez, 2020). Linear regression analysis is a statistical instrument used to
evaluate the quantitative relationship between a dependent variable and one or more
independent variables. “It is a method of data evaluation and modeling that establishes linear
relationships between variables that are dependent and independent” (Maulud & Abdulazeez,
2020). In consideration of the contextual scenario provided and through the application of
linear regression analysis, an applicable business question arises. What is the nature of the
relationship between the nurse participation rates in the well-being program and the
commensurate nurse attrition rates over 36 months? This question seeks to determine among
nurses, whether increased participation in the well-being program is associated with decreased
attrition, a key goal of the hospital's initiative to reduce stress and improve job satisfaction.
A2: NULL H Y POTHESIS
, There is no statistically significant relationship between the rates of nurse participation
in the wellbeing program and the corresponding nurse attrition rates over 36 months?
A3: ANAL Y SIS TECHNIQUE JUSTIFICATION
This is a suitable method to measure the correlation between nurse well-being program
participation and attrition rates of nurses as formulated in the business question and null
hypothesis. It effectively simulates how two continuous variables are correlated over 36 months
whether participation rates and attrition rates are related. The null hypothesis states no
significant relationship exists between these variables, which regression can test by analyzing
the statistical significance of the regression coefficient. The scenario’s quantitative data and the
retroactive linkage of attrition rates to enrollment months further support the suitability of this
approach, as it captures temporal trends and predicts outcomes. By applying linear regression,
