D. Describe the implications of your data analysis from the scenario by doing the following:

1. State the null hypothesis for this linear regression analysis.

2. Interpret the results of the data analysis by doing the following:

a. Discuss the goodness of fit with the supporting test statistic from your linear regression analysis output.

b. Discuss the significance of the independent variable(s) with support from your linear regression analysis results.

c. Create the linear equation and explain its purpose using your analysis results.

3. Discuss a limitation of the research that could affect a recommended course of action.

4. Recommend a course of action that aligns with your linear regression analysis results.

	Multiple R	0.839168643
	R Square	0.704204011
	Adjusted R Square	0.695504129
	Standard Error	0.62867577
	Observations	36
	ANOVA
		df	SS	MS	F	Significance F
	Regression	1	31.99179262	31.99179	80.94409	1.62589E-10
SUMMARY OUTPUT
Regression Statistics
Multiple R	0.839168643
R Square	0.704204011
Adjusted R Square	0.695504129
Standard Error	0.62867577
Observations	36
ANOVA
	df	SS	MS	F	Significance F
Regression	1	31.99179262	31.99179262	80.94409	1.63E-10
Residual	34	13.4379296	0.395233224
Total	35	45.42972222
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	5.240613967	0.291093339	18.00320813	5.89E-19	4.649041	5.832186806	4.649041	5.832187
Program Participation Rate (%)	-0.089138734	0.009907724	-8.996893213	1.63E-10	-0.10927	-0.069003817	-0.10927	-0.069
RESIDUAL OUTPUT
Observation	Predicted Nurse Attrition Rate (%)	Residuals