Question

Al, a healthcare administration leader, is concerned with the effects of certain blood sugar levels of 10 patients who visited the hospital in the past week. It was determined last week that each of the 10 patients had their own variation as to which of the three variables could have contributed to their blood sugar level. Al believes that it may have something to do with the patients’ age, hours of sleep, and weight. Thus,

Dependent variable: Blood sugar

Independent variable: Age, # of hours for sleeping, weight

Interpret each chart, the results based on an Linear Regression analysis that was conducted with given data (I have already used the data to run the test). Simply explain each chart below based on the regression analysis

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.565a	.319	-.021	17.772
a. Predictors: (Constant), SLEEPING, WEIGHT, AGE

ANOVAa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	887.796	3	295.932	.937	.479b
	Residual	1895.104	6	315.851
	Total	2782.900	9
a. Dependent Variable: BP
b. Predictors: (Constant), SLEEPING, WEIGHT, AGE

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	16.438	58.961		.279	.790
	AGE	.075	.302	.092	.249	.812
	WEIGHT	.232	.220	.376	1.057	.331
	SLEEPING	7.576	6.497	.428	1.166	.288
a. Dependent Variable: BP

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Answer 1

Here,

1st table :

Multiple correlation coefficient between dependent variable BP and independent variable AGE, WEIGHT and SLEEPING is = 0.565

Coefficient of determination R² = 0.319 = 31.9%

31.9% variation in BP can be explained by the linear regression using predictors as AGE, WEIGHT and SLEEPING.

2nd table :

To test the significance of overall regression model,we have

value of F statistic = 0.937

and p-value = 0.479

Since p-value > 0.05, so at 5% level of significance we can conclude that the overall regression model is not significant.

3rd table :

Testing the significance of AGE ON BP, p-value = 0.812

Since p-value > 0.05, so at 5% level of significance we can conclude that AGE is not a significantly good predictor to predict BP.

Testing the significance of WEIGHT ON BP, p-value = 0.331

Since p-value > 0.05, so at 5% level of significance we can conclude that WEIGHT is not a significantly good predictor to predict BP.

Testing the significance of SLEEPING ON BP, p-value = 0.288

Since p-value > 0.05, so at 5% level of significance we can conclude that SLEEPING is not a significantly good predictor to predict BP.