In: Statistics and Probability
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 |
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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 |
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Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
1 |
Regression |
887.796 |
3 |
295.932 |
.937 |
.479b |
Residual |
1895.104 |
6 |
315.851 |
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Total |
2782.900 |
9 |
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a. Dependent Variable: BP |
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b. Predictors: (Constant), SLEEPING, WEIGHT, AGE |
Coefficientsa |
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Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
B |
Std. Error |
Beta |
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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 |
.
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.