Depending on context, you could argue which is the dependent variable and which is the independent variable.? I chose to investigate whether obesity rates could
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Get it written →Hints: Depending on context, you could argue which is the dependent variable and which is the independent variable. I chose to investigate whether obesity rates could predict total hospital expenditures. I did not "check" the 'constant is zero" box.
Instructions Part One: A linear regression analysis was performed in SPSS to evaluate the ability of independent variables full and part-time FTEs, number of Medicare certified beds and urban vs. rural setting to predict dependent variable, occupancy rate. In paragraph, APA-formatted form, interpret the results. Include basic assumptions for regression analysis.
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Variables Entered/Removeda |
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Model |
Variables Entered |
Variables Removed |
Method |
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1 |
Urban=1 Rural=0, F59 FTEs Part Time, F59 FTEs Full Time, Medicare Certified Beds, F33 FTEs Part Time, F33 FTEs Full Timeb |
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Enter |
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a. Dependent Variable: OccRate |
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b. All requested variables entered. |
For the chart above, the Independent Variables are:
F59 FTEs Full Time [ACTTHRFT]
F59 FTEs Part Time [ACTTHRPT]
F33 FTEs Full Time [ADMIN_FT]
F33 FTEs Part Time [ADMIN_PT]
Medicare Certified Beds [MCAREBED]
Urban = 1 Rural = 0 [URBAN]
The Dependent Variable is:
Occupancy Rate [OccRate]
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Model Summaryb |
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Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
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1 |
.334a |
.111 |
.098 |
16.15966 |
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a. Predictors: (Constant), Urban=1 Rural=0, F59 FTEs Part Time, F59 FTEs Full Time, Medicare Certified Beds, F33 FTEs Part Time, F33 FTEs Full Time |
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b. Dependent Variable: OccRate |
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ANOVAa |
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Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
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1 |
Regression |
12874.868 |
6 |
2145.811 |
8.217 |
.000b |
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Residual |
102625.909 |
393 |
261.135 |
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Total |
115500.777 |
399 |
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a. Dependent Variable: OccRate |
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b. Predictors: (Constant), Urban=1 Rural=0, F59 FTEs Part Time, F59 FTEs Full Time, Medicare Certified Beds, F33 FTEs Part Time, F33 FTEs Full Time |
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Coefficientsa |
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Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
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B |
Std. Error |
Beta |
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1 |
(Constant) |
76.194 |
1.794 |
42.468 |
.000 |
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F59 FTEs Full Time |
2.799 |
1.064 |
.129 |
2.632 |
.009 |
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F59 FTEs Part Time |
-2.085 |
3.904 |
-.026 |
-.534 |
.594 |
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F33 FTEs Full Time |
.508 |
.182 |
.153 |
2.788 |
.006 |
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F33 FTEs Part Time |
1.561 |
.831 |
.101 |
1.878 |
.061 |
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Medicare Certified Beds |
-.265 |
.051 |
-.251 |
-5.155 |
.000 |
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Urban=1 Rural=0 |
.608 |
1.763 |
.017 |
.345 |
.730 |
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a. Dependent Variable: OccRate |
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Residuals Statisticsa |
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Minimum |
Maximum |
Mean |
Std. Deviation |
N |
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Predicted Value |
54.8814 |
110.5536 |
81.8834 |
5.68048 |
400 |
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Residual |
-76.74610 |
36.51113 |
.00000 |
16.03770 |
400 |
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Std. Predicted Value |
-4.753 |
5.047 |
.000 |
1.000 |
400 |
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Std. Residual |
-4.749 |
2.259 |
.000 |
.992 |
400 |
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a. Dependent Variable: OccRate |
Instructions Part Two: Use the same Excel file you used for the correlation assignment to calculate regression on obesity rates and total hospital expenditures (Data > Analyze Data (may be an add in) > Regression). Paste results and interpretation below.
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