Chat with us, powered by LiveChat 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 | WriteDen

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

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.

Variables Entered/Removeda

Model

Variables Entered

Variables Removed

Method

1

Urban=1 Rural=0, F59 FTEs Part Time, F59 FTEs Full Time, Medicare Certified Beds, F33 FTEs Part Time, F33 FTEs Full Timeb

.

Enter

a. Dependent Variable: OccRate

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]

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.334a

.111

.098

16.15966

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

b. Dependent Variable: OccRate

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

12874.868

6

2145.811

8.217

.000b

Residual

102625.909

393

261.135

Total

115500.777

399

a. Dependent Variable: OccRate

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

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

76.194

1.794

42.468

.000

F59 FTEs Full Time

2.799

1.064

.129

2.632

.009

F59 FTEs Part Time

-2.085

3.904

-.026

-.534

.594

F33 FTEs Full Time

.508

.182

.153

2.788

.006

F33 FTEs Part Time

1.561

.831

.101

1.878

.061

Medicare Certified Beds

-.265

.051

-.251

-5.155

.000

Urban=1 Rural=0

.608

1.763

.017

.345

.730

a. Dependent Variable: OccRate

Residuals Statisticsa

Minimum

Maximum

Mean

Std. Deviation

N

Predicted Value

54.8814

110.5536

81.8834

5.68048

400

Residual

-76.74610

36.51113

.00000

16.03770

400

Std. Predicted Value

-4.753

5.047

.000

1.000

400

Std. Residual

-4.749

2.259

.000

.992

400

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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