Walden University Wk 5 Regression Models Discussion Response

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Scenario

The regular regression coefficients that you see in your statistical output describe the relationship between the independent variables and the dependent variable

.

The coefficient value represents the mean change of the dependent variable given a one-unit shift in an independent variable. Consequently, you might think you can use the absolute sizes of the coefficients to identify the most important variable. After all, a larger coefficient signifies a greater change in the mean of the independent variable.

F

or instance, in the healthcare industry, healthcare leaders may want to determine how poor communication with nurses affects the overall hospital rating. The dependent variable (y) is overall hospital rating and the independent variable is communication with nurses (x). We can evaluate the relationship between these variables by conducting a multiple regression analysis.

The

N

ull and Alternative Hypothesis is:

• H0: There is no correlation between the overall rating of hospital and communication with nurses; essentially, the Pearson’s correlation coefficient is equal to zero.
• H

  1

  : There is a correlation between the overall rating of hospital and communication with nurses; in other words, Pearson’s correlation coefficient is not equal to zero.

The SPSS

Pearson Correlation

between the independent variable of

R

ate Hospital and the dependent variable of RN Communication is provided in Table 1. The p-value indicates the significance of the determined correlation. Specifically, a p-value is a number between 0 and 1, representing the probability that this data would have arisen if the null hypothesis were true. The closer the p-value is to 1, the more confident we are of a positive linear correlation. The p-value > 0.05 (alpha) at 0

.048

for RN Communication indicates positive relationship and correlation between the variables. The r-value measures the strength and direction of a linear relationship between variables on a scatterplot and is always between 1 and -1. For RN Communication, the r-value is calculated to be 0

.136

. Therefore indicating a linear relationship between the overall rating of hospital and RN communication. We would accept the null hypothesis and reject the alternative hypothesis.

Table 2 provides the

Model

Summary, showing R2 = 0

.019

, meaning that 1.9% of the overall rating of the hospital is not indicated by RN communication. Table 3 shows the ANOVA test in the regression model, with a significance level of 0

.096

, above the conventional 0.05 threshold. Therefore, we can conclude that this model does not have statistical significance.

Table 4 represents the Coefficients output. Suppose the beta coefficient is positive, as indicated in RN Communication at

B

= 0

.217

. In that case, the interpretation is that for every 1-unit increase in RN Communication, the overall Rate of Hospital will increase by the beta coefficient value of 0.217. The

Beta

= 0.217 and the significance = 0.096, which is above the 0.05 threshold. Therefore, we can accept the null hypothesis that no correlation exists between the overall rating of the hospital and RN communication.

Pearson CorrelationQuestion_1_RateHospital_TopBox

.136

Question_3_RNComm_TopBox.136

(1-tailed)

Question_1_RateHospital_TopBox..048

Question_3_RNComm_TopBox.048.NQuestion_1_RateHospital_TopBox

150

Question_3_RNComm_TopBox150150

Table 1:	Correlations
			Question_1_RateHospital_TopBox					Question_3_RNComm_TopBox
	1	.000
	1.000
		Sig.
			150

ModelR

of the Estimate

1

.019

, Question_3_RNComm_TopBox

Table 2: Model Summaryb
R Square	Adjusted R Square		Std. Error
.136a	.012	45.70417
a. Predictors:	(Constant)
b. Dependent Variable: Question_1_RateHospital_TopBox

Model

FSig.

1

15847.111

Table 3: ANOVAa
Sum of Squares	df	Mean Square
Regression		5847.111	2.799	.096b
Residual	309152.889	148	2088.871
Total	315000.000	149
	a. Dependent Variable: Question_1_RateHospital_TopBox
b. Predictors: (Constant), Question_3_RNComm_TopBox

Model

tSig.

Correlations

BStd. ErrorBeta

ial

Part

1(Constant)

.000

Question_3_RNComm_TopBox.217

.136

.096

.136.136.1361.0001.000

a. Dependent Variable: Question_1_RateHospital_TopBox

Table 4: Coefficientsa
Unstandardized Coefficients	Standardized Coefficients	95.0% Confidence Interval for B	Collinearity Statistics
Lower Bound	Upper Bound	Zero-order		Part	Tolerance	VIF
54.259	10.121	5.361	34.258	74.260
.129	1.673	-.039	.472

References

Albright, S. C., & Winston, W. L. (2017). Business analytics: Data analysis and decision making (6th ed.). Stamford, CT: Cengage Learning.

Lee, C., Famoye, F., & Shelden, B. (2008b). SPSS training workshop: Linear regression: Variable selections [Video file]. Retrieved from

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