# Walden University Wk 5 Regression Models Discussion Response

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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

Continue the Discussion and respond to your colleagues in one or more of the following ways:

• Ask a probing question, substantiated with additional background information, evidence, or research.
• Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.
• Offer and support an alternative perspective, using readings from the classroom or from your own research in the Walden Library.
• Make a suggestion based on additional evidence drawn from readings or after synthesizing multiple postings.
• Expand on your colleagues’ postings by providing additional insights or contrasting perspectives based on readings and evidence.

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