Description
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 oneunit 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.
For 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 Null 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.
 H1: 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 Rate Hospital and the dependent variable of RN Communication is provided in Table 1. The pvalue indicates the significance of the determined correlation. Specifically, a pvalue 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 pvalue is to 1, the more confident we are of a positive linear correlation. The pvalue > 0.05 (alpha) at 0.048 for RN Communication indicates positive relationship and correlation between the variables. The rvalue 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 rvalue 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 1unit 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.
Table 1: Correlations 

Question_1_RateHospital_TopBox 
Question_3_RNComm_TopBox 

Pearson Correlation 
Question_1_RateHospital_TopBox 
1.000 
.136 
Question_3_RNComm_TopBox 
.136 
1.000 

Sig. (1tailed) 
Question_1_RateHospital_TopBox 
. 
.048 
Question_3_RNComm_TopBox 
.048 
. 

N 
Question_1_RateHospital_TopBox 
150 
150 
Question_3_RNComm_TopBox 
150 
150 
Table 2: Model Summary^{b} 

Model 
R 
R Square 
Adjusted R Square 
Std. Error of the Estimate 
1 
.136^{a} 
.019 
.012 
45.70417 
a. Predictors: (Constant), Question_3_RNComm_TopBox 

b. Dependent Variable: Question_1_RateHospital_TopBox 
Table 3: ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
5847.111 
1 
5847.111 
2.799 
.096^{b} 
Residual 
309152.889 
148 
2088.871 

Total 
315000.000 
149 

a. Dependent Variable: Question_1_RateHospital_TopBox 

b. Predictors: (Constant), Question_3_RNComm_TopBox 
Table 4: Coefficients^{a} 

Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 
95.0% Confidence Interval for B 
Correlations 
Collinearity Statistics 

B 
Std. Error 
Beta 
Lower Bound 
Upper Bound 
Zeroorder 
Partial 
Part 
Tolerance 
VIF 

1 
(Constant) 
54.259 
10.121 
5.361 
.000 
34.258 
74.260 

Question_3_RNComm_TopBox 
.217 
.129 
.136 
1.673 
.096 
.039 
.472 
.136 
.136 
.136 
1.000 
1.000 

a. Dependent Variable: Question_1_RateHospital_TopBox 
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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Walden University Wk 5 Regression Models Discussion Response
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Walden University Wk 5 Regression Models Discussion Response