Walden University Wk 5 Regression Models Discussion Response
<
t
d>
< t
d>
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.
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 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.
Pearson CorrelationQuestion_1_RateHospital_TopBox.136
Question_3_RNComm_TopBox.136(1tailed)
Question_1_RateHospital_TopBox..048
Question_3_RNComm_TopBox.048.NQuestion_1_RateHospital_TopBox150
Question_3_RNComm_TopBox150150Table 1: 
Correlations 

Question_1_RateHospital_TopBox 
Question_3_RNComm_TopBox 

1 
.000 

1.000 

Sig. 

150 

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 
FSig.
115847.111
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 
tSig.
Correlations
BStd. ErrorBetaial
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 
Zeroorder 
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.
 Validate an idea with your own experience and additional research.
 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.
We've got everything to become your favourite writing service
Money back guarantee
Your money is safe. Even if we fail to satisfy your expectations, you can always request a refund and get your money back.
Confidentiality
We don’t share your private information with anyone. What happens on our website stays on our website.
Our service is legit
We provide you with a sample paper on the topic you need, and this kind of academic assistance is perfectly legitimate.
Get a plagiarismfree paper
We check every paper with our plagiarismdetection software, so you get a unique paper written for your particular purposes.
We can help with urgent tasks
Need a paper tomorrow? We can write it even while you’re sleeping. Place an order now and get your paper in 8 hours.
Pay a fair price
Our prices depend on urgency. If you want a cheap essay, place your order in advance. Our prices start from $11 per page.