Walden University Wk 5 Regression Models Discussion Response

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.

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 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.

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.