Assessment 3: Predicting an Outcome Using Regression Models
Capella University Assessment 3: Predicting an Outcome Using Regression Models-Step-By-Step Guide
This guide will demonstrate how to complete the Assessment 3: Predicting an Outcome Using Regression Models assignment based on general principles of academic writing. Here, we will show you the A, B, Cs of completing an academic paper, irrespective of the instructions. After guiding you through what to do, the guide will leave one or two sample essays at the end to highlight the various sections discussed below.
How to Research and Prepare for Assessment 3: Predicting an Outcome Using Regression Models
Whether one passes or fails an academic assignment such as the Capella University Assessment 3: Predicting an Outcome Using Regression Models depends on the preparation done beforehand. The first thing to do once you receive an assignment is to quickly skim through the requirements. Once that is done, start going through the instructions one by one to clearly understand what the instructor wants. The most important thing here is to understand the required format—whether it is APA, MLA, Chicago, etc.
After understanding the requirements of the paper, the next phase is to gather relevant materials. The first place to start the research process is the weekly resources. Go through the resources provided in the instructions to determine which ones fit the assignment. After reviewing the provided resources, use the university library to search for additional resources. After gathering sufficient and necessary resources, you are now ready to start drafting your paper.
How to Write the Introduction for Assessment 3: Predicting an Outcome Using Regression Models
The introduction for the Capella University Assessment 3: Predicting an Outcome Using Regression Models is where you tell the instructor what your paper will encompass. In three to four statements, highlight the important points that will form the basis of your paper. Here, you can include statistics to show the importance of the topic you will be discussing. At the end of the introduction, write a clear purpose statement outlining what exactly will be contained in the paper. This statement will start with “The purpose of this paper…” and then proceed to outline the various sections of the instructions.
How to Write the Body for Assessment 3: Predicting an Outcome Using Regression Models
After the introduction, move into the main part of the Assessment 3: Predicting an Outcome Using Regression Models assignment, which is the body. Given that the paper you will be writing is not experimental, the way you organize the headings and subheadings of your paper is critically important. In some cases, you might have to use more subheadings to properly organize the assignment. The organization will depend on the rubric provided. Carefully examine the rubric, as it will contain all the detailed requirements of the assignment. Sometimes, the rubric will have information that the normal instructions lack.
Another important factor to consider at this point is how to do citations. In-text citations are fundamental as they support the arguments and points you make in the paper. At this point, the resources gathered at the beginning will come in handy. Integrating the ideas of the authors with your own will ensure that you produce a comprehensive paper. Also, follow the given citation format. In most cases, APA 7 is the preferred format for nursing assignments.
How to Write the Conclusion for Assessment 3: Predicting an Outcome Using Regression Models
After completing the main sections, write the conclusion of your paper. The conclusion is a summary of the main points you made in your paper. However, you need to rewrite the points and not simply copy and paste them. By restating the points from each subheading, you will provide a nuanced overview of the assignment to the reader.
How to Format the References List for Assessment 3: Predicting an Outcome Using Regression Models
The very last part of your paper involves listing the sources used in your paper. These sources should be listed in alphabetical order and double-spaced. Additionally, use a hanging indent for each source that appears in this list. Lastly, only the sources cited within the body of the paper should appear here.
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Assessment 3: Predicting an Outcome Using Regression Models
Introduction
Regression analysis refers to the set of statistical methods that are applied in the estimation of the dependent variable and one or more independent variables. Regression analysis can be applied to assess the strength of the correlation between variables and for modeling the future relationship that may be expected between independent and dependent variables. In regression analysis, there exist several variations such as multiple linear, linear, as well as nonlinear. Some of the most common models are multiple linear and simple linear (Kumari & Yadav, 2018). Non-linear regression analysis is usually applied for complicated data sets where the independent and dependent variables indicate a nonlinear relationship (Aggarwal & Ranganathan, 2017). There are numerous applications of regression analysis, including research processes as well as financial analysis. The purpose of this assignment is to predict an outcome using regression models through the application of the dataset given.
Before conducting regression analysis, it is necessary to understand the assumptions. One of the assumptions is that the independent variable is not always random. Some other assumptions include the value of residuals is zero, the independent and dependent variables often show a linear relationship between the intercept and the slope, the value of residual is always constant across all the observations made; finally, the values of residual are not always correlated across different observations (Montgomery et al., 2021). Besides, the residual values often follow the normal distribution.
Regression analysis
From the information given, the dependent variable is hospital costs, while the independent variables include patient age, risk factors, and patient satisfaction scores. Both the independent and dependent variables are continuous.
| Table 1: Descriptive Statistics | |||
| Mean | Std. Deviation | N | |
| Cost
|
14906.51 | 2614.346 | 185 |
| Age
|
73.25 | 6.430 | 185 |
| risk | 5.69 | 2.777 | 185 |
| satisfaction | 50.02 | 28.919 | 185 |
Table 1 indicates the descriptive statistics for both the dependent and independent variables. The means of variables, cost, age, risk, and satisfaction include $14906.51, 73.25 years, 5.69, and 50.02. The sample size used was 185.
| Table 2: Correlations | |||||
| cost | age | risk | satisfaction | ||
| Pearson Correlation | cost | 1.000 | .279 | .199 | -.071 |
| age | .279 | 1.000 | .152 | .094 | |
| risk | .199 | .152 | 1.000 | .037 | |
| satisfaction | -.071 | .094 | .037 | 1.000 | |
| Sig. (1-tailed) | cost | . | .000 | .003 | .169 |
| age | .000 | . | .019 | .101 | |
| risk | .003 | .019 | . | .307 | |
| satisfaction | .169 | .101 | .307 | . | |
| N | cost | 185 | 185 | 185 | 185 |
| age | 185 | 185 | 185 | 185 | |
| risk | 185 | 185 | 185 | 185 | |
| satisfaction | 185 | 185 | 185 | 185 | |
Table 2 shows the correlation between dependent and independent variables. The outcomes show that there is a weak positive correlation between the cost and age; the correlation coefficient is 0.279. The correlation between cost and risk is also weak and positive; the correlation coefficient is 0.199. Finally, the correlation between cost and the level of satisfaction is weak and negative; the correlation coefficient is -.071.
| Table 3: Model Summary | |||||||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Change Statistics | ||||
| R Square Change | F Change | df1 | df2 | Sig. F Change | |||||
| 1 | .336a | .113 | .098 | 2482.429 | .113 | 7.692 | 3 | 181 | .000 |
| a. Predictors: (Constant), satisfaction, risk, age | |||||||||
From table 3, the R-Square is 0.113 showing a “Medium” effect size; therefore, the model attempt to explain much of the variance in the dependent variable. The significant value from the analysis is 0.000 < 0.05; therefore, we reject that null hypothesis and conclude that the model is fit or significant. Given that the analysis was done at 95% level of significance, the null hypothesis is rejected when the significant values obtained are less than 0.05.
| Table 4: Coefficientsa | ||||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | 95.0% Confidence Interval for B | |||
| B | Std. Error | Beta | Lower Bound | Upper Bound | ||||
| 1 | (Constan) | 6652.176 | 2096.818 | 3.173 | .002 | 2514.825 | 10789.527 | |
| age | 107.036 | 28.911 | .263 | 3.702 | .000 | 49.990 | 164.082 | |
| risk | 153.557 | 66.685 | .163 | 2.303 | .022 | 21.978 | 285.136 | |
| satisfaction | -9.195 | 6.358 | -.102 | -1.446 | .150 | -21.740 | 3.351 | |
| a. Dependent Variable: cost | ||||||||
From table 4, there is the indication of different unstandardized coefficients for the independent variables used in the study. A regression equation can therefore be formulated from the information given. Using the equation of a straight line, Y= Mx +C, at the Y-intercept, x becomes 0. Therefore, the equation becomes, Y=M (0) + C, Y=C. From the table above, Y= 6652.176. To formulate a regression equation, there is the need for the analysis to consider the constant and unstandardized coefficients of the independent variables. The equation takes the form of a line equation which is Y= Mx + c,
Therefore, we find that:
Cost = 6652.176 + 107.036 (age) + 153.557 (risk) – 9.195 (satisfaction)
The above regression equation can be used to predict the costs given each of the independent variables. While determining the cost using each of the variables, we set all other independent variables to zero. The above equation shows that the cost depends on the age of the patients, risks factors, as well as the level of satisfaction of the patients after treatments.
Conclusion
Regression analysis can be applied to assess the strength of the correlation between variables and for modeling the future relationship that may be expected between independent and dependent variables. The analysis shows that the hospital costs are dependent on patient age, risk factors, and patient satisfaction scores. Both the independent and dependent variables are continuous.
References
Kumari, K., & Yadav, S. (2018). Linear regression analysis study. Journal of the practice of Cardiovascular Sciences, 4(1), 33. https://www.j-pcs.org/article.asp?issn=2395-5414;year=2018;volume=4;issue=1;spage=33;epage=36;aulast=Kumari
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons. http://sutlib2.sut.ac.th/sut_contents/H133678.pdf
Aggarwal, R., & Ranganathan, P. (2017). Common pitfalls in statistical analysis: Linear regression analysis. Perspectives in clinical research, 8(2), 100. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5384397/