MHAFP5017 Assessment 4: Presenting Statistical Results for Decision Making
Capella University MHAFP5017 Assessment 4: Presenting Statistical Results for Decision Making-Step-By-Step Guide
This guide will demonstrate how to complete the Capella University MHAFP5017 Assessment 4: Presenting Statistical Results for Decision Making 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 MHAFP5017 Assessment 4: Presenting Statistical Results for Decision Making
Whether one passes or fails an academic assignment such as the Capella University MHAFP5017 Assessment 4: Presenting Statistical Results for Decision Making 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 MHAFP5017 Assessment 4: Presenting Statistical Results for Decision Making
The introduction for the Capella University MHAFP5017 Assessment 4: Presenting Statistical Results for Decision Making 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 MHAFP5017 Assessment 4: Presenting Statistical Results for Decision Making
After the introduction, move into the main part of the MHAFP5017 Assessment 4: Presenting Statistical Results for Decision Making 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 MHAFP5017 Assessment 4: Presenting Statistical Results for Decision Making
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 MHAFP5017 Assessment 4: Presenting Statistical Results for Decision Making
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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MHAFP5017 Assessment 4: Presenting Statistical Results for Decision Making
The process of data analysis often involve the consideration of different variables. Before deciding on the statistical tests to perform, it is necessary for the researcher or data analysis to understand the nature of the variables. Knowing the types of variables in a research study often help in determining the forms of analysis to take and the possible outcomes (Chen et al., 2019). Some of the descriptive tests performed in the study include descriptive statistics, regression analysis, and correlation (Kaur et al., 2018). Also, charts and graphs were applied go represent the outcomes of data analysis. Some of the variables applied in the study included: Hospital-acquired condition rate (HAC_Rate), Number of hospital-acquired conditions (HAC) per 1,000 discharges; measures extent of hospital errors, Nursing hours per patient day (Nursing_HPPD), Number of nursing staff hours per patient day (HPPD); measures nurse staffing level, Nursing skill mix (Skill_Mix), Percentage of nursing staff hours provided by Registered Nurses (RN); measures skill mix of nursing staff, Average length of stay (ALOS), Number of inpatient days per hospital discharge; measures hospital efficiency. The four variables have a continuous distribution and therefore different parametric tests can be used to determine the relationship between variables.
Descriptive Statistics
Table 1: Descriptive Statistics
| HAC_Rate | Nursing_HPPD | ||
| Mean | 117.1578947 | Mean | 3.943178947 |
| Standard Error | 0.502026306 | Standard Error | 0.100927519 |
| Median | 118 | Median | 4.006 |
| Mode | 119 | Mode | 4.006 |
| Standard Deviation | 4.893147161 | Standard Deviation | 0.983719775 |
| Sample Variance | 23.94288914 | Sample Variance | 0.967704595 |
| Kurtosis | -0.30555851 | Kurtosis | 0.064807972 |
| Skewness | -0.567810177 | Skewness | -0.005149876 |
| Range | 20 | Range | 5.091 |
| Minimum | 106 | Minimum | 1.628 |
| Maximum | 126 | Maximum | 6.719 |
| Sum | 11130 | Sum | 374.602 |
| Count | 95 | Count | 95 |
| Confidence Level (95.0%) | 0.996784999 | Confidence Level (95.0%) | 0.200393956 |
Table 1 shows the descriptive statistics for the two variables, HAC_Rate and Nursing_HPPD. The mean of HAC_Rate and
Nursing_HPPD were 117.1578947 and 3.943178947. The standard deviation of the two variables were 4.893147161 and 0.983719775 respectively. In other words, the mean number of hospital-acquired conditions (HAC) per 1,000 discharges was 117.1578947 and the mean number of nursing staff hours per patient day (HPPD) was 3.943178947.
Table 2: Descriptive Statistics
| Skill_Mix | ALOS | ||
| Mean | 59.92989474 | Mean | 6.669494737 |
| Standard Error | 0.099670622 | Standard Error | 0.138704512 |
| Median | 59.99 | Median | 6.682 |
| Mode | 60.26 | Mode | 5.812 |
| Standard Deviation | 0.971469051 | Standard Deviation | 1.351924349 |
| Sample Variance | 0.943752116 | Sample Variance | 1.827699444 |
| Kurtosis | -0.13681883 | Kurtosis | -0.490963757 |
| Skewness | 0.26791489 | Skewness | 0.034698395 |
| Range | 4.46 | Range | 5.611 |
| Minimum | 58.07 | Minimum | 4.017 |
| Maximum | 62.53 | Maximum | 9.628 |
| Sum | 5693.34 | Sum | 633.602 |
| Count | 95 | Count | 95 |
| Confidence Level (95.0%) | 0.197898356 | Confidence Level (95.0%) | 0.275401059 |
Table 2 indicates the descriptive statistics for the two variables, Skill_Mix and ALOS. The means for the Skill_Mix and ALOS were 59.92989474 and 6.669494737 respectively while the standard deviations were 0.971469051 and 1.351924349 respectively.
Correlation Analysis
Table 3: Correlation Analysis
| CORRELATION | HAC_Rate | Nursing_HPPD | Skill_Mix | ALOS |
| HAC_Rate | 1 | |||
| Nursing_HPPD | -0.799400102 | 1 | ||
| Skill_Mix | -0.304808429 | 0.4617178 | 1 | |
| ALOS | 0.417162987 | -0.312434971 | -0.133376169 | 1 |
Table 3 shows the correlation coefficient between the four variables considered in the study process. From the data, there was a strong and negative correlation between Nursing_HPPD and HAC_Rate. The correlation coefficient was -0.7994. Also, there was a strong positive correlation between Skill_Mix and Nursing_HPPD, the coefficient correlation for the two was 0.462 = 0.5. On the other hand, there was a weak negative correlation between Skill_Mix and HAC_Rate, with a correlation coefficient of -0.304. Finally, the correlation between ALOS and HAC_Rate was stronger (Kim et al., 2019).
The histogram above indicates the frequency distribution of the variable HAC_Rate. The histogram shows a normal distribution for the Number of hospital-acquired conditions (HAC) per 1,000 discharges.
Graph 2
| HAC Percentage Occurrence | |
| Vila Health | 11.70% |
| CMS (Centers for Medicare, 2019) | 4.31% |
| AHRQ | 11.50% |
The histogram above indicates the frequency distribution of the variable HAC_Rate. The histogram shows a normal distribution for the Number of hospital-acquired conditions (HAC) per 1,000 discharges.
Graph 2 shows the Chart for the HAC_Rate for every 1000 discharges. From the information provided Vila Health had the highest percentage of occurrence with 11.70% followed by AHRQ 11.50%. In other words, there were high frequencies for the discharges for Vila health compared to other healthcare centers. The rates of discharges for every 1000 persons for both the AHRQ and Vila Health were almost equal. On the other hand, Centers for Medicare (CMS) had the least number of discharges at 4.31%.
Graph 3
Graph 3 shows the percentage of occurrence for the HAC. From the information given, Vila Health has the highest percentage of occurrence with 12% while the Center for Medicare (CMS) had 4%. The percentages were recorded for every 1000 people.
Graph 4
Graph 4 shows the skill mix per HAC rate. From the information given, the skill mix is not proportionate for every HAC rate. In other words, there is no proportion for skill mix for every HAC rate that have been recorded. Skill_Mix and HAC_Rate
Graph 5
From graph 5, there is no proportion between ALOS for every HAC rate. In other words, the correlation between ALOS and HAC_Rate was stronger.
Regression Analysis
Table 4
| ANOVA | |||||
| df | SS | MS | F | Significance F | |
| Regression | 2 | 1450.067077 | 725.0335384 | 83.32006396 | 2.2399E-21 |
| Residual | 92 | 800.5645022 | 8.701788068 | ||
| Total | 94 | 2250.631579 |
From table 4, the significance F-value is 2.2399E-21 which is less than the tabulated F-value, therefore we reject the null hypothesis and conclude that the ANOVA is fit. In other words, the model can be used to determine other variables involved in the research study.
Table 5
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | 108.9131046 | 20.56372053 | 5.296371559 | 8.01413E-07 | 68.07178059 | 149.7544285 |
| Nursing_HPPD | -4.163970655 | 0.348684101 | -11.94195733 | 2.1004E-20 | -4.856487377 | -3.471453933 |
| Skill_Mix | 0.411548722 | 0.353081187 | 1.165592327 | 0.246792365 | -0.289700992 | 1.112798436 |
Table 5 shows the coefficients for the two variables, Nursing_HPPD and Skill_Mix. From the information given, when y = 0, at x-intercept, x becomes 108.91, using the equation y = mx + c, the equation for the linear model becomes:
Y = 108.91 – 4.164(Nursing_HPPD) + 0.411548722 (Skill_Mix). Therefore,
ALOS = 108.91 – 4.164(Nursing_HPPD) + 0.411548722 (Skill_Mix)
Table 6
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95.0% | |||
| Intercept | -6.833852645 | 3.05317804 | -2.238275186 | 0.027589369 | -12.89685914 | -0.770846153 | ||
| HAC_Rate | 0.115257682 | 0.026037909 | 4.426533758 | 2.60411E-05 | 0.063551556 | 0.166963808 |
Table 6 shows the coefficient for the variable, HAC_Rate with the dependent variable ALOS. The linear regression equation can be formulated as follows:
ALOS = 6.8338 + 0.11526 (HAC_Rate)
From the linear regression equation, it is easier to determine dependent variables from the independent variables provided, by simply inserting the value of independent variable in the equation, one is able to determine the variable, ALOS, in the equation. The process of data analysis often involve the consideration of different variables. Before deciding on the statistical tests to perform, it is necessary for the researcher or data analysis to understand the nature of the variables (Mishra et al., 2019).
References
Chen, Z., Cao, Y., Ding, S. X., Zhang, K., Koenings, T., Peng, T., … & Gui, W. (2019). A distributed canonical correlation analysis-based fault detection method for plant-wide process monitoring. IEEE Transactions on Industrial Informatics, 15(5), 2710-2720. https://ieeexplore.ieee.org/abstract/document/8611377
Kim, G. G., Choi, J. H., Park, S. Y., Bhang, B. G., Nam, W. J., Cha, H. L., … & Ahn, H. K. (2019). Prediction model for PV performance with correlation analysis of environmental variables. IEEE Journal of Photovoltaics, 9(3), 832-841. https://ieeexplore.ieee.org/abstract/document/8653358
Kaur, P., Stoltzfus, J., & Yellapu, V. (2018). Descriptive statistics. International Journal of Academic Medicine, 4(1), 60. https://www.ijam-web.org/article.asp?issn=2455-5568;year=2018;volume=4;issue=1;spage=60;epage=63;aulast=Kaur
Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of cardiac anaesthesia, 22(1), 67. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350423/