# quantitative analysis assignment 2

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- For this assignment, students will be given data from quantitative analysis and will be asked to analyze it using Excel, RStuido (BONUS points)Data set:Minnesota Healthcare Database.xlsxMedicare National Data by CountyMN Hospital Report Data by Care Unit FY2013MN HCCIS Imaging Procedures 2013MEPS Dental FilesMEPS Inpatient Stay DatabaseStudents will develop an analysis report, in five main sections, including introduction, research method (research questions/objective, data set, research method, and analysis), results, conclusion and health policy recommendations. This is a 5-6 page individual project report.Here are the main steps for this assignment.Step 1: Students require to submit the topic using topic selection discussion forum by the end of week 1 and wait for instructor approval.Step 2: Develop the research question andStep 3: Run the analysis using EXCEL (RStudio for BONUS points) and report the findings using the assignment instruction.The Report Structure:Start with the1.Cover page (1 page, including running head).Please look at the example http://www.apastyle.org/manual/related/sample-experiment-paper-1.pdf (you can download the file from the class) and http://www.umuc.edu/library/libhow/apa_tutorial.cfm to learn more about the APA style.In the title page include:
- Title, this is the approved topic by your instructor.
- Student name
- Class name
- Instructor name
- Date

2.IntroductionIntroduce the problem or topic being investigated. Include relevant background information, for example;

- Indicates why this is an issue or topic worth researching;
- Highlight how others have researched this topic or issue (whether quantitatively or qualitatively), and
- Specify how others have operationalized this concept and measured these phenomena

Note: Introduction should not be more than one or two paragraphs.Literature ReviewThere is no need for a literature review in this assignment3.Research Question or Research HypothesisWhat is the Research Question or Research Hypothesis?***Just in time information: Here are a few points for Research Question or Research HypothesisThere are basically two kinds of research questions: testable and non-testable. Neither is better than the other, and both have a place in applied research.Examples of non-testable questions are:

*How do managers feel about the reorganization?**What do residents feel are the most important problems facing the community?*Respondents’ answers to these questions could be summarized in descriptive tables and the results might be extremely valuable to administrators and planners. Business and social science researchers often ask non-testable research questions. The shortcoming with these types of questions is that they do not provide objective cut-off points for decision-makers.In order to overcome this problem, researchers often seek to answer one or more testable research questions. Nearly all testable research questions begin with one of the following two phrases:*Is there a significant difference between …?**Is there a significant relationship between …?*For example:Is there a significant relationship between the age of managers? and their attitudes towards the reorganization?A research hypothesis is a testable statement of opinion. It is created from the research question by replacing the words “Is there” with the words “There is,” and also replacing the question mark with a period. The hypotheses for the two sample research questions would be:*There is a significant relationship between the age of managers and their attitudes towards the reorganization.*It is not possible to test a hypothesis directly. Instead, you must turn the hypothesis into a null hypothesis. The null hypothesis is created from the hypothesis by adding the words “no” or “not” to the statement. For example, the null hypotheses for the two examples would be:*There is no significant relationship between the age of managers*and their attitudes towards the reorganization.There is no significant difference between white and minority residentswith respect to what they feel are the most important problems facing the community.All statistical testing is done on the null hypothesis…never the hypothesis. The result of a statistical test will enable you to either:1) reject the null hypothesis, or2) fail to reject the null hypothesis. Never use the words “accept the null hypothesis.” *Source: StatPac for Windows Tutorial. (2017). User’s Guide; Formulating Hypotheses from Research Questions. Retrieved May 17, 2019 from https://statpac.com/manual/index.htm?turl=formulatinghypothesesfromresearchquestions.htmWhat does significance really mean?â€œSignificance is a statistical term that tells how sure you are that a difference or relationship exists. To say that a significant difference or relationship exists only tells half the story. We might be very sure that a relationship exists, but is it a strong, moderate, or weak relationship? After finding a significant relationship, it is important to evaluate its strength. Significant relationships can be strong or weak. Significant differences can be large or small. It just depends on your sample size.To determine whether the observed difference is statistically significant, we look at two outputs of our statistical test:*P-value:*The primary output of statistical tests is the p-value (probability value). It indicates the probability of observing the difference if no difference exists.The p-value from above example, 0.9926, indicates that we DO NOT expect to see a meaningless (random) difference of 5% or more in â€˜hospital bedsâ€™ only about 993 times in 1000 there is no difference (0.9926*1000=992.6 ~ 993).Note: This is an example from the week1 exercise.The p-value from above example, 0.0001, indicates that weâ€™d expect to see a meaningless (random) â€˜number of the employees on payerâ€™ difference of 5% or more only about 0.1 times in 1000 (0.0001 * 1000=0.1). CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-40.82 ; 40.44]):CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-382.16 ; 125.53]):The boundaries of this confidence interval around the difference also provide a way to see what the upper [40.44] and lower bounds [-40.82].*As a summary:*â€œStatistically significant means a result is unlikely due to chance.The p-value is the probability of obtaining the difference we saw from a sample (or a larger one) if there really isnâ€™t a difference for all users.Statistical significance doesnâ€™t mean practical significance. Only by considering context can we determine whether a difference is practically significant; that is, whether it requires action.The confidence interval around the difference also indicates statistical significance if the interval does not cross zero. It also provides likely boundaries for any improvement to aide in determining if a difference really is noteworthy.With large sample sizes, youâ€™re virtually certain to see statistically significant results, in such situations, itâ€™s important to interpret the size of the differenceâ€(“Measuring U”, 2019).*ResourceMeasuring U. (2019). Statistically significant. Retrieved May 17, 2019 from: https://measuringu.com/statistically-significant/Small sample sizes often do not yield statistical significance; when they do, the differences themselves tend also to be practically significant; that is, meaningful enough to warrant action.4.Research MethodDiscuss the Research Methodology (in general). Describe the variable or variables that are being analyzed. Identify the statistical test you will select to analyze these data and explain why you chose this test. Summarize your statistical alternative hypothesis. This section includes the following sub-sections:*a)Describe the Dataset*Example: The primary source of data will be HOSPITAL COMPARE MEDICARE DATA (APA formatted in-text citation). This dataset provides information on hospital characteristics, such as: Number of staffed beds, ownership, system membership, staffing by nurses and non-clinical staff, teaching status, percentage of discharge for Medicare and Medicaid patients, and information regarding the availability of specialty and high-tech services, as well as Electronic Medical Record (EMR) use (Describe dataset in 2-3 lines, Google the dataset and find the related website to find more information about the data).Also, describe the sample size; for example, â€œThe writer is using Medicare data-2013, this data includes 3000 obs. for all of the hospitals in the US.â€*b)Describe Variables*Next, review the database you selected and select a variable or variables that are a â€œbest-fit.â€ That is, choose a variable that quantitatively measures the concept or concepts articulated in your research question or hypothesis. Return to your previously stated Research Question or Hypothesis and evaluate it considering the variables you have selected. (See the sample Table 1).Table 1. List of variables used for the analysis*Variable**Definition**Description**of code**Source**Year*Total Hospital Beds Total facility beds set up and staffedat the end of the reporting period Numeric MN Data 2013 â€¦. â€¦.. Source: UMUC, 2019 ***Just in time information:To cite a dataset, you can go with two approaches:First, look at the note in the dataset for example;Medicare National Data by County. (2012). Dartmouth Atlas of Health Care, A Second, use the online citation, for example:Zare, H., (2019, May). MN Hospital Report Data. Data posted in University of Maryland University College HMGT 400 online classroom, archived at: http://campus.umuc.eduSee two examples describing the variables from Minnesota Data:Table 2. Definition of variables used in the analysis

*Variable**Definition**Description**of code**Source**Year*hospital_beds Total facility beds set up and staffedat the end of the reporting period Numeric MN data 2013 year FY Categorical MN data 2013 Source: UMUC, 2019

*c)Describe the Research Method for Analysis*First, describe the research method as a general (e.g., this is a quantitative method and then explain about this method in about one paragraph. If you have this part in the introduction, you do not need to add here).Then, explain the statistical method you plan to use for your analysis (Refer to content in week 3 on Biostatistics for information on various statistical methods you can choose from).Example:Hypothesis: AZ hospitals are more likely to have lower readmission rates for PN compared to CA.Research Method: To determine whether Arizona hospitals are more likely to have lower readmission rate than California, we will use a t-test, to determine whether differences across hospital types are statistically significant (You can change the test depends on your analysis).*d)Describe**statistical package*Add one paragraph for the statistical package, e.g., Excel or RStudio.5. ResultsDiscuss your findings considering the following tips:â–ª Why you needed to see the distribution of data before any analysis (e.g., check for outliers, finding the best fit test; for example, if the data had not a normal distribution, you canâ€™t use the parametric test, etc., so just add 1 or 2 sentences).â–ª Did you eliminate outliers? (Please write 1 or 2 sentences, if applicable).â–ª How many observations do you have in your database and how many for selected variables, report % of missing.â–ª When you are finished with this, go for the next steps:Present the results of your statistical analysis; include any relevant statistical information (summary tables, including N, mean, std. dev.). Make sure to completely and correctly name all your columns and rows, tables and variables. For this part you could have at least 1-2 tables and 1-2 figures (depending on your variables bar-chart, pi-chart, or scatter-plot), you can use a table like this:Table 3. Descriptive analysis to compare % of BL in Medicare beneficiary, MD vs. VA- 2013*Variable**Obs.**Mean**SD**P-value*Per of Lipid in MD 24 83.20 2.32

0.4064

Per of Lipid in VA 124

82.69

4.41

Source: UMUC, 2019When you have tables and plots ready, think about your finding and state the statistical conclusion. That is, do the results present evidence in favor or the null hypothesis or evidence that contradicts the null hypothesis?6.Conclusion and DiscussionReview your research questions or hypothesis. How has your analysis informed this question or hypothesis? Present your conclusion(s) from the results (presented above) and discuss the meaning of this conclusion(s) considering the research question or hypothesis presented in your introduction. At the end of this section, add one or two sentences and discuss the limitations (including biases) associated with this analysis and any other statements you think are important in understanding the results of this analysis. ReferencesInclude a reference page listing the bibliographic information for all sources cited in this report. This information should be consistent with the requirements specified in the American Psychological Association (APA) format and style guide.

## RStudio: Working toward BONUS points

In this week exercise, you will learn more about the regression model and

*lm function*in RStudio Reading Materials:Master codes:The following codes are three examples to run the analysis for Individual Assignment 2.The dataset of choice used for the analysis in the class should be downloaded from week 6 (Content, chose from Minnesota Health Care Database; MEPS Dental Files: or MEPS Inpatient Stay Database) and converted to CSV (see following steps).Depending on your topic, you may need to use one of the following datasets listed in the assignment instructions.

- Convert the Excel file to CSV file
- Remove the three-digit separator (,) from the data for example 1,000 must be 1000 in data, to do that right-click on related column and then choose â€˜Generalâ€™ category or use the â€œNumberâ€™ but do not click on â€˜Use 1000 separatorâ€™ (See screenshot):

- When you have your data, then change the location of the file in sample codes, modify the variables if needed and run codes.
- The SINK command does not store plots, so you need to copy plots from the plot-panel to your assignment.
- You can add your codes as an appendix to your assignment (after “Reference” list).

*Sample Code#1: # Data: MNHCCISImagingProcedures2013Suggested Topic: Comparing MRI Procedures and PET.CT Procedures in MN # Step 1: Read your data# Note: I changed this to run codes you need to back to your original codes for reading data.Minnesota <- read.csv(“C:/UMUC/MNHCCISImagingProcedures2013.csv”, header=T, sep = ‘,’)#Step 2: See the variables’ namesnames(Minnesota)# Step 3: See the Meansmean(Minnesota$MRI.Procedures, na=T)mean(Minnesota$PET.CT.Procedures, na=T) # Step 4: See the SDssd(Minnesota$MRI.Procedures, na=T)sd(Minnesota$PET.CT.Procedures, na=T)# Step 5# N mytable <- table(Minnesota$MRI.Procedures)summary(mytable)mytable <- table(Minnesota$PET.CT.Procedures)summary(mytable) # Step 6: t-test resultst.test(Minnesota$MRI.Procedures, Minnesota$PET.CT.Procedures, paired = F)# Step 7: Box Plotboxplot(Minnesota$MRI.Procedures, Minnesota$PET.CT.Procedures, main=”Figure 1. Comparing MRI & PET.CT Procedures in MN”,font.main = 1, xlab=”Number of Admissions”, ylab=”Number”, col=topo.colors(2))legend(“topleft”, inset=.02, c(“MRI”,”PET”), fill=topo.colors(2), horiz=TRUE, cex=0.8)# After looking at the BOX plot if there are outliers, then you need to subset the data, using the following code:Minnesota1 <- subset(Minnesota, Minnesota$MRI.Procedures<10000) # Step 8: Box Plot (Now, see the data after removing outlieres)boxplot(Minnesota1$MRI.Procedures, Minnesota1$PET.CT.Procedures, main=”Figure 2. Comparing MRI & PET.CT Procedures in MN after Eliminating>10000″, font.main = 1, xlab=”Number of Admissions”, ylab=”Number”, col=topo.colors(2))legend(“topleft”, inset=.02, c(“MRI”,”PET”), fill=topo.colors(2), horiz=TRUE, cex=0.8)#Step 9: See the Density Curves Acute care admissionsAdmissions1 <- density(Minnesota1$MRI.Procedures, na.rm=T)lines(Admissions1, col = “red”)Admissions <- density(Minnesota1$PET.CT.Procedures, na.rm=T)plot(range(Admissions$x), range(Admissions$y), type = “n”, xlab = “$ Value”, main=”Figure 3. Density Curve for MRI & PET.CT Procedures in MN”, cex.main =.8, ylab = “Density”)lines(Admissions, col = “red”)lines(Admissions1, col = “blue”)labels <- c(“MRI”, “PET”)legend (“topright”, inset = .1, title = “Legend”, labels, lwd = 2, col = c(“red”, “Blue”)) # Step 10: t-test results for Subset datat.test(Minnesota1$MRI.Procedures, Minnesota1$PET.CT.Procedures, paired = F) *Sample Code #2:# Data: MinnesotaAdmissionSuggested Topic: Comparing Managed and Non Managed Care in MN# Step #1: read dataMNTA <-read.csv(“C:/UMUC/MinnesotaAdmission.csv”, header=T, sep = ‘,’)# Step #2: See the variables’ namesnames (MNTA)# Step #3: See the distribution for all databoxplot (MNTA$Total.Managed.Care.Admissions, MNTA$Total.Non.Managed.Care.Admissions, na.rm=T, main=”Figure 1. ComparingA dmission “, xlab=”Type of Admission”, ylab=”# of Admission”, names = c(“Managed”, “Non-Managed”)) # Step #4: As you see there is two outliers in data we need to remove them firstMNSUBSET <- subset(MNTA, MNTA$Total.Managed.Care.Admissions<50000, select=c(Total.Managed.Care.Admissions, Total.Non.Managed.Care.Admissions, Licensed.Beds)) # Step #5: Check to see the distribution of data after removing the outliersboxplot (MNSUBSET$Total.Managed.Care.Admissions, MNSUBSET$Total.Non.Managed.Care.Admissions, na.rm=T, main=”Figure 2. Pl Add title -After removing Outliers”, xlab=”Type of Admission”, ylab=”# of Admission”, names = c(“Managed”, “Non-Managed”)) # Step #6: Now, you can compute the admission rate#7-1 First you need to generate the Total AdmissionsMNSUBSET$TotalAdmissions <- MNSUBSET$Total.Managed.Care.Admissions + MNSUBSET$Total.Non.Managed.Care.Admissions #7-2 Then, you need to generate the Admission RatioMNSUBSET$Total.Managed.Care.Admissions.Ratio <- MNSUBSET$Total.Managed.Care.Admissions/MNSUBSET$TotalAdmissionsMNSUBSET$Total.Non.Managed.Care.Admissions.Ratio <- MNSUBSET$Total.Non.Managed.Care.Admissions/MNSUBSET$TotalAdmissions #7 See the variablesnames (MNSUBSET) #Step 8: See Meansmean(MNSUBSET$Total.Managed.Care.Admissions.Ratio, na.rm=T)mean(MNSUBSET$Total.Non.Managed.Care.Admissions.Ratio, na.rm=T) #Step 9: See SDssd(MNSUBSET$Total.Managed.Care.Admissions.Ratio, na.rm=T)sd(MNSUBSET$Total.Non.Managed.Care.Admissions.Ratio, na.rm=T) #Step 10: See Nsmytable <- table(MNSUBSET$Total.Managed.Care.Admissions.Ratio)summary(mytable) mytable <- table(MNSUBSET$Total.Non.Managed.Care.Admissions.Ratio)summary(mytable) mytable <- table(MNSUBSET$Licensed.Beds)summary(mytable) #Step 11: test Hypothesist.test(MNSUBSET$Total.Managed.Care.Admissions.Ratio, MNSUBSET$Total.Non.Managed.Care.Admissions.Ratio, na.rm=T) #Step 12: See BoxPLot of Ratioboxplot (MNSUBSET$Total.Managed.Care.Admissions.Ratio, MNSUBSET$Total.Non.Managed.Care.Admissions.Ratio, na.rm=T, main=”Figure 3. Comparing Admission Rate: Managed vs. Non-Managed Care”, xlab=”Type of Admission”, ylab=”# of Admission”, names = c(“Managed”, “Non-Managed”))*Sample Code #3:# Data: MedicareNationalDataCSVSuggested Topic: Comparing Eye Exam Between Black and White Medicare Beneficiaries # Step 1: Read dataMND <- read.csv(“C:/UMUC/MedicareNationalDataCSV.csv”, header=T, sep = ‘,’) #Step 2: See the variables’ namesnames (data) #Step 3: See meansmean(MND$per.eyeexam.black, na.rm=T)mean(MND$per.eyeexam.white, na.rm=T) #Step 4: See SDssd(MND$per.eyeexam.black, na.rm=T)sd(MND$per.eyeexam.white, na.rm=T) #Step 5: See Observations — FLmytable <- table(MND$per.eyeexam.black)summary(mytable) #Step 6: See Observations — GAmytable <- table(MND$per.eyeexam.white)summary(mytable) #Step 7 test the FL and GAt.test(MND$per.eyeexam.black, MND$per.eyeexam.white, na.rm=T) #Step 8 look at the box-plotboxplot(MND$per.eyeexam.black, MND$per.eyeexam.white, main=”Figure 1. Comparing Eye-Exam, between Black & White)”, ylab=”% of Eye Exam “, names = c(“Black”, “White”)) #Step 9 See density of data (this would not run correctly for me)black <- density(MND$per.eyeexam.black, na.rm=T)white <- density(MND$per.eyeexam.white, na.rm=T)plot(range(black$x, white$x), range(black$y, white$y), type = “n”, xlab = “Percent”, main=”Figure 2. Comparing density plots between Black & White”, cex.main =.8, ylab = “Density”)lines(black, col = “red”)lines(white, col = “blue”)labels <- c(“Black”, “White”)legend (“topleft”, inset = .1, title = “Legend”, labels, lwd = 2, col = c(“red”, “blue”))

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