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Description These instructions apply to all parts Points are associated with all  instructions; not following the instructions will result in a loss of points. When it is appropriate to show decimals, you should show 2 decimal places; use of decimal  places should be consistent within a column (or variable). Use cell referencing in all formulas,  and use functions that we have discussed in class whenever possible; a significant part of your  grade is dependent on your use of appropriate cell referencing and use of functions. While you  may already be familiar with Excel and know other methods for answering the questions, for  this assignment you must use the functions covered in class.  TIPS: Make it easy to locate all requested information. You can use bold or another font color  or try using the paint can to fill the cell with color ( ) Use column/row titles to describe the  data included.  Question (40 points possible) Using the Second Quarter 2023 California Hospital Quarterly Financial and Utilization data (the  first tab in the “Data for Assignment 1”), complete parts A through L.  A. Copy the San Diego County and Los Angeles County hospitals to a new worksheet within this  worksheet. (Copy the rows holding the variable names and Los Angeles County and San  PH 307 2 Diego County hospitals as described in the Watch & Practice: Practice Skills for Assignment  1 video; do not copy the whole worksheet.) (3 pts)  B. Sort the hospitals by county. (2 pts)  C. Name this tab Los Angeles and San Diego. (2 pts)  D. Hide the columns that you won’t need to answer this question; do not delete columns. (2  pts)  E. In a new column, calculate the net outpatient revenue (est.) for each hospital located in San  Diego County and Los Angeles County. (Hint: You will need to refer to the OSHPD documentation to determine how to calculate the net outpatient revenue.) Format with  1000 separator. (5 pts)  F. Using the appropriate function, calculate the mean net outpatient revenue (est.) for San  Diego and Los Angeles Counties combined. This can be placed in another new column or at  the bottom of the list of hospitals. Make certain to label this as “Mean.” (5 pts) G. Using the appropriate function, calculate the median net outpatient revenue (est.) for San  Diego and Los Angeles Counties combined. This can be placed under the mean. Make  certain to label this as “Median.” (5 pts)  H. In the next empty column, use the appropriate function discussed in the videos to recode  the net outpatient revenue for each hospital as follows: 1) 0 if the net outpatient revenue is less than $30,000,000; 2) 1 if the net outpatient revenue is equal to or greater than $30,000,000. Cell reference the criteria used in part H. (5 pts) I. In the next empty column, calculate the cost-to-charge ratio for each hospital located in San  Diego County and Los Angeles County. (Refer to the OSHPD documentation to determine  the correct formula.) Format as a percent. (5 pts)  J. Set up this worksheet as follows: 1) Include a header with your first and last names and the date the assignment is due in  the top right corner (1 pt); 2) Set the variable names to repeat at top and the facility number and name to repeat at  left for printing (2 pts); 3) Set the gridlines to print. (1 pt)  K. All numbers should be right-aligned. (1 pt) L. Show only the facility number, facility name, county name, type_control, the columns used  in the calculation, and the columns holding the information you calculated. (1 pt)

Description Assignment Setting The items for sale in many retail stores sometimes ring up at a different price at the cash register compared to the price marked on the shelf. This goes unnoticed by many consumers. But whether the mistakes are intentional or not, the retailers get checked by government inspectors (for overcharges only) and can potentially be fined for the mistakes if their error rate is high enough.  The retailers also run the risk of receiving bad publicity through the press. As an example, in one such inspection: “during Walmart’s second inspection, the price of a ceiling fan was off by $30. The shelf price listed the fan at $19.99, but it rang up as $49.97 at the register.” For more, read: Inspector: One in 10 NC stores ring up wrong prices[1]. To avoid the fine and the public embarrassment that follows for being caught, each retailer will self-inspect their records. They typically use the same process as the government inspectors by selecting a random sample of items for cross-checking the register and the shelf prices. In a recent self-check, the store manager had her employees compare prices on 230 different items which were picked at random for cross-check. The employees recorded the shelf price and the price that appeared on the cash register. You can find these results in the MS Excel file titled “Price-Check”. The government inspector will not fine companies that have an error rate of less than 2%. Questions  Question 1 Based on the data in this study, what is the estimate of proportion of items that will ring up at higher prices and thus be counted as a violation? (Hint: Should your estimate be a single number or a range to provide appropriate background for managerial decision-making?) Insert answer here. Question 2 Based on your answer for Question 1, if an inspector showed up today, would the store manager be 95% confident that the store will pass? Why, or why not? Insert answer here. Question 3 If the manager wants to have a margin error of 0.5% (.005), then how many items need to be cross-checked? Insert answer here. Question 4 The manager doesn’t believe she has the resources to do as many cross-checks to ensure a margin of error of 0.5%. She has noticed that in the items checked, some items are ringing up for less at the cash register, so technically, a customer could end up with a bill that is less than what they were expecting. She wonders if she would be able to defend her store against fines based on the average amount customers are being wrongly charged (either over- or undercharged). 

Description Assignment: https://intervisualtechnology.us/pxudpothrb/167/13252/Unit_2_Quadratic_Functions_Unit_Project.pdf

Description Each student is assigned a unique data set consisting of a simple random sample extracted from the 2022 General Social Survey (GSS) cross-section data. For more information about the GSS, please visit https://gss.norc.org/pages/GSS50.aspx. Information about the variables you will analyze are found here: GSS2022 - Variable Descriptions.pdf Instructions for this assignment are provided within the SIP Report Template below. Download the template below, replace the title with your own, and use the headings provided within the template. After completing your report, please delete the template instructions before submitting your work. Download: SIP Report Template.docx

Description The topic of this assignment is " Comparing percent of Medicare, Part A eligible beneficiaries between California and South Carolina.*********I found out there is NO DATA for South Carolina, so it was changed to North Carolina Students will develop an analysis report, in five main sections, including introduction, research method (includes research questions/objective, data set, research method, and analysis), results, conclusion and health policy recommendations.  Here are the main steps for this assignment. Step 1: Students are required to submit the topic using topic selection discussion forum by the end of week 1 and wait for the instructor's approval. Step 2: Develop the research question and Step 3: Run the analysis using EXCEL (RStudio for BONUS points) and report the findings using the assignment instruction. THE REPORT STRUCTURE: Start with the 1.Cover page (1 page, including running head). Please look at the example Sample paper (you can download the file from the class) and SUCCEED AGAIN WITH A UMGC ONLINE PROGRAM 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.Introduction Introduce the problem or topic being investigated. Include relevant background information. For example; Indicate 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 Review There is no need for a literature review in this assignment 3.Research Question or Research Hypothesis What is the Research Question or Research Hypothesis? ***Just in time information: Here are a few points for Research Question or Research Hypothesis There 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 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 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 residents with 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, or 2) 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 Survey Research What does significance really mean? “Significance 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. 4.Research Method Discuss the Research Methodology (in general). Describe the variable or variables that are being analyzed. Identify the statistical test selected to analyze these data and explain why you chose this test. Summarize your statistical alternative hypothesis. This section includes the following sub-sections: a)Description of the Dataset Example: The primary source of data was the HOSPITAL COMPARE MEDICARE DATA (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, “Medicare data-2013 was used for this analysis. This data includes 3000 obs. for all of the hospitals in the US.” b)Description of Variables Next, review the database you selected and select a variable or variables that are a “best-fit.” 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 Table 2. Definition of variables used in the analysis c)Description of the Research Method for Analysis First, describe the research method as a general (e.g., this is a quantitative method and then explain 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 used 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 rates than California, we used the t-test. This test was used to determine whether any differences observed across hospital types are statistically significant (You can change the test depending on your analysis). d)Description of Statistical Package Add one paragraph for the statistical package, e.g., Excel or RStudio. 5. Results Discuss your findings considering the following tips: ? 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 can’t use the parametric test, etc., so just add 1 or 2 sentences). ? Did you eliminate outliers? (Please write 1 or 2 sentences, if applicable). ? How many observations do you have in your database and how many for selected variables, report % of missing. ? 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, pie-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 When you have tables and plots ready, think about your finding and state the statistical conclusion. That is, do the results present evidence in favor of the null hypothesis or evidence that contradicts the null hypothesis? 6.Discussion and Conclusion Review 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(ese) 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.

Description Topic: Comparing the average percentage of female Medicare enrollees aged 67-69 years having at least one mammogram over a two year period between Blacks and Whites. Task: Select and analyze ONE peer-reviewed article. Summarize and extract bias-table. Please see the attachments for details. 

Description MAT 240 Project Two Guidelines and Rubric Competency In this project, you will demonstrate your mastery of the following competency: Apply statistical techniques to address research problems Perform hypothesis testing to address an authentic problem Overview In this project, you will apply inference methods for means to test your hypotheses about the housing sales market for a region of the United States. You will use appropriate sampling and statistical methods. Scenario You have been hired by your regional real estate company to determine if your region’s housing prices and housing square footage are significantly different from those of the national market. The regional sales director has three questions that they want to see addressed in the report: Are housing prices in your regional market lower than the national market average? Is the square footage for homes in your region different than the average square footage for homes in the national market? For your region, what is the range of values for the 95% confidence interval of square footage for homes in your market? You are given a real estate data set that has houses listed for every county in the United States. In addition, you have been given national statistics and graphs that show the national averages for housing prices and square footage. Your job is to analyze the data, complete the statistical analyses, and provide a report to the regional sales director. You will do so by completing the Project Two Template located in the What to Submit area below. Directions Introduction Region: Start by picking one region from the following list of regions: West South Central, West North Central, East South Central, East North Central, Mid Atlantic Purpose: What is the purpose of your analysis? Sample: Define your sample. Take a random sample of 500 house sales for your region. Describe what is included in your sample (i.e., states, region, years or months). Questions and type of test: For your selected sample, define two hypothesis questions (see the Scenario above) and the appropriate type of test for each. Address the following for each hypothesis: Describe the population parameter for the variable you are analyzing. Describe your hypothesis in your own words. Identify the hypothesis test you will use (1-Tail or 2-Tail). Level of confidence: Discuss how you will use estimation and confidence intervals to help you solve the problem. 1-Tail Test Hypothesis: Define your hypothesis. Define the population parameter. Write null (Ho) and alternative (Ha) hypotheses. Note: For means, define a hypothesis that is less than the population parameter. Specify your significance level. Data analysis: Summarize your sample data using appropriate graphical displays and summary statistics and confirm assumptions have not been violated to complete this hypothesis test. Provide at least one histogram of your sample data. In a table, provide summary statistics including sample size, mean, median, and standard deviation. Note: For quartiles 1 and 3, use the quartile function in Excel: =QUARTILE([data range], [quartile number]) Summarize your sample data, describing the center, spread, and shape in comparison to the national information (under Supporting Materials, see the National Summary Statistics and Graphs House Listing Price by Region PDF). Note: For shape, think about the distribution: skewed or symmetric. Check the conditions. Determine if the normal condition has been met. Determine if there are any other conditions that you should check and whether they have been met. Note: Think about the central limit theorem and sampling methods. Hypothesis test calculations: Complete hypothesis test calculations. Calculate the hypothesis statistics. Determine the appropriate test statistic (t). Note: This calculation is (mean – target)/standard error. In this case, the mean is your regional mean, and the target is the national mean. Calculate the probability (p value). Note: This calculation is done with the T.DIST function in Excel: =T.DIST([test statistic], [degree of freedom], True) The degree of freedom is calculated by subtracting 1 from your sample size. Interpretation: Interpret your hypothesis test results using the p value method to reject or not reject the null hypothesis. Relate the p value and significance level. Make the correct decision (reject or fail to reject). Provide a conclusion in the context of your hypothesis. 2-Tail Test Hypotheses: Define your hypothesis. Define the population parameter. Write null and alternative hypotheses. Note: For means, define a hypothesis that is not equal to the population parameter. State your significance level. Data analysis: Summarize your sample data using appropriate graphical displays and summary statistics and confirm assumptions have not been violated to complete this hypothesis test. Provide at least one histogram of your sample data. In a table, provide summary statistics including sample size, mean, median, and standard deviation. Note: For quartiles 1 and 3, use the quartile function in Excel: =QUARTILE([data range], [quartile number]) Summarize your sample data, describing the center, spread, and shape in comparison to the national information. Note: For shape, think about the distribution: skewed or symmetric. Check the assumptions. Determine if the normal condition has been met. Determine if there are any other conditions that should be checked on and whether they have been met. Note: Think about the central limit theorem and sampling methods. Hypothesis test calculations: Complete hypothesis test calculations. Calculate the hypothesis statistics. Determine the appropriate test statistic (t). Note: This calculation is (mean – target)/standard error. In this case, the mean is your regional mean, and the target is the national mean.] Determine the probability (p value). Note: This calculation is done with the TDIST.2T function in Excel: =T.DIST.2T([test statistic], [degree of freedom]) The degree of freedom is calculated by subtracting 1 from your sample size. Interpretation: Interpret your hypothesis test results using the p value method to reject or not reject the null hypothesis. Compare the p value and significance level. Make the correct decision (reject or fail to reject). Provide a conclusion in the context of your hypothesis. Comparison of the test results: Revisit Question 3 from the Scenario section: For your region, what is the range of values for the 95% confidence interval of square footage for homes? Calculate and report the 95% confidence interval. Show or describe your method of calculation.

Description Graphs Step One: Find a graph (not a table, a graph) that uses real data. Make sure they do cite a source for their data. Choose your source from one of these sources: Pew Research Center: http://www.pewresearch.org/Links to an external site. Gallup Poll News: http://news.gallup.com/home.aspxLinks to an external site. (Some articles have graphs, many do not - do not pay for access.) Flowing Data: http://flowingdata.com/Links to an external site. OK Cupid's Blog Data Center: https://theblog.okcupid.com/tagged/dating-data-centerLinks to an external site.  (If this link doesn't work, just google "OK Cupid trends") Let's discuss the quality of the graphs you find.  Step Two: In your main post: Post the link & an image of the actual graph* and start the discussion by identifying and commenting on: The source of the data - can you tell if it is a good and known source? Can we trust that their information is accurate? What is good about the graph? What is not working in the graph? What is(are) the main variable(s) and is this the right or appropriate graph for it(them)? (Be sure to identify level of measurement for each variable since that's what identifies appropriateness..) Step Three: Comment on the graphs that others post.

Description Choose either topic 1 or topic 2 for your initial post. Respond to either for your peer reply post. Topic 1: One goal of statistics is to identify relations among variables. What happens to one variable as another variable changes? Does a change in one variable cause a change in another variable? These questions can lead to powerful methods of predicting future values through linear regression.   It is important to note the true meaning and scope of correlation, which is the nature of the relation between two variables. Correlation does not allow us to say that there is any causal link between the two variables. In other words, we cannot say that one variable causes another; however, it is not uncommon to see such use in the news media. An example is shown below. Here we see that, at least visually, there appears to be a relation between the divorce rate in Maine and the per capita consumption of margarine. Does this data imply that all married couples in Maine should immediately stop using margarine to stave off divorce? Common sense tells us that is probably not true.   This is an example of a spurious correlation in which there appears to be a relation between the divorce rate and margarine consumption, but it is not a causal link.   The appearance of such a relation could merely be due to coincidence or perhaps another unseen factor. What is one instance where you have seen correlation misinterpreted as causation? Please describe. This serves as your initial post to the discussion (if you choose topic 1) and is due by 11:59 p.m. EST on Saturday.   -OR-   Topic 2: Linear regression is used to predict the value of one variable from another variable. Since it is based on correlation, it cannot provide causation. In addition, the strength of the relationship between the two variables affects the ability to predict one variable from the other variable; that is, the stronger the relationship between the two variables, the better the ability to do prediction.   For example, given this data on literacy and undernourishment, we can create a scatter plot which shows that there seems to be a relationship between the variables. The graph implies that as literacy (x) increases, the percentage of people who are undernourished (y) decreases.   We can calculate a best-fit line equation and use this to predict that the undernourishment rate we would expect in a country with a percentage literacy rate of 87% would be y = (-0.5539)(87)+55.621 or about 7.43 percent.   What is one instance where you think linear regression would be useful to you in your workplace or chosen major? Please describe why and how it would be used. This serves as your initial post to the discussion (if you choose topic 2) and is due by 11:59 p.m. EST on Saturday.  

Description CASE STUDY: FACTORIAL ANALYSIS OF VARIANCE TEMPLATE INSTRUCTIONS To successfully complete this case study assignment, all of the following instructions must be adhered to. Only paste tables/figures that contain pertinent information. Only paste tables/ figures where you are instructed to do so, and use only relevant data from tables, as well as your response to each question. In the write-up section, all information must be provided in paragraph format. When statistical values are discussed, the numerical value must be given and a reference must be made to the table(s)/figure(s) where the value(s) can be found. An example of this is “heart rate was statistically greater during running versus cycling (189 vs. 143 bpm) as indicated