Description
For incomes that are more than €20,000, the tax is €2000 plus 40% of the amount over€20,000.
f(x) =
if 0 ≤ x ≤ 20,000 | |
if x > 20,000 |
(b) Find
f −1.
f −1(x) =
if 0 ≤ x ≤ 2000 | |
if x > 2000 |
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Explanation & Answer
f(x)=10/100 * x
f(x)=40/100 * x +2000
similarly 100x/10=10x
and (x-2000)*100/40 are your four answers,thanks
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100%
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GCU Food Choices in Areas with Consistent Behavioral Differences Analysis
In public health, data are often gathered from separate groups in order to describe health-related behavior for a topic of ...
GCU Food Choices in Areas with Consistent Behavioral Differences Analysis
In public health, data are often gathered from separate groups in order to describe health-related behavior for a topic of interest. In practice, a larger sample is often surveyed, and then focus groups or interviews can be conducted with a smaller subset of the sample. The quantitative survey data from a larger group helps to identify health-related trends and patterns within the sample group. The qualitative data collected with the smaller group complements the quantitative survey data and helps to determine why and how a phenomenon exists. The cumulative findings can then help public health professionals form a conclusion about the health issue and inform future public health research, policy, and practice.
The purpose of this assignment is to analyze qualitative and quantitative data from two separate groups, propose a research question, and then disseminate your findings in a mixed-methods manuscript. The survey and focus group are both on the topic of nutrition. The quantitative data is adapted from the Youth Risk Behavior Surveillance System (YRBSS), and the qualitative data is from a focus group transcript in which 9th grade girls discussed healthy eating.
For this assignment, you will use:
IBM SPSS Statistics and the "Youth Risk Behavior Surveillance System Dataset" to conduct a basic quantitative statistical analysis.
The "9th Grade Girls Healthy Food Focus Group Transcription" to conduct a qualitative analysis and identify key themes.
Part 1: Analyzing Data
Using the resources indicated above, conduct the following analyses and record the results on the "Results and Outputs" template. Attach this document as an Appendix in your paper.
Qualitative Analysis: Read the "9th Grade Girls Healthy Food Focus Group Transcription." This transcript is from a focus group of 9th grade girls discussing healthy eating with the moderator. Generate codes and summarize the qualitative data.
Quantitative Analysis:
Review the "Youth Risk Behavior Surveillance System Dataset." Identify two or three variables of interest and identify a study topic for your paper. Conduct a literature search for three to five peer-reviewed articles from the last 5 years that have studies supporting your topic. Develop a research question for your study based on the selected variables. The research question should demonstrate support for the focus group results and should be supported by existing literature.
Prepare the data to complete the analysis based on your research question.
Using SPSS, conduct descriptive statistics to summarize the sample.
Using SPSS, select an appropriate quantitative inferential statistical test to analyze the data.
Part 2: Reporting Data
Prepare a 1,250-1,500 word manuscript to disseminate the findings of your proposed study. The study should be based on your proposed research question and supported by the findings from the qualitative and quantitative analyses above, as well as by current literature. Include the following:
Abstract
A 150-250 word summary of the manuscript.
Introduction
Summarize the purpose of the research, the problem being addressed, and your proposed research question.
Support your summary using three to five peer-reviewed articles from the last 5 years that are relevant to your topic and that support why the study is being conducted.
Methods
Qualitative Methods (Focus Group)
Describe the focus group sample and data collection process.
Describe the process used to analyze the qualitative data.
Quantitative Methods (YRBSS Survey)
Discuss how the data were collected.
Identify and describe the variables used in the analysis.
Describe the descriptive and inferential statistical tests that were conducted.
Identify the software that was used to conduct the analysis.
Results
Qualitative Results (Focus Group)
Summarize the results of the qualitative analysis from the "9th Grade Girls Healthy Food Focus Group Transcription."
Quantitative Results (YRBSS Survey)
Summarize the sample using the results of the descriptive statistics.
Describe the main outcomes of the inferential statistical analysis.
Include at least one table or figure to support the results.
Discussion
Interpret the study results in relation to the related literature and study purpose.
Discuss the study limitations.
Based on the interpreted results of the study, provide recommendations for future public health research, policy, or practice.
Appendix
"Results and Outputs" analysis document.
General Requirements
You are required to cite to a minimum of three sources to complete this assignment. Sources must be published within the last 5 years and appropriate for the assignment criteria and public health content.
Prepare this assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
You are required to submit this assignment to LopesWrite. A link to the LopesWrite technical support articles is located in Class Resources if you need assistance.
Benchmark Information
This benchmark assignment assesses the following programmatic competencies:
Master Public Health
2.2: Select quantitative and qualitative data collection methods appropriate for a given public health context.
2.3: Analyze quantitative and qualitative data using biostatistics, informatics, computer-based programming, and software, as appropriate.
2.4: Interpret results of data analysis for public health research, policy, or practice.
MSN Public Health Nursing
6.3: Interpret results of data analysis for public health research, policy, or practice.
Multiple regression project
Multiple Regression Project The is the only deliverable in Week Four. It is the case study titled “Locating New Pam an ...
Multiple regression project
Multiple Regression Project The is the only deliverable in Week Four. It is the case study titled “Locating New Pam and Susan’s Stores,” described at the end of Chapter 12 of your textbook. The case involves the decision to locate a new store at one of two candidate sites. The decision will be based on estimates of sales potential, and for this purpose, you will need to develop a multiple regression model to predict sales. Specific case questions are given in the textbook, and the necessary data is in the file named pamsue.xls. Assuming that you are reasonably comfortable with using Excel and its Analysis ToolPak add-in, you should expect to spend approximately 2-3 hours on computer work, and another 3-4 hours on writing the report. It is a good idea not to wait until the last day to do the entire project and write the report. Content of the report consists of your answers to the case questions, plus computer output(s) to support your answers. Please keep the entire report - including computer outputs - under 8 printed pages. Thus, your write up should be concise, and you need to be selective in deciding which computer outputs to include. You can use your discretion in formatting your write up, but use good writing practices and try to make it look professional (more on the report format below). Project Hints and Guidelines It is assumed that you have access to Microsoft Excel with Analysis ToolPak (do NOT use stepwise regression for this project even if it runs on your computer). Data file named pamsue.xls in the DataSets.zip folder. Basic Excel skills you need are the ability to construct histograms and scatterplots, to create dummy variables, copying or moving columns of data in a spreadsheet, and the ability to use the Correlation and Regression facilities under Data Analysis (available when Analysis ToolPak has been added in). Remember that Analysis ToolPak requires contiguous ranges of data for correlation or regression. Open the file pamsue.xls. First, move the column for sales so that it is the rightmost column (it is now to the right of comtype). If the old sales column remains but appears empty, delete that column. Obtain a scatterplot of the sales on the vertical axis against comtype on the horizontal axis. This will give you a good idea of whether different categories of comtype appear to differ in sales. In the scatterplot, you should see that sales in the middle categories 3 - 6 are in similar ranges on the vertical axis, but 1 and 2 have somewhat higher sales, and category 7 appears to have somewhat lower sales. This implies that, when you create dummy variables for comtype, dummy variables for categories 1, 2, 7 are likely to be statistically significant in the multiple regression model (and dummy variables for categories 3 - 6 are likely to be not significant). Although it would be desirable to also obtain the scatterplot of sales against every other X variable, you can omit these if you do not have time, and use the correlation coefficients instead (see step 4 below). Insert seven new columns immediately to the left of comtype, and in these columns, create seven dummy variables to represent the seven categories of site types. Name them comtype1, comtype2, ..., comtype7. At this point, you have 40 columns of data in the spreadsheet with comtype and sales in the last two columns. Use the Correlation facility under Data Analysis to obtain the correlation coefficients between sales and all of the other variables except store and comtype (why exclude comtype?). This will produce a matrix of correlation coefficients between sales and every X variable, as well as between every pair of X variables. To make them easy to read, you may want to format the cells to show numbers with 2 or 3 decimal places. Write down the names of 10 quantitative X variables having the highest correlations with sales. From the correlations worksheet, move to the data worksheet. Select the following columns: sales, plus the 10 quantitative X variables you wrote down, plus comtype1, comptype2, comptype7 (here, you could include up to three more dummy variables, but they are likely to be statistically not significant, so you can save some work - see 2. above). Copy these onto a blank worksheet. Make sure there are no blank columns in within the data range in the new worksheet. Note: To prevent unexpected changes in copying data when formulas are involved, use Paste Special with Values selected when pasting data into a new worksheet. Use Regression under Data Analysis to obtain the regression output table for sales using the variables in the columns you had selected, making sure that Labels and New Worksheet Ply checkboxes are checked, and leave the other boxes unchecked. On the name tab of the output sheet (at the bottom), change the name of the worksheet to Model1.Using appropriate statistics in the regression output table, see if any of the X variables is statistically not significant. If there is at least one insignificant X variable, write down the most insignificant variable, move to the data sheet and delete that column, and re-run Regression without that variable. Repeat until there are no insignificant X variables. Name each output sheet Model2, Model3, and so on for easy identification. When you get to a model in which all remaining X variables are statistically significant, you will have found the final regression equation for predicting sales. Re-run the last model, but this time checking the Residuals checkbox. This will reproduce the last regression table, but below it, you will see columns for Predicted sales and Residuals. Obtain a scatterplot of Residuals against Predicted sales. Also obtain a histogram of Residuals.Use the final regression equation you found in the last step to predict sales at the two sites under consideration. You have just completed all necessary computer work for your project report. Now you have to write a report to present your answers to the case questions (see pages 433-434 of your textbook), and the reasons for those answers. In terms of physical organization, a reasonable format for the report is described below. Content and Format of the Project Report Cover page Include the report title, your name, course, section, facilitator, and date. Go to a new page, and use the following subsection headings for the report. Introduction One paragraph (two at most) describing the subject and context of the project. Data One or two paragraphs describing the data in plain English (number of variables, number of observations, units for data values, etc.) Results and Discussion This is the main body of the report. It is where you will describe what you have done, what you found, and answer the case questions with the reasons for your answers. These reasons should be based on the analytical work you have done using Excel. Depending on how concisely you write and how many tables and graphs you include, this page could be 3-4 pages long. Conclusion One or two paragraphs discussing any remaining issues (e.g. shortcomings and possible improvements of the analyses in the report). In the Results and Discussion section, you should include a few informative tables or graphs derived from your computer analyses. DO NOT include anything that is not absolutely necessary. DO NOT include entire worksheets form Excel, but only the parts you need. For example, do not include the entire correlation matrix found in step 4 above, but you can make a small table to show the 10 variables having the highest correlations with sales. You should include the scatterplot of sales against comtype, relevant portion of the final regression output table, the final regression equation, and the two residual graphs you obtained in step 8. Please keep the total length of the report under 8 printed pages (5 to 6 pages should be sufficient in most cases). Please submit you report as a Word
Please Answer the Following Question
Task 1 A real estate company sells plots of land. The plot shown at the right costs $84,120. What is the price per square ...
Please Answer the Following Question
Task 1 A real estate company sells plots of land. The plot shown at the right costs $84,120. What is the price per square foot of the land? Explain how you found your answer.Task 2: An arc length is a fractional part of the circumference of a circle. The area of a sector is a fractional part of the area of a circle. The stained glass circle-head window has a 2-in. wide frame. The grills divide the semicircular glass pane into four congruent regions. a. What is the area of the blue region? b. What is the outside perimeter of the window frame? . Task 3: The ratio of the perimeters and the ratio of the areas of two similar figures are related to the ratio of the corresponding measures Regular hexagon ABCDEF has vertices at A(4, 4!3), B(8, 4!3), C(10, 2!3), D(8, 0), E(4, 0) and F(2, 2!3). Suppose the sides of the hexagon are reduced by 40% to produce a similar regular hexagon. What are the perimeter and area of the smaller hexagon rounded to the nearest tenth? Explain how you found your answer.I have attached a copy of the actual assignment that contains pictures and the project rubric that shows everything that must be included to receive a passing grade.
Walden University Simulation for Performance Improvement Discussion
Discussion: Simulation for Performance Improvement
As you have examined this week, simulation as an analytic tool can assi ...
Walden University Simulation for Performance Improvement Discussion
Discussion: Simulation for Performance Improvement
As you have examined this week, simulation as an analytic tool can assist healthcare administration leaders execute important improvement initiatives. Simulations can be used to determine the impact of hospital outbreaks, shortages in staff, potential disaster events, or even financial challenges that might impact healthcare delivery for a health services organization. As a current or future healthcare administration leader, the ability to use simulation as an analytic technique will help you execute sound decision making to tackle healthcare administration challenges.
For this Discussion, review the resources for this week, and consider those issues that might most affect healthcare administration practice. Consider how those issues might be addressed through the process of using simulation as an analytic technique, and reflect on how you might apply the process of simulation to address these issues.
By Day 3
Post an explanation of how simulation might be used to improve performance in your health services organization or one with which you are familiar. Be specific, and provide examples.
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Most Popular Content
GCU Food Choices in Areas with Consistent Behavioral Differences Analysis
In public health, data are often gathered from separate groups in order to describe health-related behavior for a topic of ...
GCU Food Choices in Areas with Consistent Behavioral Differences Analysis
In public health, data are often gathered from separate groups in order to describe health-related behavior for a topic of interest. In practice, a larger sample is often surveyed, and then focus groups or interviews can be conducted with a smaller subset of the sample. The quantitative survey data from a larger group helps to identify health-related trends and patterns within the sample group. The qualitative data collected with the smaller group complements the quantitative survey data and helps to determine why and how a phenomenon exists. The cumulative findings can then help public health professionals form a conclusion about the health issue and inform future public health research, policy, and practice.
The purpose of this assignment is to analyze qualitative and quantitative data from two separate groups, propose a research question, and then disseminate your findings in a mixed-methods manuscript. The survey and focus group are both on the topic of nutrition. The quantitative data is adapted from the Youth Risk Behavior Surveillance System (YRBSS), and the qualitative data is from a focus group transcript in which 9th grade girls discussed healthy eating.
For this assignment, you will use:
IBM SPSS Statistics and the "Youth Risk Behavior Surveillance System Dataset" to conduct a basic quantitative statistical analysis.
The "9th Grade Girls Healthy Food Focus Group Transcription" to conduct a qualitative analysis and identify key themes.
Part 1: Analyzing Data
Using the resources indicated above, conduct the following analyses and record the results on the "Results and Outputs" template. Attach this document as an Appendix in your paper.
Qualitative Analysis: Read the "9th Grade Girls Healthy Food Focus Group Transcription." This transcript is from a focus group of 9th grade girls discussing healthy eating with the moderator. Generate codes and summarize the qualitative data.
Quantitative Analysis:
Review the "Youth Risk Behavior Surveillance System Dataset." Identify two or three variables of interest and identify a study topic for your paper. Conduct a literature search for three to five peer-reviewed articles from the last 5 years that have studies supporting your topic. Develop a research question for your study based on the selected variables. The research question should demonstrate support for the focus group results and should be supported by existing literature.
Prepare the data to complete the analysis based on your research question.
Using SPSS, conduct descriptive statistics to summarize the sample.
Using SPSS, select an appropriate quantitative inferential statistical test to analyze the data.
Part 2: Reporting Data
Prepare a 1,250-1,500 word manuscript to disseminate the findings of your proposed study. The study should be based on your proposed research question and supported by the findings from the qualitative and quantitative analyses above, as well as by current literature. Include the following:
Abstract
A 150-250 word summary of the manuscript.
Introduction
Summarize the purpose of the research, the problem being addressed, and your proposed research question.
Support your summary using three to five peer-reviewed articles from the last 5 years that are relevant to your topic and that support why the study is being conducted.
Methods
Qualitative Methods (Focus Group)
Describe the focus group sample and data collection process.
Describe the process used to analyze the qualitative data.
Quantitative Methods (YRBSS Survey)
Discuss how the data were collected.
Identify and describe the variables used in the analysis.
Describe the descriptive and inferential statistical tests that were conducted.
Identify the software that was used to conduct the analysis.
Results
Qualitative Results (Focus Group)
Summarize the results of the qualitative analysis from the "9th Grade Girls Healthy Food Focus Group Transcription."
Quantitative Results (YRBSS Survey)
Summarize the sample using the results of the descriptive statistics.
Describe the main outcomes of the inferential statistical analysis.
Include at least one table or figure to support the results.
Discussion
Interpret the study results in relation to the related literature and study purpose.
Discuss the study limitations.
Based on the interpreted results of the study, provide recommendations for future public health research, policy, or practice.
Appendix
"Results and Outputs" analysis document.
General Requirements
You are required to cite to a minimum of three sources to complete this assignment. Sources must be published within the last 5 years and appropriate for the assignment criteria and public health content.
Prepare this assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
You are required to submit this assignment to LopesWrite. A link to the LopesWrite technical support articles is located in Class Resources if you need assistance.
Benchmark Information
This benchmark assignment assesses the following programmatic competencies:
Master Public Health
2.2: Select quantitative and qualitative data collection methods appropriate for a given public health context.
2.3: Analyze quantitative and qualitative data using biostatistics, informatics, computer-based programming, and software, as appropriate.
2.4: Interpret results of data analysis for public health research, policy, or practice.
MSN Public Health Nursing
6.3: Interpret results of data analysis for public health research, policy, or practice.
Multiple regression project
Multiple Regression Project The is the only deliverable in Week Four. It is the case study titled “Locating New Pam an ...
Multiple regression project
Multiple Regression Project The is the only deliverable in Week Four. It is the case study titled “Locating New Pam and Susan’s Stores,” described at the end of Chapter 12 of your textbook. The case involves the decision to locate a new store at one of two candidate sites. The decision will be based on estimates of sales potential, and for this purpose, you will need to develop a multiple regression model to predict sales. Specific case questions are given in the textbook, and the necessary data is in the file named pamsue.xls. Assuming that you are reasonably comfortable with using Excel and its Analysis ToolPak add-in, you should expect to spend approximately 2-3 hours on computer work, and another 3-4 hours on writing the report. It is a good idea not to wait until the last day to do the entire project and write the report. Content of the report consists of your answers to the case questions, plus computer output(s) to support your answers. Please keep the entire report - including computer outputs - under 8 printed pages. Thus, your write up should be concise, and you need to be selective in deciding which computer outputs to include. You can use your discretion in formatting your write up, but use good writing practices and try to make it look professional (more on the report format below). Project Hints and Guidelines It is assumed that you have access to Microsoft Excel with Analysis ToolPak (do NOT use stepwise regression for this project even if it runs on your computer). Data file named pamsue.xls in the DataSets.zip folder. Basic Excel skills you need are the ability to construct histograms and scatterplots, to create dummy variables, copying or moving columns of data in a spreadsheet, and the ability to use the Correlation and Regression facilities under Data Analysis (available when Analysis ToolPak has been added in). Remember that Analysis ToolPak requires contiguous ranges of data for correlation or regression. Open the file pamsue.xls. First, move the column for sales so that it is the rightmost column (it is now to the right of comtype). If the old sales column remains but appears empty, delete that column. Obtain a scatterplot of the sales on the vertical axis against comtype on the horizontal axis. This will give you a good idea of whether different categories of comtype appear to differ in sales. In the scatterplot, you should see that sales in the middle categories 3 - 6 are in similar ranges on the vertical axis, but 1 and 2 have somewhat higher sales, and category 7 appears to have somewhat lower sales. This implies that, when you create dummy variables for comtype, dummy variables for categories 1, 2, 7 are likely to be statistically significant in the multiple regression model (and dummy variables for categories 3 - 6 are likely to be not significant). Although it would be desirable to also obtain the scatterplot of sales against every other X variable, you can omit these if you do not have time, and use the correlation coefficients instead (see step 4 below). Insert seven new columns immediately to the left of comtype, and in these columns, create seven dummy variables to represent the seven categories of site types. Name them comtype1, comtype2, ..., comtype7. At this point, you have 40 columns of data in the spreadsheet with comtype and sales in the last two columns. Use the Correlation facility under Data Analysis to obtain the correlation coefficients between sales and all of the other variables except store and comtype (why exclude comtype?). This will produce a matrix of correlation coefficients between sales and every X variable, as well as between every pair of X variables. To make them easy to read, you may want to format the cells to show numbers with 2 or 3 decimal places. Write down the names of 10 quantitative X variables having the highest correlations with sales. From the correlations worksheet, move to the data worksheet. Select the following columns: sales, plus the 10 quantitative X variables you wrote down, plus comtype1, comptype2, comptype7 (here, you could include up to three more dummy variables, but they are likely to be statistically not significant, so you can save some work - see 2. above). Copy these onto a blank worksheet. Make sure there are no blank columns in within the data range in the new worksheet. Note: To prevent unexpected changes in copying data when formulas are involved, use Paste Special with Values selected when pasting data into a new worksheet. Use Regression under Data Analysis to obtain the regression output table for sales using the variables in the columns you had selected, making sure that Labels and New Worksheet Ply checkboxes are checked, and leave the other boxes unchecked. On the name tab of the output sheet (at the bottom), change the name of the worksheet to Model1.Using appropriate statistics in the regression output table, see if any of the X variables is statistically not significant. If there is at least one insignificant X variable, write down the most insignificant variable, move to the data sheet and delete that column, and re-run Regression without that variable. Repeat until there are no insignificant X variables. Name each output sheet Model2, Model3, and so on for easy identification. When you get to a model in which all remaining X variables are statistically significant, you will have found the final regression equation for predicting sales. Re-run the last model, but this time checking the Residuals checkbox. This will reproduce the last regression table, but below it, you will see columns for Predicted sales and Residuals. Obtain a scatterplot of Residuals against Predicted sales. Also obtain a histogram of Residuals.Use the final regression equation you found in the last step to predict sales at the two sites under consideration. You have just completed all necessary computer work for your project report. Now you have to write a report to present your answers to the case questions (see pages 433-434 of your textbook), and the reasons for those answers. In terms of physical organization, a reasonable format for the report is described below. Content and Format of the Project Report Cover page Include the report title, your name, course, section, facilitator, and date. Go to a new page, and use the following subsection headings for the report. Introduction One paragraph (two at most) describing the subject and context of the project. Data One or two paragraphs describing the data in plain English (number of variables, number of observations, units for data values, etc.) Results and Discussion This is the main body of the report. It is where you will describe what you have done, what you found, and answer the case questions with the reasons for your answers. These reasons should be based on the analytical work you have done using Excel. Depending on how concisely you write and how many tables and graphs you include, this page could be 3-4 pages long. Conclusion One or two paragraphs discussing any remaining issues (e.g. shortcomings and possible improvements of the analyses in the report). In the Results and Discussion section, you should include a few informative tables or graphs derived from your computer analyses. DO NOT include anything that is not absolutely necessary. DO NOT include entire worksheets form Excel, but only the parts you need. For example, do not include the entire correlation matrix found in step 4 above, but you can make a small table to show the 10 variables having the highest correlations with sales. You should include the scatterplot of sales against comtype, relevant portion of the final regression output table, the final regression equation, and the two residual graphs you obtained in step 8. Please keep the total length of the report under 8 printed pages (5 to 6 pages should be sufficient in most cases). Please submit you report as a Word
Please Answer the Following Question
Task 1 A real estate company sells plots of land. The plot shown at the right costs $84,120. What is the price per square ...
Please Answer the Following Question
Task 1 A real estate company sells plots of land. The plot shown at the right costs $84,120. What is the price per square foot of the land? Explain how you found your answer.Task 2: An arc length is a fractional part of the circumference of a circle. The area of a sector is a fractional part of the area of a circle. The stained glass circle-head window has a 2-in. wide frame. The grills divide the semicircular glass pane into four congruent regions. a. What is the area of the blue region? b. What is the outside perimeter of the window frame? . Task 3: The ratio of the perimeters and the ratio of the areas of two similar figures are related to the ratio of the corresponding measures Regular hexagon ABCDEF has vertices at A(4, 4!3), B(8, 4!3), C(10, 2!3), D(8, 0), E(4, 0) and F(2, 2!3). Suppose the sides of the hexagon are reduced by 40% to produce a similar regular hexagon. What are the perimeter and area of the smaller hexagon rounded to the nearest tenth? Explain how you found your answer.I have attached a copy of the actual assignment that contains pictures and the project rubric that shows everything that must be included to receive a passing grade.
Walden University Simulation for Performance Improvement Discussion
Discussion: Simulation for Performance Improvement
As you have examined this week, simulation as an analytic tool can assi ...
Walden University Simulation for Performance Improvement Discussion
Discussion: Simulation for Performance Improvement
As you have examined this week, simulation as an analytic tool can assist healthcare administration leaders execute important improvement initiatives. Simulations can be used to determine the impact of hospital outbreaks, shortages in staff, potential disaster events, or even financial challenges that might impact healthcare delivery for a health services organization. As a current or future healthcare administration leader, the ability to use simulation as an analytic technique will help you execute sound decision making to tackle healthcare administration challenges.
For this Discussion, review the resources for this week, and consider those issues that might most affect healthcare administration practice. Consider how those issues might be addressed through the process of using simulation as an analytic technique, and reflect on how you might apply the process of simulation to address these issues.
By Day 3
Post an explanation of how simulation might be used to improve performance in your health services organization or one with which you are familiar. Be specific, and provide examples.
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