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4-3 Project One Submission
CompetenciesIn this project, you will demonstrate your mastery of the following competencies:Apply statistical techniques ...
4-3 Project One Submission
CompetenciesIn this project, you will demonstrate your mastery of the following competencies:Apply statistical techniques to address research problemsPerform regression analysis to address an authentic problemOverviewThe purpose of this project is to have you complete all of the steps of a real-world linear regression research project starting with developing a research question, then completing a comprehensive statistical analysis, and ending with summarizing your research conclusions.ScenarioYou have been hired by the D. M. Pan National Real Estate Company to develop a model to predict median housing prices for homes sold in 2019. The CEO of D. M. Pan wants to use this information to help their real estate agents better determine the use of square footage as a benchmark for listing prices on homes. Your task is to provide a report predicting the median housing prices based square footage. To complete this task, use the provided real estate data set for all U.S. home sales as well as national descriptive statistics and graphs provided.DirectionsUsing the Project One Template located in the What to Submit section, generate a report including your tables and graphs to determine if the square footage of a house is a good indicator for what the listing price should be. Reference the National Statistics and Graphs document for national comparisons and the Real Estate County Data spreadsheet (both found in the Supporting Materials section) for your statistical analysis.Note: Present your data in a clearly labeled table and using clearly labeled graphs.Specifically, include the following in your report:IntroductionDescribe the report: Give a brief description of the purpose of your report.Define the question your report is trying to answer.Explain when using linear regression is most appropriate.When using linear regression, what would you expect the scatterplot to look like?Explain the difference between response and predictor variables in a linear regression to justify the selection of variables.Data CollectionSampling the data: Select a random sample of 50 counties.Identify your response and predictor variables.Scatterplot: Create a scatterplot of your response and predictor variables to ensure they are appropriate for developing a linear model.Data AnalysisHistogram: For your two variables, create histograms.Summary statistics: For your two variables, create a table to show the mean, median, and standard deviation.Interpret the graphs and statistics:Based on your graphs and sample statistics, interpret the center, spread, shape, and any unusual characteristic (outliers, gaps, etc.) for the two variables.Compare and contrast the shape, center, spread, and any unusual characteristic for your sample of house sales with the national population. Is your sample representative of national housing market sales?Develop Your Regression ModelScatterplot: Provide a graph of the scatterplot of the data with a line of best fit.Explain if a regression model is appropriate to develop based on your scatterplot.Discuss associations: Based on the scatterplot, discuss the association (direction, strength, form) in the context of your model.Identify any possible outliers or influential points and discuss their effect on the correlation.Discuss keeping or removing outlier data points and what impact your decision would have on your model.Find r: Find the correlation coefficient (r).Explain how the r value you calculated supports what you noticed in your scatterplot.Determine the Line of Best Fit. Clearly define your variables. Find and interpret the regression equation. Assess the strength of the model.Regression equation: Write the regression equation (i.e., line of best fit) and clearly define your variables.Interpret regression equation: Interpret the slope and intercept in context.Strength of the equation: Provide and interpret R-squared.Determine the strength of the linear regression equation you developed.Use regression equation to make predictions: Use your regression equation to predict how much you should list your home for based on the square footage of your home.ConclusionsSummarize findings: In one paragraph, summarize your findings in clear and concise plain language for the CEO to understand. Summarize your results.Did you see the results you expected, or was anything different from your expectations or experiences?What changes could support different results, or help to solve a different problem?Provide at least one question that would be interesting for follow-up research.
8 pages
Chp 8 Problems
Big Sky Hospital plans to obtain a new MRI that costs $1.5 million and has an estimated four-year useful life. It can obta ...
Chp 8 Problems
Big Sky Hospital plans to obtain a new MRI that costs $1.5 million and has an estimated four-year useful life. It can obtain a bank loan for the ...
MAT 240 SNHU Prediction Model for the Median Housing Price Report
CompetenciesIn this project, you will demonstrate your mastery of the following competencies:Apply statistical techniques ...
MAT 240 SNHU Prediction Model for the Median Housing Price Report
CompetenciesIn this project, you will demonstrate your mastery of the following competencies:Apply statistical techniques to address research problemsPerform regression analysis to address an authentic problemOverviewThe purpose of this project is to have you complete all of the steps of a real-world linear regression research project starting with developing a research question, then completing a comprehensive statistical analysis, and ending with summarizing your research conclusions.ScenarioYou have been hired by the D. M. Pan National Real Estate Company to develop a model to predict median housing prices for homes sold in 2019. The CEO of D. M. Pan wants to use this information to help their real estate agents better determine the use of square footage as a benchmark for listing prices on homes. Your task is to provide a report predicting the median housing prices based square footage. To complete this task, use the provided real estate data set for all U.S. home sales as well as national descriptive statistics and graphs provided.DirectionsUsing the Project One Template located in the What to Submit section, generate a report including your tables and graphs to determine if the square footage of a house is a good indicator for what the listing price should be. Reference the National Statistics and Graphs document for national comparisons and the Real Estate County Data spreadsheet (both found in the Supporting Materials section) for your statistical analysis.Note: Present your data in a clearly labeled table and using clearly labeled graphs.Specifically, include the following in your report:IntroductionDescribe the report: Give a brief description of the purpose of your report.Define the question your report is trying to answer.Explain when using linear regression is most appropriate.When using linear regression, what would you expect the scatterplot to look like?Explain the difference between response and predictor variables in a linear regression to justify the selection of variables.Data CollectionSampling the data: Select a random sample of 50 counties.Identify your response and predictor variables.Scatterplot: Create a scatterplot of your response and predictor variables to ensure they are appropriate for developing a linear model.Data AnalysisHistogram: For your two variables, create histograms.Summary statistics: For your two variables, create a table to show the mean, median, and standard deviation.Interpret the graphs and statistics:Based on your graphs and sample statistics, interpret the center, spread, shape, and any unusual characteristic (outliers, gaps, etc.) for the two variables.Compare and contrast the shape, center, spread, and any unusual characteristic for your sample of house sales with the national population. Is your sample representative of national housing market sales?Develop Your Regression ModelScatterplot: Provide a graph of the scatterplot of the data with a line of best fit.Explain if a regression model is appropriate to develop based on your scatterplot.Discuss associations: Based on the scatterplot, discuss the association (direction, strength, form) in the context of your model.Identify any possible outliers or influential points and discuss their effect on the correlation.Discuss keeping or removing outlier data points and what impact your decision would have on your model.Find r: Find the correlation coefficient (r).Explain how the r value you calculated supports what you noticed in your scatterplot.Determine the Line of Best Fit. Clearly define your variables. Find and interpret the regression equation. Assess the strength of the model.Regression equation: Write the regression equation (i.e., line of best fit) and clearly define your variables.Interpret regression equation: Interpret the slope and intercept in context.Strength of the equation: Provide and interpret R-squared.Determine the strength of the linear regression equation you developed.Use regression equation to make predictions: Use your regression equation to predict how much you should list your home for based on the square footage of your home.ConclusionsSummarize findings: In one paragraph, summarize your findings in clear and concise plain language for the CEO to understand. Summarize your results.Did you see the results you expected, or was anything different from your expectations or experiences?What changes could support different results, or help to solve a different problem?Provide at least one question that would be interesting for follow-up research.
Florida National University Quantifying the Extent of Disease
For this assignment, you will create a research (exploration and examination) project for a specific disease that you will ...
Florida National University Quantifying the Extent of Disease
For this assignment, you will create a research (exploration and examination) project for a specific disease that you will be able to choose. The project and research is designed to apply the knowledge and skills learned in the Biostatistics to a specific public health issue or diseases. The final project illustrates the student's understanding of biostatistics methods, principles, and processes, as well as her/his ability to actively apply this knowledge and demonstrate acquisition of the necessary skills. You are encouraged to choose any disease to design this project and develop satisfactorily. The paper will be 7-9 pages long. More information and due date will provide in the assignments link. APA style 7th edition format. ASSIGNMENT GUIDELINES (2 points /10%): The purpose of the final project is to provide the student with a culminating capstone experience where she/he applies the knowledge and skills learned in the MHSA program to a specific public health issue or problem. The final project illustrates the student's understanding of biostatistics methods, principles, and processes, as well as her/his ability to actively apply this knowledge and demonstrate acquisition of the necessary skills. The final project must also adequately demonstrate MHSA degree competencies. EACH PAPER SHOULD INCLUDE THE FOLLOWING: Introduction (5 points / 25%) Offer an abstract that provide a brief outlook of the proposal and explaining in your own words what is meant the disease of your choosing and the most important information about it. 2. Your Biostatistics Project: Quantifying the Extent of Disease Plan (10 points 50%) a. Presentation Page: PROJECT NAME DISEASE NAME STUDENT NAME AFILIATION b. Introduction Purpose of the project • Significance of the project Review of relevant literature • Relevance to biostatistics c. Methods • Description of the project and project deliverables. •A statement on the relationship of the project to the class experience • Methods to be used in completing the project • Project timeline *Description of Sample • Description of variables and how they were measured d. Results • Findings from the analysis (includes tables, figures, etc.) e. Discussion • Comparison of results with other studies • Strengths and limitations of the study 3. Conclusion (3 points / 15%) Briefly recapitulate your thoughts & conclusion to Your Biostatistics Project: Quantifying the Extent of Disease. How did this research impact your thoughts on Health Care Administrator and statistics? Evaluation will be based on how clearly you respond to the above, in particular: a) The clarity with which you associate, relates, stablish and apply your knowledge to generate the Biostatistics Project: Quantifying the Extent of Disease. b) The Complexity, depth, scope, Profundity and organization of your paper; and, c) Your conclusions, including a description of the impact of the Your Biostatistics Project on any Health Care Setting.
Statistics Question
Take a look at the website below:Hospital Comparison (Links to an external site.)https://www.medicare.gov/care-compare/?pr ...
Statistics Question
Take a look at the website below:Hospital Comparison (Links to an external site.)https://www.medicare.gov/care-compare/?providerTyp... website allows people to compare the results seen at different hospitals across things such as patient experience and timely and effective care. Visit the website and compare the hospitals in your local area.( Orange county, California) What statistics stand out to you? Do you believe that these statistics should/could play a role in patient care?
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4-3 Project One Submission
CompetenciesIn this project, you will demonstrate your mastery of the following competencies:Apply statistical techniques ...
4-3 Project One Submission
CompetenciesIn this project, you will demonstrate your mastery of the following competencies:Apply statistical techniques to address research problemsPerform regression analysis to address an authentic problemOverviewThe purpose of this project is to have you complete all of the steps of a real-world linear regression research project starting with developing a research question, then completing a comprehensive statistical analysis, and ending with summarizing your research conclusions.ScenarioYou have been hired by the D. M. Pan National Real Estate Company to develop a model to predict median housing prices for homes sold in 2019. The CEO of D. M. Pan wants to use this information to help their real estate agents better determine the use of square footage as a benchmark for listing prices on homes. Your task is to provide a report predicting the median housing prices based square footage. To complete this task, use the provided real estate data set for all U.S. home sales as well as national descriptive statistics and graphs provided.DirectionsUsing the Project One Template located in the What to Submit section, generate a report including your tables and graphs to determine if the square footage of a house is a good indicator for what the listing price should be. Reference the National Statistics and Graphs document for national comparisons and the Real Estate County Data spreadsheet (both found in the Supporting Materials section) for your statistical analysis.Note: Present your data in a clearly labeled table and using clearly labeled graphs.Specifically, include the following in your report:IntroductionDescribe the report: Give a brief description of the purpose of your report.Define the question your report is trying to answer.Explain when using linear regression is most appropriate.When using linear regression, what would you expect the scatterplot to look like?Explain the difference between response and predictor variables in a linear regression to justify the selection of variables.Data CollectionSampling the data: Select a random sample of 50 counties.Identify your response and predictor variables.Scatterplot: Create a scatterplot of your response and predictor variables to ensure they are appropriate for developing a linear model.Data AnalysisHistogram: For your two variables, create histograms.Summary statistics: For your two variables, create a table to show the mean, median, and standard deviation.Interpret the graphs and statistics:Based on your graphs and sample statistics, interpret the center, spread, shape, and any unusual characteristic (outliers, gaps, etc.) for the two variables.Compare and contrast the shape, center, spread, and any unusual characteristic for your sample of house sales with the national population. Is your sample representative of national housing market sales?Develop Your Regression ModelScatterplot: Provide a graph of the scatterplot of the data with a line of best fit.Explain if a regression model is appropriate to develop based on your scatterplot.Discuss associations: Based on the scatterplot, discuss the association (direction, strength, form) in the context of your model.Identify any possible outliers or influential points and discuss their effect on the correlation.Discuss keeping or removing outlier data points and what impact your decision would have on your model.Find r: Find the correlation coefficient (r).Explain how the r value you calculated supports what you noticed in your scatterplot.Determine the Line of Best Fit. Clearly define your variables. Find and interpret the regression equation. Assess the strength of the model.Regression equation: Write the regression equation (i.e., line of best fit) and clearly define your variables.Interpret regression equation: Interpret the slope and intercept in context.Strength of the equation: Provide and interpret R-squared.Determine the strength of the linear regression equation you developed.Use regression equation to make predictions: Use your regression equation to predict how much you should list your home for based on the square footage of your home.ConclusionsSummarize findings: In one paragraph, summarize your findings in clear and concise plain language for the CEO to understand. Summarize your results.Did you see the results you expected, or was anything different from your expectations or experiences?What changes could support different results, or help to solve a different problem?Provide at least one question that would be interesting for follow-up research.
8 pages
Chp 8 Problems
Big Sky Hospital plans to obtain a new MRI that costs $1.5 million and has an estimated four-year useful life. It can obta ...
Chp 8 Problems
Big Sky Hospital plans to obtain a new MRI that costs $1.5 million and has an estimated four-year useful life. It can obtain a bank loan for the ...
MAT 240 SNHU Prediction Model for the Median Housing Price Report
CompetenciesIn this project, you will demonstrate your mastery of the following competencies:Apply statistical techniques ...
MAT 240 SNHU Prediction Model for the Median Housing Price Report
CompetenciesIn this project, you will demonstrate your mastery of the following competencies:Apply statistical techniques to address research problemsPerform regression analysis to address an authentic problemOverviewThe purpose of this project is to have you complete all of the steps of a real-world linear regression research project starting with developing a research question, then completing a comprehensive statistical analysis, and ending with summarizing your research conclusions.ScenarioYou have been hired by the D. M. Pan National Real Estate Company to develop a model to predict median housing prices for homes sold in 2019. The CEO of D. M. Pan wants to use this information to help their real estate agents better determine the use of square footage as a benchmark for listing prices on homes. Your task is to provide a report predicting the median housing prices based square footage. To complete this task, use the provided real estate data set for all U.S. home sales as well as national descriptive statistics and graphs provided.DirectionsUsing the Project One Template located in the What to Submit section, generate a report including your tables and graphs to determine if the square footage of a house is a good indicator for what the listing price should be. Reference the National Statistics and Graphs document for national comparisons and the Real Estate County Data spreadsheet (both found in the Supporting Materials section) for your statistical analysis.Note: Present your data in a clearly labeled table and using clearly labeled graphs.Specifically, include the following in your report:IntroductionDescribe the report: Give a brief description of the purpose of your report.Define the question your report is trying to answer.Explain when using linear regression is most appropriate.When using linear regression, what would you expect the scatterplot to look like?Explain the difference between response and predictor variables in a linear regression to justify the selection of variables.Data CollectionSampling the data: Select a random sample of 50 counties.Identify your response and predictor variables.Scatterplot: Create a scatterplot of your response and predictor variables to ensure they are appropriate for developing a linear model.Data AnalysisHistogram: For your two variables, create histograms.Summary statistics: For your two variables, create a table to show the mean, median, and standard deviation.Interpret the graphs and statistics:Based on your graphs and sample statistics, interpret the center, spread, shape, and any unusual characteristic (outliers, gaps, etc.) for the two variables.Compare and contrast the shape, center, spread, and any unusual characteristic for your sample of house sales with the national population. Is your sample representative of national housing market sales?Develop Your Regression ModelScatterplot: Provide a graph of the scatterplot of the data with a line of best fit.Explain if a regression model is appropriate to develop based on your scatterplot.Discuss associations: Based on the scatterplot, discuss the association (direction, strength, form) in the context of your model.Identify any possible outliers or influential points and discuss their effect on the correlation.Discuss keeping or removing outlier data points and what impact your decision would have on your model.Find r: Find the correlation coefficient (r).Explain how the r value you calculated supports what you noticed in your scatterplot.Determine the Line of Best Fit. Clearly define your variables. Find and interpret the regression equation. Assess the strength of the model.Regression equation: Write the regression equation (i.e., line of best fit) and clearly define your variables.Interpret regression equation: Interpret the slope and intercept in context.Strength of the equation: Provide and interpret R-squared.Determine the strength of the linear regression equation you developed.Use regression equation to make predictions: Use your regression equation to predict how much you should list your home for based on the square footage of your home.ConclusionsSummarize findings: In one paragraph, summarize your findings in clear and concise plain language for the CEO to understand. Summarize your results.Did you see the results you expected, or was anything different from your expectations or experiences?What changes could support different results, or help to solve a different problem?Provide at least one question that would be interesting for follow-up research.
Florida National University Quantifying the Extent of Disease
For this assignment, you will create a research (exploration and examination) project for a specific disease that you will ...
Florida National University Quantifying the Extent of Disease
For this assignment, you will create a research (exploration and examination) project for a specific disease that you will be able to choose. The project and research is designed to apply the knowledge and skills learned in the Biostatistics to a specific public health issue or diseases. The final project illustrates the student's understanding of biostatistics methods, principles, and processes, as well as her/his ability to actively apply this knowledge and demonstrate acquisition of the necessary skills. You are encouraged to choose any disease to design this project and develop satisfactorily. The paper will be 7-9 pages long. More information and due date will provide in the assignments link. APA style 7th edition format. ASSIGNMENT GUIDELINES (2 points /10%): The purpose of the final project is to provide the student with a culminating capstone experience where she/he applies the knowledge and skills learned in the MHSA program to a specific public health issue or problem. The final project illustrates the student's understanding of biostatistics methods, principles, and processes, as well as her/his ability to actively apply this knowledge and demonstrate acquisition of the necessary skills. The final project must also adequately demonstrate MHSA degree competencies. EACH PAPER SHOULD INCLUDE THE FOLLOWING: Introduction (5 points / 25%) Offer an abstract that provide a brief outlook of the proposal and explaining in your own words what is meant the disease of your choosing and the most important information about it. 2. Your Biostatistics Project: Quantifying the Extent of Disease Plan (10 points 50%) a. Presentation Page: PROJECT NAME DISEASE NAME STUDENT NAME AFILIATION b. Introduction Purpose of the project • Significance of the project Review of relevant literature • Relevance to biostatistics c. Methods • Description of the project and project deliverables. •A statement on the relationship of the project to the class experience • Methods to be used in completing the project • Project timeline *Description of Sample • Description of variables and how they were measured d. Results • Findings from the analysis (includes tables, figures, etc.) e. Discussion • Comparison of results with other studies • Strengths and limitations of the study 3. Conclusion (3 points / 15%) Briefly recapitulate your thoughts & conclusion to Your Biostatistics Project: Quantifying the Extent of Disease. How did this research impact your thoughts on Health Care Administrator and statistics? Evaluation will be based on how clearly you respond to the above, in particular: a) The clarity with which you associate, relates, stablish and apply your knowledge to generate the Biostatistics Project: Quantifying the Extent of Disease. b) The Complexity, depth, scope, Profundity and organization of your paper; and, c) Your conclusions, including a description of the impact of the Your Biostatistics Project on any Health Care Setting.
Statistics Question
Take a look at the website below:Hospital Comparison (Links to an external site.)https://www.medicare.gov/care-compare/?pr ...
Statistics Question
Take a look at the website below:Hospital Comparison (Links to an external site.)https://www.medicare.gov/care-compare/?providerTyp... website allows people to compare the results seen at different hospitals across things such as patient experience and timely and effective care. Visit the website and compare the hospitals in your local area.( Orange county, California) What statistics stand out to you? Do you believe that these statistics should/could play a role in patient care?
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