Analysis of Variance A study compared three display panels used by air traffic c

Analysis of Variance
A study compared three display panels used by air traffic controllers. Each display panel was tested for four different simulated emergency conditions. Twenty-four highly trained air traffic controllers were used in the study. Two controllers were randomly assigned to each display panel–emergency condition combination. The time (in seconds) required to stabilize the emergency condition was recorded. The resulting data and analysis results are given below.
Display panel data Emergency conditionDisplay panel1234A17253114 14243413B1522289 12193110C21293215 24283719
Two-way ANOVA resultsSource of VariationDFSSMSFPPanel2218.58109.2926.490.000Condition31247.4615.82100.800.000Interaction616.422.740.660.681Error1249.504.13  Total231531.96   
Tasks:
Set up null and alternative hypotheses to (a) test the significance of display panel effect, (b) test the significance of emergency condition effects, and (c) test the interaction a between the two factors.
Justify the type of ANOVA (one-way or two-way) you will apply to test the hypotheses.
Interpret the p values in the results table for an accept/reject decision regarding the hypotheses.
State your conclusions including what the ANOVA results do not tell you and your needed actions as a researcher.
Submission Details:
Submit a Microsoft Word document that contains your responses to assignment questions, using APA style.
Name your document SU_BUS7200_W3_ LastName_FirstInitial.doc

Providing background and descriptive statistics is like a literature review sect

Providing background and descriptive statistics is like a literature review section of a dissertation. You review and communicate the analysis on the raw data. You present visual representations of the data to give meaning to the raw data. 
An optional supplementary textbook is OpenIntro Statistics, and you can read the concepts there.
Use the lab file WS5Practice to practice the Excel skills (includes an Excel hands-on video inside the spreadsheet as a link).
Use the file WS5Homework to demonstrate the Excel skills. Each of the three problems is worth 20 points, for a total of 60 points possible for this assignment.
When you have completed your assignment, save a copy for yourself and submit a copy to your instructor by the end of the workshop.

Question 1 A national survey was initiated and intended to capture the prevalenc

Question 1
A national survey was initiated and intended to capture the prevalence of HIV in the post-HAART (Highly active anti-retroviral therapy) era of treatment. Out of a sample of 1,483 participants, a total of 241 were found to be HIV positive.
A) Calculate a SE.
B) Construct a 95% confidence interval about the estimate.
Question 2
Suppose a national survey intends to identify the prevalence of Hepatitis C in a population of intravenous drug users. Out of a sample of 6,458 a total of 754 were confirmed to have the disease. 
A) Calcuate a SE using the plus-four method.
B) Construct a 99% confidence interval about the estimate.
Question 3
A national organization sets out to investigate the change in prevalence of HIV since the last census in 2010. A total of 4,706 participants were interviewed and a total of 468 responses were confirmed to be HIV positive. Assume that the data from the census indicated that the prevalence of HIV in the particular population was 7.5%.
A) Write out the null and alternative hypotheses for a formal test of significance.
B) Interpret your results at 95% confidence.
Question 4
The national blood bank sponsored by the US estimates that approximately 38% of the population has blood type O+. Suppose a local blood bank samples 14 participants, and determines that 8 participants are O+.
A) Can we approximate by the normal distribution in this instance?
B) Depending on your decision in part A), carry out a test to determine if the proportion of responses based on the local blood bank differ significantly from those at the national blood bank.
Question 5
A researcher is interested in conducting an observational study looking at the incidence of breast cancer in women 40-65 years of age followed for a period of 15 years. The researcher is interested in showing that the incidence of breast cancer has risen from 24% to 35% since the last surveillance data was published covering this population. If the researcher wants to be 95% confident with 90% power, how many participants should be enrolled? Blank 1
DUE TONIGHT BY 11:00 PM EST TIME

Analysis of Variance A study compared three display panels used by air traffic c

Analysis of Variance
A study compared three display panels used by air traffic controllers. Each display panel was tested for four different simulated emergency conditions. Twenty-four highly trained air traffic controllers were used in the study. Two controllers were randomly assigned to each display panel–emergency condition combination. The time (in seconds) required to stabilize the emergency condition was recorded. The resulting data and analysis results are given below.
Display panel data Emergency conditionDisplay panel1234A17253114 14243413B1522289 12193110C21293215 24283719
Two-way ANOVA resultsSource of VariationDFSSMSFPPanel2218.58109.2926.490.000Condition31247.4615.82100.800.000Interaction616.422.740.660.681Error1249.504.13  Total231531.96   
Tasks:
Set up null and alternative hypotheses to (a) test the significance of display panel effect, (b) test the significance of emergency condition effects, and (c) test the interaction a between the two factors.
Justify the type of ANOVA (one-way or two-way) you will apply to test the hypotheses.
Interpret the p values in the results table for an accept/reject decision regarding the hypotheses.
State your conclusions including what the ANOVA results do not tell you and your needed actions as a researcher.
Submission Details:
Submit a Microsoft Word document that contains your responses to assignment questions, using APA style.
Name your document SU_BUS7200_W3_ LastName_FirstInitial.doc

Must format answer in attached template. Must use attachments for data. Must use

Must format answer in attached template. Must use attachments for data. Must use previous attached assignment for reference.
Overview
The 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.
Scenario
You 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.
Directions
Using 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:
Introduction
Describe 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 Collection
Sampling 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 Analysis
Histogram: 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 Model
Scatterplot: 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.
Conclusions
Summarize 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.

Competencies In this project, you will demonstrate your mastery of the following

Competencies
In this project, you will demonstrate your mastery of the following competencies:
Apply statistical techniques to address research problems
Perform regression analysis to address an authentic problem
Overview
The 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.
Scenario
You 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.
Directions
Using 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:
Introduction
Describe 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 Collection
Sampling 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 Analysis
Histogram: 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 Model
Scatterplot: 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.
Conclusions
Summarize 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.
What to Submit
To complete this project, you must submit the following:
Project One Template: Use this template to structure your report, and submit the finished version as a Word document.
Supporting Materials
The following resources may help support your work on the project:
Document: National Statistics and Graphs
Use this data for input in your project report.
Spreadsheet: Real Estate County Data
Use this data for input in your project report.

Question 1 A national survey was initiated and intended to capture the prevalenc

Question 1
A national survey was initiated and intended to capture the prevalence of HIV in the post-HAART (Highly active anti-retroviral therapy) era of treatment. Out of a sample of 1,483 participants, a total of 241 were found to be HIV positive.
A) Calculate a SE.
B) Construct a 95% confidence interval about the estimate.
Question 2
Suppose a national survey intends to identify the prevalence of Hepatitis C in a population of intravenous drug users. Out of a sample of 6,458 a total of 754 were confirmed to have the disease. 
A) Calcuate a SE using the plus-four method.
B) Construct a 99% confidence interval about the estimate.
Question 3
A national organization sets out to investigate the change in prevalence of HIV since the last census in 2010. A total of 4,706 participants were interviewed and a total of 468 responses were confirmed to be HIV positive. Assume that the data from the census indicated that the prevalence of HIV in the particular population was 7.5%.
A) Write out the null and alternative hypotheses for a formal test of significance.
B) Interpret your results at 95% confidence.
Question 4
The national blood bank sponsored by the US estimates that approximately 38% of the population has blood type O+. Suppose a local blood bank samples 14 participants, and determines that 8 participants are O+.
A) Can we approximate by the normal distribution in this instance?
B) Depending on your decision in part A), carry out a test to determine if the proportion of responses based on the local blood bank differ significantly from those at the national blood bank.
Question 5
A researcher is interested in conducting an observational study looking at the incidence of breast cancer in women 40-65 years of age followed for a period of 15 years. The researcher is interested in showing that the incidence of breast cancer has risen from 24% to 35% since the last surveillance data was published covering this population. If the researcher wants to be 95% confident with 90% power, how many participants should be enrolled? Blank 1
DUE TONIGHT BY 11:00 PM EST TIME

For this assessment, you will develop a 3-4 page critique of the quantitative de

For this assessment, you will develop a 3-4 page critique of the quantitative design, methods, and results of a scholarly study.
The ability to use quantitative approaches to analyze health care data is a vital skill for today’s doctoral prepared professional. You will be expected to have the skills to critically assess the deeper analytical qualities of an article and ultimately comment on its overall validity and practical relevance. This assessment will provide you with an opportunity to demonstrate and hone your ability to analyze and critique the quantitative methods of a research study using an example from the literature.
For the learner who has finished the data collection process for the doctoral project, analysis of that data offers an exciting (and sometimes challenging) opportunity of discovery. One of the most common statistical techniques for examining the relationship of two variables is correlation analysis. The specific kind of correlational technique depends on the combination of the measurement level (that is, categorical, ordinal, or interval or ratio) of the two data variables being examined. Correlation analysis can tell us the direction and strength of relationships between two variables.
Overview
Whether preparing a scholarly document for your doctoral program or simply trying to stay current in your professional field, you must continuously grow in your ability to read research. As an undergraduate, you probably just skimmed over an article’s abstract and introduction, focusing most of your attention on the interpretation of the results at the conclusion. As a doctoral-level professional, your colleagues will expect you to have the skills to critically assess the deeper analytical qualities of an article and ultimately comment on its overall validity and practical relevance.
Using the readings, media, and various resources in this course, you have an opportunity to engage in critical thinking to assess the analytical results of a peer-reviewed quantitative study. This assessment parallels and complements the literature critique skill set you have developed previously in your program.
The following will help provide you with potential approaches and frameworks to completing the critique and assessment portions of this assessment:
Shahnazi, H., Hosseintalaei, M., Esteki Ghashghaei, F., Charkazi, A., Yahyavi, Y., & Sharifirad, G. (2016). Effect of educational intervention on perceived susceptibility self-efficacy and DMFT of pregnant women. Iranian Red Crescent Medical Journal, 18(5), e24960.
Coughlan, M., Cronin, P., & Ryan, F. (2007). Step-by-step guide to critiquing research. Part 1 – Quantitative research. British Journal of Nursing, 16(11), 658–663.
The Value of a Research Critique to Translate Evidence Into Practice.
Demonstration of Proficiency
By successfully completing this assessment you will address the following scoring guide criteria, which align to the indicated course competencies.
Competency 1: Describe underlying concepts and reasoning related to the collection and evaluation of quantitative data in health care research.
Describe the study results for a quantitative study published in scholarly literature.
Competency 3: Interpret the results and practical significance of statistical health care data analyses.
Interpret and critique the analytical testing approach used in a quantitative study described in scholarly literature.
Competency 4: Assess the quality of quantitative research methods reported in peer-reviewed health care literature.
Cite and summarize a selected article.
Assess the overall methodological quality of an article using critique guidelines.
Competency 5: Address assignment purpose in a well-organized text, incorporating appropriate evidence and tone in grammatically sound sentences.
Articulate meaning relevant to the main topic, scope, and purpose of the prompt. 
Apply APA formatting to in-text citations and references.
Instructions
Read the article by Shahnazi et al. linked earlier in these instructions.
Cite and summarize the article.
Include study PICO, goals, intervention, and assessment data collected.
Describe, interpret, and critique the statistical testing approach.
Include preanalytic normal distribution and post-intervention analytical testing. The article by Shahnazi et al. may be a helpful reference.
Describe, interpret, and critique the study’s results from the analysis.
Address issues of significance; type I and II errors, confidence intervals, and effect sizes.
Assess the overall methodological quality of the article using the step-by-step critique guidelines in the article by Coughlan, Cronin, and Ryan, linked above.
Additional Requirements
Length: Your paper will be 3–4 double-spaced pages of content plus title and reference pages.
Font: Times New Roman, 12 points.
APA Format: Your title and reference pages must follow APA format and style guidelines. See the APA Module for more information. The body of your paper does not need to conform to APA guidelines. Do make sure that it is clear, persuasive, organized, and well written, without grammatical, punctuation, or spelling errors. You also must cite your sources according to APA guidelines.
Please review the assessment scoring guide before submitting your paper. The requirements outlined above correspond to the grading criteria in the scoring guide, so be sure to address each point. In addition, you may wish to review the performance-level descriptions for each criterion to see how your work will be assessed.

For this assessment, you will determine the relevant statistical tests to apply

For this assessment, you will determine the relevant statistical tests to apply to the analysis of a data set, and then write a 3–4 page interpretation of the results of your analysis.
This assessment will ask you to select, apply, and interpret the results of a variety of statistical tests on a health care data set. This may include tests you have learned about or applied previously in the course, or the new nonparametic t-Test which is presented in the resources for this assessment. The challenge is using what you have learned to determine the best course of action to complete the interpretative tasks the assessment lays out for you. This attempts to mirror real-world situations where the data or statistical analysis could be approached in a variety of different ways. To decide which statistical test to use for the various dependent variables to be analyzed, one must first know more about the data type (measurement level) within those variables.
Overview
Public health researchers are often involved in collaborating in the design, development, and analysis of community initiatives of varying complexity. While this course alone will not provide sufficient training for you to act as a statistical consultant, it does offer a broad and practice-based analytic foundation that can position you to better understand and more fully contribute to real-world project teams. Building on the basic statistical concepts and analytical techniques of the previous units, this assessment is an opportunity to use your cumulative quantitative-analysis skills to address a broad set of real-world research questions.
Demonstration of Proficiency
By successfully completing this assessment you will address the following scoring guide criteria, which align to the indicated course competencies.
Competency 2: Apply appropriate statistical methods using common software tools in the collection and evaluation of health care data.
Perform the most appropriate parametric or nonparametric test to answer each question.
Competency 3: Interpret the results and practical significance of statistical health care data analyses.
Assess the assumption of normal distribution prior to analysis.
Appropriately interpret the statistical output (such as estimate, p-value, confidence interval, and effect size) resulting from each statistical test.
Summarize the clinical implications, significance, and potential limitations of the study data and outcomes.
Competency 4: Assess the quality of quantitative research methods reported in peer-reviewed health care literature.
Describe the practical significance of the results of statistical tests.
Competency 5: Address assignment purpose in a well-organized text, incorporating appropriate evidence and tone in grammatically sound sentences.
Articulate meaning relevant to the main topic, scope, and purpose of the prompt. 
Apply APA formatting to in-text citations and references.
Instructions
Complete the following for this two-part assessment.
Software
The following statistical analysis software is required to complete your assessments in this course:
IBM SPSS Statistics Standard or Premium GradPack, version 22 or higher, for PC or Mac.
You have access to the more robust IBM SPSS Statistics Premium GradPack.
Please refer to the Statistical Software page on Campus for general information on SPSS software, including the most recent version made available to Capella learners.
Part 1: Yoga and Stress Study Statistical Tests
Use the Yoga Stress (PSS) Study Data Set [XLSX] to determine the measurement level of data of the dependent or outcome variable (Psychological Stress Score) you are analyzing.
Is the data categorical, ordinal, or interval or ratio?
Before performing any statistical tests, you must determine which tests would be most appropriate for your data type.
Perform a preevaluation of the data for outliers (all variables) and normal distribution (only dependent variables) as you have done previously.
Use How to Choose a Statistical Test [PPTX] as general guidance in helping you to decide which test to use.
Use the readings, media, resources, and textbook as guides to perform an analysis of the selected variables.
Perform and interpret an appropriate series of statistical tests (including preanalytical testing for outliers and normal distribution of data) that answer the following research questions:
How would you quantitatively describe the study population?
Summarize the primary demographic data using descriptive statistics.
Is there any association between gender and race in this military study?
Perform an appropriate chi-square analysis.
Perform preliminary assessment of the data, then compare pretest to post-test scores.
In total population being studied, what was the effect of the yoga intervention on stress?
Provide the SPSS “.sav” output file that shows your programming and results for this assessment.
Part 2: Interpretive Report
Summarize the clinical implications related to the statistical outcomes for each of the questions above.
Describe potential limitations of the study (Part 1, number 3).
Additional Requirements
Length: Your paper will be 3–4 typed, double-spaced pages of content plus title and reference pages.
​Font: Times New Roman, 12 points.
APA Format: Your title and reference pages must conform to APA format and style guidelines. See the APA Module for more information. The body of your paper does not need to conform to APA guidelines. Do make sure that it is clear, persuasive, organized, and well written, without grammatical, punctuation, or spelling errors. You also must cite your sources according to APA guidelines.
Refer to the helpful links in Resources as you prepare your assessment.
Please review the assessment scoring guide before completing your submission. The requirements outlined above correspond to the grading criteria in the scoring guide, so be sure to address each point. In addition, you may want to review the performance-level descriptions for each criterion to see how your work will be assessed.

Let’s consider the phrases at least, at most, less than, more than. Often when w

Let’s consider the phrases at least, at most, less than, more than. Often when we are looking at probabilities, it is phrases like those which are key to translating a word problem into a probabilistic problem. 
Consider the following statement: The probability that Mary (spends at least, spends at most, spends less than, spends more than) 20 minutes per day exercising. Depending on which phrase you chose in parenthesis you end up with a different expression and meaning in terms of probability theory. For instance, P(x≤20)(probability Mary spends at most 20 minutes exercising) means something different from P(x>20)(probability Mary spends more than 20 minutes exercising).
Write an expression related to your major for each of phrases above and label them with the correct mathematical symbol (use my example as a reference, but I encourage creativity here!). Then explain the differences between the four statements i.e., differences between at least, at most, less than, more than.