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PSY 1110 Relationships Between Measurement Variables Questions
The primary objective of Module 5 is to introduce basic relationships between measurement
variables. Specifically, by the ...
PSY 1110 Relationships Between Measurement Variables Questions
The primary objective of Module 5 is to introduce basic relationships between measurement
variables. Specifically, by the completion of this lesson you should be able to:use a scatterplot
describe the characteristics of correlations
calculate and interpret the Pearson Product Moment Correlation (r)
describe factors that can affect correlation
distinguish between correlation and causation
calculate and interpret a regression line
explain what a regression line tells us
calculate and interpret r
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SNHU Selling Price and Area Analysis for DM Pan National Real Estate Company
OverviewRecall that samples are used to generate a statistic, which businesses use to estimate the population parameter. Y ...
SNHU Selling Price and Area Analysis for DM Pan National Real Estate Company
OverviewRecall that samples are used to generate a statistic, which businesses use to estimate the population parameter. You have learned how to take samples from populations and use them to produce statistics. For two quantitative variables, businesses can use scatterplots and the correlation coefficient to explore a potential linear relationship. Furthermore, they can quantify the relationship in a regression equation.PromptThis assignment picks up where the Module Two assignment left off and will use components of that assignment as a foundation.You have submitted your initial analysis to the sales team at D.M. Pan Real Estate Company. You will continue your analysis of the provided Real Estate County Data spreadsheet using your selected region to complete your analysis. You may refer back to the initial report you developed in the Module Two Assignment Template to continue the work. This document and the National Statistics and Graphs spreadsheet will support your work on the assignment.Note: In the report you prepare for the sales team, the dependent, or response, variable (y) should be the median listing price and the independent, or predictor, variable (x) should be the median square feet.Using the Module Three Assignment Template, specifically address the following:Regression Equation: Provide the regression equation for the line of best fit using the scatterplot from the Module Two assignment.Determine r: Determine r and what it means. (What is the relationship between the variables?)Determine the strength of the correlation (weak, moderate, or strong).Discuss how you determine the direction of the association between the two variables.Is there a positive or negative association?What do you see as the direction of the correlation?Examine the Slope and Intercepts: Examine the slope b1{"version":"1.1","math":"b1"} and intercept b0{"version":"1.1","math":"b0"}.Draw conclusions from the slope and intercept in the context of this problem.Does the intercept make sense based on your observation of the line of best fit?Determine the value of the land only.Note: You can assume, when the square footage of the house is zero, that the price is the value of just the land. This happens when x=0, which is the y-intercept.Determine the R-squared Coefficient: Determine the R-squared value.Discuss what R-squared means in the context of this analysis.Conclusions: Reflect on the Relationship: Reflect on the relationship between square feet and sales price by answering the following questions:Is the square footage for homes in your selected region different than for homes overall in the United States?For every 100 square feet, how much does the price go up (i.e., can you use slope to help identify price changes)?Use the regression equation to estimate how much you would list your house for if it was 1,200 square feet.What square footage range would the graph be best used for?Guidelines for SubmissionSubmit your completed Module Three Assignment Template as a Word document that includes your response and supCompetenciesIn 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.What to SubmitTo 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 MaterialsThe following resources may help support your work on the project:Document: National Statistics and GraphsUse this data for input in your project report.Spreadsheet: Real Estate County DataUse this data for input in your project report.porting charts/graphs. The following resources support your work on this assignment:
Grossmont College Drinking Habits Statistics Lab Discussion
CONTEXTA university conducted a student survey, collecting data from a random sample of 236 undergraduate students.The dat ...
Grossmont College Drinking Habits Statistics Lab Discussion
CONTEXTA university conducted a student survey, collecting data from a random sample of 236 undergraduate students.The data for this lab is a subset of the real data collected by the university. Some students did not answer all of the questions in the survey. We use the symbol * in the spreadsheet to indicate missing data. VARIABLESGender Male or FemaleAlcohol Number of alcoholic beverages consumed in a typical weekHeight Self-reported height (in inches)Cheat Would you tell the instructor if you saw somebody cheating on an exam? (0=No, 1=Yes)DATAIf you have not done so already, download the drinking (Links to an external site.) datafile, then upload the file in StatCrunch. PROMPTIn your initial discussion post, respond to each of the following after you download the drinking.xlsx file provided in the Data section below.Describe the distribution of weekly alcohol consumption using concepts from Unit 2.Make an appropriate graph and provide appropriate numerical summaries. To recall how to create these items, see the StatCrunch directions provided below.Describe the shape, center and spread using numerical measures that best describe the distribution.In your description, include an interval of typical values and a discussion of variability.Embed your StatCrunch graph in your initial post. To recall how to embed StatCrunch output, see the directions provided below.Do the data suggest that drinking is a problem in this university?Use the data to support your answer.NOTE: I will provide you the Canvas log in and password when you bid the question to view and understand the discussion better. You have to use StatCrunch for the data so I will provide you with the log in info too.Last NOTE: use you own words with basic English. NO copying or Plagiarism.
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PSY 1110 Relationships Between Measurement Variables Questions
The primary objective of Module 5 is to introduce basic relationships between measurement
variables. Specifically, by the ...
PSY 1110 Relationships Between Measurement Variables Questions
The primary objective of Module 5 is to introduce basic relationships between measurement
variables. Specifically, by the completion of this lesson you should be able to:use a scatterplot
describe the characteristics of correlations
calculate and interpret the Pearson Product Moment Correlation (r)
describe factors that can affect correlation
distinguish between correlation and causation
calculate and interpret a regression line
explain what a regression line tells us
calculate and interpret r
2
25 pages
Test Answers
Part 1 of 7 - Linear Regression and Correlation; Outliers and Values of Correlation During the 2008 Recession homeowners l ...
Test Answers
Part 1 of 7 - Linear Regression and Correlation; Outliers and Values of Correlation During the 2008 Recession homeowners lost thousands of dollars on ...
SNHU Selling Price and Area Analysis for DM Pan National Real Estate Company
OverviewRecall that samples are used to generate a statistic, which businesses use to estimate the population parameter. Y ...
SNHU Selling Price and Area Analysis for DM Pan National Real Estate Company
OverviewRecall that samples are used to generate a statistic, which businesses use to estimate the population parameter. You have learned how to take samples from populations and use them to produce statistics. For two quantitative variables, businesses can use scatterplots and the correlation coefficient to explore a potential linear relationship. Furthermore, they can quantify the relationship in a regression equation.PromptThis assignment picks up where the Module Two assignment left off and will use components of that assignment as a foundation.You have submitted your initial analysis to the sales team at D.M. Pan Real Estate Company. You will continue your analysis of the provided Real Estate County Data spreadsheet using your selected region to complete your analysis. You may refer back to the initial report you developed in the Module Two Assignment Template to continue the work. This document and the National Statistics and Graphs spreadsheet will support your work on the assignment.Note: In the report you prepare for the sales team, the dependent, or response, variable (y) should be the median listing price and the independent, or predictor, variable (x) should be the median square feet.Using the Module Three Assignment Template, specifically address the following:Regression Equation: Provide the regression equation for the line of best fit using the scatterplot from the Module Two assignment.Determine r: Determine r and what it means. (What is the relationship between the variables?)Determine the strength of the correlation (weak, moderate, or strong).Discuss how you determine the direction of the association between the two variables.Is there a positive or negative association?What do you see as the direction of the correlation?Examine the Slope and Intercepts: Examine the slope b1{"version":"1.1","math":"b1"} and intercept b0{"version":"1.1","math":"b0"}.Draw conclusions from the slope and intercept in the context of this problem.Does the intercept make sense based on your observation of the line of best fit?Determine the value of the land only.Note: You can assume, when the square footage of the house is zero, that the price is the value of just the land. This happens when x=0, which is the y-intercept.Determine the R-squared Coefficient: Determine the R-squared value.Discuss what R-squared means in the context of this analysis.Conclusions: Reflect on the Relationship: Reflect on the relationship between square feet and sales price by answering the following questions:Is the square footage for homes in your selected region different than for homes overall in the United States?For every 100 square feet, how much does the price go up (i.e., can you use slope to help identify price changes)?Use the regression equation to estimate how much you would list your house for if it was 1,200 square feet.What square footage range would the graph be best used for?Guidelines for SubmissionSubmit your completed Module Three Assignment Template as a Word document that includes your response and supCompetenciesIn 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.What to SubmitTo 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 MaterialsThe following resources may help support your work on the project:Document: National Statistics and GraphsUse this data for input in your project report.Spreadsheet: Real Estate County DataUse this data for input in your project report.porting charts/graphs. The following resources support your work on this assignment:
Grossmont College Drinking Habits Statistics Lab Discussion
CONTEXTA university conducted a student survey, collecting data from a random sample of 236 undergraduate students.The dat ...
Grossmont College Drinking Habits Statistics Lab Discussion
CONTEXTA university conducted a student survey, collecting data from a random sample of 236 undergraduate students.The data for this lab is a subset of the real data collected by the university. Some students did not answer all of the questions in the survey. We use the symbol * in the spreadsheet to indicate missing data. VARIABLESGender Male or FemaleAlcohol Number of alcoholic beverages consumed in a typical weekHeight Self-reported height (in inches)Cheat Would you tell the instructor if you saw somebody cheating on an exam? (0=No, 1=Yes)DATAIf you have not done so already, download the drinking (Links to an external site.) datafile, then upload the file in StatCrunch. PROMPTIn your initial discussion post, respond to each of the following after you download the drinking.xlsx file provided in the Data section below.Describe the distribution of weekly alcohol consumption using concepts from Unit 2.Make an appropriate graph and provide appropriate numerical summaries. To recall how to create these items, see the StatCrunch directions provided below.Describe the shape, center and spread using numerical measures that best describe the distribution.In your description, include an interval of typical values and a discussion of variability.Embed your StatCrunch graph in your initial post. To recall how to embed StatCrunch output, see the directions provided below.Do the data suggest that drinking is a problem in this university?Use the data to support your answer.NOTE: I will provide you the Canvas log in and password when you bid the question to view and understand the discussion better. You have to use StatCrunch for the data so I will provide you with the log in info too.Last NOTE: use you own words with basic English. NO copying or Plagiarism.
9 pages
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The evolution of data sharing and communication platforms such as Twitter, Facebook, Instagram, and emails has resulted in ...
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