GESC 254 Wilfrid Laurier University SPSS Bivariate Analysis
In this lab, you will apply bivariatestatistics learned in class to your own Qualtrics survey dataset. You will save your final sets ofprocedures/commandsto a “syntax” file (a form oftext editor) as a means to document what you have done and potentially re-use it in the future. In SPSS you will comparemeans, create boxplots/scatterplots, calculate correlation coefficients, apply lines of best fit, and create cross-tabulated tables. You will copy and paste the most interesting results to MS Word, and provide a full interpretation.
WK 5 Testing Rotational Zeros and Polynomial Factors Discussion
I’m working on a algebra project and need support to help me learn.
Use synthetic division to determine whether is a factor of . Show all steps of the synthetic division. You must use the equation editor. Please do not post handwritten solutions. Note formatting is CRITICAL to the understanding; make sure your work can be easily followed.
Clearly state your linear factor and whether it is or is not a factor of the polynomial – and why you know this.
Use the rational zeros test to list all potential rational zeros.
MATH 160 Cuyamaca College Regression Line and Absolute Prediction Error Questions
Instructions
Progress Check
Use this activity to assess whether you can:
Use StatCrunch to graph a scatterplot with its least squares regression line and to simultaneously produce the equation of the regression line along with its correlation coefficient, r.
Identify the x with the largest absolute prediction error.
Explain why a given data point is an outlier.
In this activity you will use StatCrunch and embed your results in an essay question. The essay questions are not automatically graded; your instructor will enter the points for these questions later. WARNING: you will need to enter your response to each essay question with every attempt. Your instructor will only grade the essay for your attempt with the highest total score for the automatically graded questions.
Discussion Board
Use the Module 27discussion board (opens in a new tab) to ask questions or provide feedback about the problems in any Module 27 activity – including this lab.
Some features of this activity may not work well on a cell phone or tablet. We highly recommend that you complete this activity on a computer.
A list of StatCrunch directions is provided at the bottom of this text-box.
Context
The modern Olympic Games have changed dramatically since their inception in 1896. Are athletes getting better? We will use regression to investigate the change in winning times for one event—the men’s 1,500 meter race.
Variables
Year: the year of the Olympic Games, from 1896 to 2000. Time: the winning time for the 1,500 meter race, in seconds.
Since the winning time depends on the year, the Year since 1896 is the explanatory variable, and the Winning time is the response variable.
Data
Download the olympics (Links to an external site.) datafile for the men’s 1,500 meter race. Then upload the datafile in StatCrunch. If you need a reminder about how to do this, review the list of StatCrunch directions below.
Prompt
In the first two questions below, you will use StatCrunch to produce and examine the scatterplot for the olympics datafile. You will also use StatCrunch to find the regression equation and correlation coefficient.
List of StatCrunch Directions
As you work through numbers 1) and 2) below, refer back to these StatCrunch directions when you need a quick reminder.
These directions assume that you have uploaded the olympics datafile in StatCrunch, and the StatCrunch worksheet with the data is open. If not, please see the Data section above.
Using the year since 1896 as the explanatory variable and the winning time as the response variable: graph the scatterplot with the regression line and produce the regression equation with the correlation coefficient – all at the same time (directions)
Toggle to the output page with the scatterplot and regression line. Notice that the data has a strong linear association, so it makes sense to use linear regression. (Always check the form of the scatterplot before using linear regression.)
Download the StatCrunch output page with your scatterplot and regression line graphed together. (directions)
Save the .png file (the graph of your scatterplot and regression line) to your Stats-Class folder. (directions)
Embed the .png file for your scatterplot and regression line in the text-box below. (directions)
UnansweredQuestion 2Not yet graded / 3 pts
These directions assume you have produced the Simple linear regression results in a multipage StatCrunch output window. If not please see the previous question.
Toggle to the StatCrunch output page with the regression equation, correlation coefficient, and other statistics.
Under the heading Simple linear regression results, copy and paste the first five lines (dependent variable, independent variable, linear equation, sample size, and R) into the text-box below. (directions)
Simple linear regression results:
Dependent Variable: Time Independent Variable: Year Time = 994.19341 – 0.39304496 Year Sample size: 24 R (correlation coefficient) = -0.89075356
Question 32 / 2 pts
For which of the years 1900, 1940, or 2000 is the absolute prediction error the largest?
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Correct. Vertical distance from the regression line is the prediction error. The data point for 2000 is father from the regression line than the other options, so the prediction error is largest.
Question 42 / 2 pts
For the year 1896, the winning time for the men’s 1500-meter race is an outlier. In what ways is this data point an outlier? Check all that apply
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Correct. The winning time in 1896 is much larger than the other winning times.
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Correct. The data point (1896, 273) deviates from the pattern in the rest of the data. It does not follow the strong, negative association or the linear pattern.
MAT 240 Southern New Hampshire University Housing Price Prediction Worksheet
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 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 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 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 houses.
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:
Walden University High School Longitudinal Study Dataset Discussion
Whether in a scholarly or practitioner setting, good research and data analysis should have the benefit of peer feedback. For this Discussion, you will post your response to the hypothesis test, along with the results. Be sure and remember that the goal is to obtain constructive feedback to improve the research and its interpretation, so please view this as an opportunity to learn from one another.
To prepare for this Discussion:
Review this week’s Learning Resources and media program related to multiple regression.
Create a research question using the Afrobarometer Dataset or the HS Long Survey Dataset, that can be answered by multiple regression.
Use SPSS to answer the research questions.
If you are using the Afrobarometer Dataset, report the mean of Q1 (Age). If you are using the HS Long Survey Dataset, report the mean of X1SES.
What is your research question?
What is the null hypothesis for your question?
What research design would align with this question?
What dependent variable was used and how is it measured?
What independent variables are used and how are they measured? What is the justification for including these predictor variables?
If you found significance, what is the strength of the effect?
Explain your results for a lay audience, explain what the answer to your research question.
Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.
Learning Resources
Required Readings
Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.
Chapter 12, “Regression and Correlation” (pp. 401-457) (previously read in Week 8)
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
Chapter 8, “Correlation and Regression Analysis”
Chapter 11, “Editing Output” (previously read in Week 2, 3, 4, 5. 6, 7, and 8)
Note: The approximate length of this media piece is 7 minutes.
In this media program, Dr. Matt Jones demonstrates multiple regression using the SPSS software.
Accessible player
Optional Resources
Skill Builder: Interpreting the Results from Regression Models
To access these Skill Builders, navigate back to your Blackboard Course Home page, and locate “Skill Builders” in the left navigation pane. From there, click on the relevant Skill Builder link for this week.
You are encouraged to click through these and all Skill Builders to gain additional practice with these concepts. Doing so will bolster your knowledge of the concepts you’re learning this week and throughout the course.
Walden University Week 9 Performance in the KPSS Exam Discussion
Why did the authors use multiple regression?
Do you think it’s the most appropriate choice? Why or why not?
Did the authors display the data?
Do the results stand alone? Why or why not?
Did the authors report effect size? If yes, is this meaningful?
Use proper APA format, citations, and referencing.
Learning Resources
Required Readings
Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.
Chapter 12, “Regression and Correlation” (pp. 401-457) (previously read in Week 8)
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
Chapter 8, “Correlation and Regression Analysis”
Chapter 11, “Editing Output” (previously read in Week 2, 3, 4, 5. 6, 7, and 8)
Note: The approximate length of this media piece is 7 minutes.
In this media program, Dr. Matt Jones demonstrates multiple regression using the SPSS software.
Accessible player –Downloads– Download Video w/CC Download Audio Download Transcript
Optional Resources
Skill Builder: Interpreting the Results from Regression Models
To access these Skill Builders, navigate back to your Blackboard Course Home page, and locate “Skill Builders” in the left navigation pane. From there, click on the relevant Skill Builder link for this week.
You are encouraged to click through these and all Skill Builders to gain additional practice with these concepts. Doing so will bolster your knowledge of the concepts you’re learning this week and throughout the course.
Math 280 Minnesota State University Mankato Practice Problems Questions
I have attached the questions, and I marked them with yellow. please make sure to go through the questions before bidding. I will provide more information if needed later. Thank you.
