Using the data set that you identified in week 1, use Excel to find the following descriptive statistics for the price data.

Descriptive statistics:

Mean

Median

Standard Deviation

Use these summary statistics to make two conclusions or observations about the typical vehicle in the sample. One conclusion must relate to the measure of center (mean/median) and one to the variability (standard deviation) of the vehicles.

Next, add an 11th vehicle to the data set. Choose a “supercar” that costs at least $1 million. Recalculate the summary statistics to include this vehicle.

Descriptive statistics:

Mean

Median

Standard Deviation

Which summary statistics were affected the most by the addition of this outlier? How were they changed, and were you surprised by the results? I encourage you to review the Week 2 descriptive statistics PDF at the bottom of the discussions. This will give you a step by step example on how to calculate these values using Excel. I DO NOT recommend doing this by hand. Let Excel do the heavy lifting for you.

Attached is a copy of the first week work

To prepare for this Discussion:

• Review Warner’s Chapter 12 and Chapter 2 of the Wagner      course text and the media program found in this week’s Learning Resources      and consider the use of dummy variables.
• Create a research question using the General Social      Survey dataset that can be answered by multiple regression. Using the SPSS      software, choose a categorical variable to dummy code as one of your      predictor variables.

Estimate a multiple regression model that answers your research question. Post your response to the following:

1. What is your research question?
2. Interpret the coefficients for the model, specifically      commenting on the dummy variable.
3. Run diagnostics for the regression model. Does the model      meet all of the assumptions? Be sure and comment on what assumptions were      not met and the possible implications. Is there any possible remedy for      one the assumption violations?

Learning Resources

Required Readings

Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.

• Chapter 2, “Transforming Variables” 
• Chapter 11, “Editing Output” (previously read in Week 2, 3, 4, 5. 6, 7, 8, and 9)

Allison, P. D. (1999). Multiple regression: A primer. Thousand Oaks, CA: Pine Forge Press/Sage Publications.

Multiple Regression: A Primer, by Allison, P. D. Copyright 1998 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center. Chapter 6, “What are the Assumptions of Multiple Regression?” (pp. 119–136)

Allison, P. D. (1999). Multiple regression: A primer. Thousand Oaks, CA: Pine Forge Press/Sage Publications.

Multiple Regression: A Primer, by Allison, P. D. Copyright 1998 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center. 

Multiple Regression: A Primer, by Allison, P. D. Copyright 1998 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center. 

• Chapter 7, “What can be done about Multicollinearity?” (pp. 137–152)

Warner, R. M. (2012). Applied statistics from bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: Sage Publications.

Applied Statistics From Bivariate Through Multivariate Techniques, 2nd Edition by Warner, R.M. Copyright 2012 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center.

Applied Statistics From Bivariate Through Multivariate Techniques, 2nd Edition by Warner, R.M. Copyright 2012 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center.

Non-Normally Distributed Errors. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 41-49). Thousand Oaks, CA: SAGE Publications, Inc.

Fox, J. (1991). Regression diagnostics. Thousand Oaks, CA: SAGE Publications.

Discrete Data. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 62-67). Thousand Oaks, CA: SAGE Publications, Inc.

Nonconstant Error Variance. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 49-54). Thousand Oaks, CA: SAGE Publications, Inc.

Nonlinearity. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 54-62). Thousand Oaks, CA: SAGE Publications, Inc.

Outlying and Influential Data. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 22-41). Thousand Oaks, CA: SAGE Publications, Inc.

Fox, J. (Ed.). (1991). Regression diagnostics. Thousand Oaks, CA: SAGE Publications.

• Chapter 3, “Outlying and Influential Data” (pp. 22–41)
• Chapter 4, “Non-Normally Distributed Errors” (pp. 41–49)
• Chapter 5, “Nonconstant Error Variance” (pp. 49–54)
• Chapter 6, “Nonlinearity” (pp. 54–62)
• Chapter 7, “Discrete Data” (pp. 62–67)

Note: You will access these chapters through the Walden Library databases.

Document: Walden University: Research Design Alignment Table

Required Media

Laureate Education (Producer). (2016m). Regression diagnostics and model evaluation [Video file]. Baltimore, MD: Author.

Note: The approximate length of this media piece is 7 minutes.

In this media program, Dr. Matt Jones demonstrates regression diagnostics and model evaluation using the SPSS software.

Accessible player  –Downloads– Download Video w/CC Download Audio Download Transcript 

Laureate Education (Producer). (2016). Dummy variables [Video file]. Baltimore, MD: Author.

Note: This media program is approximately 12 minutes.

In this media program, Dr. Matt Jones demonstrates dummy variables using the SPSS software.

Accessible player  –Downloads– Download Video w/CC Download Audio Download Transcript 

Discrete Data. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 62-67). Thousand Oaks, CA: SAGE Publications, Inc.

Nonconstant Error Variance. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 49-54). Thousand Oaks, CA: SAGE Publications, Inc. 

Hi,

I have a practice mock for which i require full solutions, i need the full explanation for all the questions provided (explained in detail) in the attached documents. The topics for this set of questions is Section A: Theory and Technique(Limits, Differentiation, Integration) , Section B: Applications.

Recall the car data set you identified in Week 2. You will want to calculate the average for your data set. (Be sure you use the numbers without the supercar outlier) Once you have the average count how many of your data points fall below the average. You will take that number and divide it by 10. This will be your p or “success” in your problem. Once you have p, calculate q.

If you were to find another random sample of 10 cars based on the same data, what is the probability that exactly 4 of them will fall below the average? Make sure you interpret your results.

If you were to find another random sample of 10 cars based on the same data, what is the probability that fewer than 5 of them will fall below the average? Make sure you interpret your results.

If you were to find another random sample of 10 cars based on the same data, what is the probability that more than 6 of them will fall below the average? Make sure you interpret your results.

If you were to find another random sample of 10 cars based on the same data, what is the probability that at least 4 of them will fall below the average? Make sure you interpret your results.

I encourage you to review the Week 3 Binomial probabilities PDF at the bottom of the discussion. This will give you a step by step example to follow and show you how to find probabilities using Excel. You can also use this PDF in the Quizzes section.

There are additional PDFs that were created to help you with the Homework, Lessons and Tests in the Quizzes section. While they won’t be used to answer the questions in the discussion, they are just as useful and beneficial. I encourage you to review these ASAP! These PDFs are also located at the bottom of the discussion.

Once you have posted your initial discussion, you must reply to at least two other learner’s post. Each post must be a different topic. So, you will have your initial post from one topic, your first follow-up post from a different topic, and your second follow-up post from one of the other topics. Of course, you are more than welcome to respond to more than two learners.”

Instructions: You must respond to at least 2 other students. Responses may include direct questions. In your peer posts, compare the probabilities that you found with those of your classmates. Were they higher/lower and why? In your responses, refer to the specific data from your classmates’ posts. Make sure you include your data set in your initial post as well.