Please note that my final exam seminar for Stat 2000 is now scheduled and I am taking registrations.  I have split the seminar into two days since we will have to cover Lesson 6 in Volume 1 as well as all of Volume 2.  The seminar is from 9:00 am to 6:00 pm each day in room 100 St. Paul's College.  Each day will cost $40 or, if you attend Day One, you can attend Day Two for half-price (you will pay a total of $60, in other words).

 

Day One is Easter Sunday, March 31 and Day Two 2 weeks later on Sunday, April 14.

 

Click here for more information and/or to register for the Stat 2000 seminar.

 

 

Grant's Tutoring Study Guides (Including Free Samples)

You need to study the Chi-Square Goodness of Fit part of Lesson 8: Chi-Square Tests (if you are using an older edition of my book, this may be Lesson 9).  You also will need to study Lesson 9: Review of Linear Regression and Lesson 10: Inferences for Linear Regression (up to the end of question 3, you do not need to study the Multiple Linear Regression section at this time).  Note that they have omitted Multiple Linear Regression and Lesson 11: Nonparametric Tests (The Sign Test) this term.

 

Question 1 is not unlike my question 5 in Lesson 8.  Make sure you follow their instructions for rounding.  They make their goodness-of-fit table horizontally, while I prefer to make mine vertically.

 

Question 4 is a runthrough of Linear Regression.  Be sure to study Lessons 9 and 10 in my book before attempting this and the rest of the questions in this assignment.  You should especially work through question 1 in Lesson 9 and questions 1 and 3 in Lesson 10.

 

Note that they give you SSE, the sum of the squared residuals, so you are able to compute the variance of the residuals (MSE = SSE/DFE).  MSE is your estimate for σ, as requested in part (e).  That is what I call s_e, the standard deviation of the residuals, the estimate for σ_ε, the standard deviation of the population of residuals.

 

Never forget, in a regression context, if they start talking about σ or s, they are referring to the standard deviation of the residuals for the population or sample, respectively.  To add to the confusion, they called s, σ-hat in this part.

 

Open a "New Data Table".  Enter all the data for x in Column 1 and all the data for y in Column 2.  Be sure to name the columns appropriately.  I think you all get different questions here, so I am unable to be more specific.  Select "Analyze, Fit Y By X".  Highlight "Column 1" and click "X, Factor".  Highlight "Column 2" and click "Y, Response".  Click OK.

 

You should now be looking at a scatterplot.  Click the red triangle and select Density Ellipse and select 0.99 (it doesn't matter; you don't want this at all, but this gives you a summary of the means, standard deviations, and the correlation coefficient, r).  Click the red triangle that appears below the scatterplot which says Bivariate Normal Ellipse and deselect "Line of Fit" to make the ellipse disappear from your scatterplot.  You will also note that there is a title bar called Correlation below the scatterplot now.  Click the blue triangle to open it up and confirm the means and standard deviations match those you were given.  If not, perhaps you were mixed up which one was x and which one was y?

 

Click the red triangle and select "Fit Line" to get the least-squares regression line.  You now have all the outputs you need.  Be sure to read in Lesson 10 the connection between the t test statistic for the slope and the t test statistic for the correlation.  And also the connection between t for the slope and F for the slope.  Although they want you to do a lot of this question by hand (and you certainly should since that will also happen on the exam), do note that JMP does do a lot of this stuff for you and you can use it to check your answers before you submit them.

 

Question 5 is not unlike my question 2 in Lesson 10.