Stat 2000: Assignment 7 Tips (Distance/Online Sections)

Published: Sun, 03/03/13


 
My tips for Assignment 7 are coming below, but first a couple of announcements.
 
I plan to split my final exam seminar over two days.  Day One will be Sunday, March 31 (Easter Sunday), and Day Two will be Sunday, April 14.  Each day will cover different lessons in my book, so you will have to attend both days if you want to see the entire seminar.  It will cost $40 to attend just one day, or, if you attend the first day, you can attend the second day for half-price (meaning you will pay a total of $60 for both days).  I am still confirming rooms and dates, so I am not ready to take registrations yet, but wanted to give you a heads-up so you can make plans to attend one or both days if you wish.
 
Did you read my Tips on How to Do Well in this Course? 
Make sure you do:  Tips on How to Do Well in Stat 2000 
 
Did you read my Tips on what kind of calculator you should get?
Tips on what calculator to buy for Statistics
 
Did you miss my Tips for Assignment 6?
Tips for Stat 2000 Distance Assignment 6
 
If you are taking the course by Classroom Lecture (Sections A01, A02, etc.), there is no Assignment 7.
 
Tips for Assignment 7 (Distance/Online Sections D01, D02, D03, etc.)
 
Don't have my book?  You can download a free sample containing Lesson 3 at my website here:
Grant's Tutoring Study Guides (Including Free Samples)
 
Continue to study Lesson 10 at least up to the end of question 3 to prepare for this assignment.  You do not need to study the section on Multiple Linear Regression at this time.  Note that HW 6, 7 and 8 all deal with concepts from Lesson 10.
 
Question 1 is similar to my question 3 in Lesson 10.  Note, by s, the standard error points about the regression line, they mean the standard deviation of the residuals, Se.
 
Question 2 is standard stuff.  Obviously, watch that they have changed the value of n.  This is demonstrating that, if you choose a large enough sample size, almost any nonzero r value will be statistically significant (but perhaps of no practical importance).
 
You will use JMP for question 3.  Open a "New Data Table" and create two columns.  Name the first column "Diameter" and the second column "Height".  Remember, to create a new column, simply double-click in the space at the top of the column, to the right of a pre-existing column.  Enter in the data manually, and we are now ready to analyze the data. Double-click both column names and confirm their Data Type is Numeric and their Modeling Type is Continuous.
 
Question 3(a) and (b):  Select "Analyze" then "Fit Y by X".  You should be able to tell which is x and which is y.  Select the y variable and click "Y, Response" and select the x variable and click "X, Factor".  Click OK.  You will now see a scatterplot.  Click the red triangle next to "Bivariate Fit ..." and select "Fit Line" to have JMP compute and graph the least-squares regression line.  When they tell you to "interpret the regression coefficient," I believe they want you to interpret the slope (since you have just found the slope).  The slope can also be called the coefficient, as we see in multiple linear regression.
 
Question 3(c): JMP gives you the coefficient of determination, r2.  Compute r from that value.
 
Question 3(d) and (e):  Click the red triangle next to "Linear Fit" and select "Confid Curve Indiv" and "Confid Curve Fit" to get these two intervals they want.  As I tell you in Lesson 10, the curves that are closer to the line are the confidence intervals for the mean, the outer curves are the prediction intervals.
 
Question 3(f):  JMP already did this test for you when you selected "Fit Line".  The ANOVA table and the "Parameter Estimates" are giving you all the info you need (test statistic and P-value), but be sure to write out your hypotheses and conclusion in the file you are uploading.  You can determine if there is a linear relationship by either testing the hypothesis about zero correlation or a hypothesis about zero slope.  JMP gives us the latter in the ANOVA and Parameter Estimates, so I would do the zero slope hypothesis.  I show you how to read these outputs in my question 3 of Lesson 10.
 
Question 3(g) and (h):  These must be computed by hand using the appropriate formulas and numbers from JMP as I show in my question 3 of Lesson 10. You need to know x-bar for this problem, so you can get JMP to do this for you.  In the output where you have your scatterplot, click the red triangle and select "Density Ellipse".  Select whichever one you like (.99, whatever, we don't want it anyway).  It will draw an ellipse on your scatterplot (which we have no use for at all), but it was also give you a table summarizing the means and standard deviations and the correlation for this problem.  Now get rid of that ellipse on the scatterplot.  You will see directly below the scatterplot a red triangle next to Bivariate Normal Ellipse or something like that.  Click the triangle and deselect the Line of Fit to make the ellipse go away.
 
Question 3(i):  You should know what this ratio is computing and how to determine it.  I talk about this in Lesson 10, and show you how to interpret it in question 1(d) of Lesson 9.