In multiple regression, the relative size of the coefficients is not important. For example, your company may have a nationwide hiring program that focuses on hiring employees who have graduated from college in the past 3 years, and let's say you want to know what attributes of those graduates has the biggest influence on sales ($M) in their first year on the job. You hypothesize that the factors that will influence first year sales to be undergraduate GPA (GPA), years of experience since graduation (EXP), the quality of their undergraduate institution (RANK), and their performance on the Wonderlic test (TEST). You estimate the regression as:
It is difficult to compare the size of the various coefficients because each of the independent variables is measured on a different scale. Undergraduate GPA is measured on a scale from 0.0 to 4.0. Experience ranges from 0 to 3. University ranking ranges from 1 to 4 (with 1 being the highest rank), and the Wonderlic test ranges from 0 to 50. Can you think of a way to compare the coefficients? If you are going to take this information to make a decision of where to focus your hiring, which element should you place the highest emphasis on?© BrainMass Inc. brainmass.com October 17, 2018, 1:55 am ad1c9bdddf
The solution is comprised of detailed step-by-step calculations and explanation of the given problems related to Correlation and Regression Analysis. This solution provides students with a clear perspective of the underlying statistical aspects.
Scatterplots and coefficients and regression analysis
I need help with the attached. It deals with scatter plots.
Thank you for you help.
1. A value of r = -0.851 shows that there is very little relationship between the two variables being compared.
2. A value of r = 0.158 shows that there is very little relationship between the two variables being compared.
3. If a correlation coefficient of r = 0.642 was found between two variables in a sample of paired data that were measured in feet, the value of r would change if the data were converted to inches and r was computed again.
4. Bear Chest Size and Weight. Listed below are the chest sizes (in inches) and weights (in pounds) of randomly selected bears that were anesthetized and measured (based on data from Gary Alt and Minitab, Inc.). Because it is much more difficult to weigh a bear than to measure its chest size, the presence of a correlation could lead to a method for estimating weight based on chest size. Is there a linear correlation between chest size and weight?
5. Buying a TV Audience. The New York Post published the annual salaries (in millions) and the number of viewers (in millions), with results given below for Oprah Winfrey, David Letterman, Jay Leno, Kelsey Grammer, Barbara Walters, Dan Rather, James Gandolfini, and Susan Lucci, respectively. Is there a correlation between salary and number of viewers? Which of the listed stars has the lowest cost per viewer? Highest cost per viewer?
6. Temperatures and marathons. In "The Effects of Temperature on Marathon Runner's Performance," by David Martin and John Buoncristiani (Chance, Vol. 12, No. 4), high temperatures and times (in minutes) were given for women who won the New York City marathon in recent years. Results are listed below. Is there a correlation between temperature and winning time? Does it appear that winning times are affected by temperature?
7. Use the same data sets as Exercises 13-32 in Section 10-2. In each case, find the regression equation, letting the first variable be the independent (x) variable. Find the indicated predicted values.
Bear Chest Size and Weight. Find the best predicted weight (in pounds) of a bear with a chest size of 50 in.View Full Posting Details