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A lab exercise for performing simple regression analysis in excel to estimate the relationships between attendance (y) and various factors (x), including population, capacity, prior wins, current wins, and teams. The document guides users through setting up the excel file, inserting new worksheets, and using the regression tool to estimate the intercept, slope, standard errors, and correlation coefficient for each relationship.
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Simple Regression
Part I. Setup
A. Be sure you’ve got the data analysis tool pack added in excel Launch Excel. Under “tools” in the main tool bar at the top of the screen see if the option “data analysis” is available (it should be at the bottom of the list). If data analysis is not there, do the following:
B. Data For this lab, we’re going to use a data set called “baseball attendance and 12 factors”. It is on my webpage for ECN 422.
1. Open the data set by clicking the appropriate link on the webpage, then save it as an excel worksheet (onto the C drive or onto a floppy disk). Be sure to close the browser version of excel and re-open the newly saved file in excel.
C. Purpose of Today’s lab In today’s lab we’re going to learn how to get Excel to perform simple regression.
D. Set up for Today’s Lab
Part II. Simple Regression Here we’re going to estimate 5 linear relationships: these are the relationships between ATTENDANCE (Y) and POP, CAPACITY, PRIORWIN, CURNTWIN, and TEAMS (each of these will serve as X).
Examine the regression results. Locate each of the following:
0
1
c. The standard error of the estimate (SXY)
0
1
f. The 95% CI’s for β 0 and β 1
g. The t-statistics for Ho(1): β 0 = 0 and Ho(2): β 1 = 0
h. The correlation coefficient (r, or “multiple r”)
i. The value of R-squared.
j. We’ll talk about the ANOVA table in class soon
Also look at the line-fit plot, and see if you can change the settings and size to make a nice graph that could be pasted into a document. Questions:
What can you say about the statistical significance of the relationship between attendance and each of the X variables?
Write out and interpret each of the regression equations you’ve estimated.