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Regression Computer Project - Statistics | MATH 1040, Study Guides, Projects, Research of Mathematics

Salt Lake Community CollegeMathematics

Material Type: Project; Class: Intro to Statistics (QL); Subject: Mathematics; University: Salt Lake Community College; Term: Summer 1994;

Typology: Study Guides, Projects, Research

Pre 2010

Uploaded on 08/13/2009

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Math 1040 - Statistics Name ___________________
Regression Computer Project #4, v2.1
OBJECTIVE:
The object of this computer project is to predict future world records for the mile run using a linear
regression model. Data are mile records from 1911 to 1994.
OUTLINE: (steps to accomplish the outline are given in the procedure below).
A. Run the spreadsheet and enter the data.
B. Do the regression analysis.
C. Identify the correlation coefficient and the regression equation.
D. Make a scatter plot with regression line.
E. Use the regression equation to make predictions.
Instructions for the following procedure are for Excel. However, similar operations are supported by other
spreadsheets.
PROCEDURE:
A. Run Excel and enter the following years into column A and the following mile record times in
seconds into column B. There are 26 data entries in each column, some of which may appear on the
following page. Note: if you also display this lab on a word processor, you can copy and paste the
data directly to Excel.
1911 255.4
1913 254.6
1915 252.6
1923 250.4
1931 249.2
1933 247.6
1934 246.8
1937 246.4
1942 244.6
1943 242.6
1944 241.6
1945 241.4
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Math 1040 - Statistics Name ___________________ Regression Computer Project #4, v2. OBJECTIVE: The object of this computer project is to predict future world records for the mile run using a linear regression model. Data are mile records from 1911 to 1994. OUTLINE: (steps to accomplish the outline are given in the procedure below). A. Run the spreadsheet and enter the data. B. Do the regression analysis. C. Identify the correlation coefficient and the regression equation. D. Make a scatter plot with regression line. E. Use the regression equation to make predictions. Instructions for the following procedure are for Excel. However, similar operations are supported by other spreadsheets. PROCEDURE: A. Run Excel and enter the following years into column A and the following mile record times in seconds into column B. There are 26 data entries in each column, some of which may appear on the following page. Note: if you also display this lab on a word processor, you can copy and paste the data directly to Excel. 1911 255. 1913 254. 1915 252. 1923 250. 1931 249. 1933 247. 1934 246. 1937 246. 1942 244. 1943 242. 1944 241. 1945 241.

Data Observations:

  1. How many times during the Second World War was world mile record broken? List the years.
  2. What do you notice about the data during the 8-year period immediately following the war?
  3. What do you notice about the data beginning with the year 1981?

B. To create the linear regression, select ‘data analysis’ from the ‘tools’ menu. (If there is no 'Data Analysis' entry in the 'Tools' menu, select 'Add Ins' and check the 'Analysis Tool Pak' box.) Scroll down this list and select ‘Regression’. Click OK. In the regression window, click the appropriate field to be sure the cursor is blinking and enter the following data into the fields. Input Y Range: $B$1:$B$26 (or drag the mouse down column B data) Input X Range: $A$1:$A$26 (or drag the mouse down column A data). Check (x) ‘Output Range’ $D$1 (or click on cell D1) Click OK C. The correlation coefficient 'Multiple R' is located under 'Regression Statistics'. Enter the value below.

  1. Use the regression line to predict the world record in the year 2500. 2500 _______.
  2. According to this model, in what year will the runner arrive at the one-mile finish line at exactly the same instant that he or she started? (The runner would be moving faster than the speed of light!) Hint: the runners time is the y value so set y = 0 in the equation and solve for x. Year _______ CONCLUSION: Briefly explain how a regression equation is useful to extract information from data.

Print Sheet1 - the data, statistics, and graph - and attach to the lab to hand in.