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    How To Regress 3

    Uploaded by

    Muhammad Ali Raza
    This document explains how to use Excel's Data Analysis Toolpak to perform simple linear regression on a dataset. It provides step-by-step instructions for selecting the regression tool, choosing the input and output ranges, and interpreting the key results, including the R-squared value, standard error, intercept and slope estimates, and their standard errors and p-values. An example of the regression output is also shown.

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    How To Regress 3

    Uploaded by

    Muhammad Ali Raza
    96 views3 pages
    This document explains how to use Excel's Data Analysis Toolpak to perform simple linear regression on a dataset. It provides step-by-step instructions for selecting the regression tool, choosing the input and output ranges, and interpreting the key results, including the R-squared value, standard error, intercept and slope estimates, and their standard errors and p-values. An example of the regression output is also shown.

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    How to Regress 3

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    This document explains how to use Excel's Data Analysis Toolpak to perform simple linear regression on a dataset. It provides step-by-step instructions for selecting the regression tool, choosing the input and output ranges, and interpreting the key results, including the R-squared value, standard error, intercept and slope estimates, and their standard errors and p-values. An example of the regression output is also shown.

    Copyright:

    Attribution Non-Commercial (BY-NC)
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    96 views3 pages

    How To Regress 3

    Uploaded by

    Muhammad Ali Raza
    This document explains how to use Excel's Data Analysis Toolpak to perform simple linear regression on a dataset. It provides step-by-step instructions for selecting the regression tool, choosing the input and output ranges, and interpreting the key results, including the R-squared value, standard error, intercept and slope estimates, and their standard errors and p-values. An example of the regression output is also shown.

    Copyright:

    Attribution Non-Commercial (BY-NC)
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    of 3

    MATH 124 Find Regression Coefficients (and a lot more) using the Data Analysis Toolpak bolstad_math124@bmbolstad.com http://math124sfsu.bmbolstad.

    com
    This document explains how to use Data Analysis toolpak to fit a simple linear regression. It is expected that you have already read the Transform and both the other two Find Regression Coefficient documents before you reached this point (if not now would be a good time to do so). We will again concentrate on the first dataset in the transform data file. A completely worked spreadsheet is available on the website.

    Using the data analysis toolpak 1. It was explained in the Histogram document how to install the Analysis Toolpak. If you do not have it installed do so now. 2. Now it is time to fit the regression model. Go to the Tools menu and select the Data Analysis Option. Then choose Regression from the list and click ok.

    3. Fill in the fields in the dialog box in a similar manner to the picture below. In particular the Input Y Range should be the set of cells containing the response variable (B2:B56), the Input X range should be the set of cells containing the explanatory variable and we will place the output back into the original spreadsheet at any empty location (B168). Make sure you have the Output Range radio button clicked. Click ok to finish.

    4. You should now find output that looks something like the following appears in your spreadsheet. There are certainly a lot of numbers and in class we have only (and will only) discussed a subset of them.
    SUMMARY OUTPUT Regression Statistics Multiple R 0.986553 R Square 0.973287 Adjusted R Square 0.972783 Standard Error 0.373172 Observations 55 ANOVA df Regression Residual Total 1 53 54 SS MS 268.9171 268.9171 7.380636 0.139257 276.2978 F 1931.081 Significance F 2.25E-43

    Intercept X Variable 1

    Coefficients Standard Error t Stat 2.999511 0.378339 7.928098 10.62795 0.241852 43.94407

    P-value 1.46E-10 2.25E-43

    Lower 95% 2.240659 10.14285

    Upper 95% 3.758363 11.11304

    5. The most interesting things for us are the R-square value, the Standard Error value (this corresponds with the estimate of sigma we discussed in class), the estimates

    of the intercept and slope, their respective standard errors, t statistics and corresponding P-values (these are discussed in some of the lectures). We will not discuss the remaining items. 6. The excel spreadsheet online has Data Analysis Toolpak regression output for the other datasets.

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