Spreadsheet Exam Ch 5 and 6

Spreadsheet Exam Ch 5 and 6 - DATAGIVEN MONTHOBSERVED...

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Sample regression using multiple regression DATA GIVEN # OF  # OF UNITS JANITORIAL JANITORIAL MONTH OBSERVED PRODUCED WORKDAYS COSTS JANUARY  115   21   $3,840  FEBRUARY  109   19   $3,648  MARCH  102   23   $4,128  APRIL  76   20   $3,456  MAY  69   23   $4,320  JUNE  108   22   $4,032  JULY  77   16   $2,784  AUGUST  71   14   $2,688  SEPTEMBER  127   21   $3,840  What I am going to use here is the data analysis regression that I showed you in the solutions to the sample problem. I will do the regression using units produced as the cost driver, and then do again using janitorial workdays as the cost driver.  We will then analyze the print outs of the regressions and the line fit plots to see what it tells us of the drivers. I will also do a print out using both variables together.
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SUMMARY OUTPUT See Analysis Below Regression Statistics Multiple R 0.4343890051 R Square 0.1886938078 Adjusted R Square 0.0727929232 Standard Error 549.695970385 Observations 9 ANOVA df SS MS F Significance F Regression 1 491944.3809972 491944 1.6280618427 0.242675603 Residual 7 2115159.619003 302166 Total 8 2607104 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2554.1018425855 868.505841269 2.9408 0.0216885804 500.41186823 4607.79181694 500.41186823 4607.79181694 X Variable 1 11.4157885442 8.9468563998 1.27596 0.242675603 -9.7401650687 32.571742157 -9.7401650687 32.571742157 RESIDUAL OUTPUT Observation Predicted Y Residuals 1 3866.9175251663 -26.9175251663 2 3798.4227939012 -150.4227939012 3 3718.512274092 409.487725908 4 3421.7017719433 34.2982280567 5 3341.791252134 978.208747866 6 3787.007005357 244.992994643 7 3433.1175604874 -649.1175604874 8 3364.6228292224 -676.6228292223 9 4003.9069876965 -163.9069876965 ITEM EXPLANATION The model or formula produced by the regression only explains 18% of the variance.    Line Fit Plot You can see that the actual data points are not close to the formula line.  As such,    that is why only 18% of the variance (the distance of the actual data to the predicted    values) are far apart.  As such using units produced as the cost driver is not very good    for predicting janitorial costs in the future. t Stat For the intercept the t Stat is > 2, but this is not true of the X variable P-value The P-values of the intercept is a small number less than 5% but this is not true of the    X variable.  The t Stat and P-value are telling you the same thing, we can not be sure
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