Data Input for Regression Analysis

Excel Help for Data Input For Regression Analysis in Excel Multiple Regression Analysis and Forecasting Template

Data Input For Regression Analysis

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	I would like to determine if the amount of salary earned in a particular grade level is related to a person's time in the job; time with the company; their gender; and/or their race.  Would I plug all the different salary amounts into the "dependent variable" and the date entered the job; date hired with the company, their gender; and their race into separate independent variables?
	Posted by BD on 28 Mar 2006

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	Regression Using Date Values Rate this: (3/5 from 1 vote) It is correct that the salary grades should be entered as the dependent variable in this multiple regression analysis application. For the dates that the employee entered the job and was originally hired in the company, it would be better to convert this into duration such as the number of days.  While the resulting regression equation can apply the date values, the use of the equation will be more relevant using duration values.
	Excel Business Forums Administrator
	Posted by Excel Helper on 28 Mar 2006

	BD Rate this: (3/5 from 1 vote) Thank you.  Would the analysis also perform better if I converted the gender (female or male) to numeric (1 or 2) data - as well as converting race data to numeric?
	Posted by BD on 29 Mar 2006

	Convert independent variables to numeric values Rate this: (3/5 from 1 vote) Yes - it would make sense to convert boolean values such as gender to numeric values in order to create forecasts using the numeric values in the regression formula.
	Excel Business Forums Administrator
	Posted by Excel Helper on 29 Mar 2006

	BD Rate this: (3/5 from 1 vote) Thanks again.
	Posted by BD on 29 Mar 2006

Interpreting the Results Rate this: (3/5 from 1 vote) I have run a regression (for the first time) and would like a little help in the interpretation.  I have pasted the results below.  The dependent Variable is "hired or not hired" (put in as "1" for being hired and "0" if not) - as you can see.  Can you tell me which factors have been determined by the analysis to be the most important to being hired - and what the statistics mean (in easy terms)?  I appreciate any help on this to get me started in having knowledge to do this in the future. Regards BD                                           False Bitmap Equation Parameters                                   False Bitmap   R Square 0.0598  05.98% of the change in Hired-not hired can be explained by the change in the 10 Independent Variables         Adjusted R Square 0.0536  Adjusted for Sample Size bias     0.39002   Durbin-Watson Statistic Critical D-W Values: Lower (Dl)=1.57; Upper (Du)=1.78 False Bitmap Standard Error 0.4160  to +/- on result of Regression Equation     Therefore Positive Autocorrelation detected at 95% Confidence                       F - Statistic 9.6729  Therefore analysis IS Significant   1.83687   Critical F-Statistic at 95% Confidence  (Significance holds to 99.9% Level of Confidence)                                           False Bitmap  Multiple Regression Equation   False Bitmap Independent Analysis   False Bitmap Auto Correlation False Bitmap Tests for Multicolinearity between Independent Variables   Coefficients Standard Error    R Squared  Gradient  Intercept   Dl=1.65 Du=1.69 Adjusted R-Squared against other Indep  Independent R-Square Matrix Intercept 0.262 0.042           DW-Stat gender 0.158 0.030   3.62% 0.20 0.08     24.05% 100% 1% 4% 5% 14% 12% 12% 3% 4% 6% gender HS-GED -0.224 0.043   2.03% -0.20 0.42     1.23% 1% 100% 19% 24% 8% 10% 9% 21% 19% 14% HS-GED screw gun 0.053 0.039   0.17% 0.04 0.21     45.13% 4% 19% 100% 59% 27% 34% 31% 52% 25% 42% screw gun drill -0.017 0.047   0.04% 0.02 0.23     63.66% 5% 24% 59% 100% 30% 39% 34% 60% 27% 47% drill band saw 0.062 0.036   0.76% 0.07 0.20     52.52% 14% 8% 27% 30% 100% 56% 49% 27% 17% 31% band saw table saw -0.026 0.037   0.33% 0.05 0.21     54.73% 12% 10% 34% 39% 56% 100% 47% 34% 18% 34% table saw drill press -0.005 0.033   0.38% 0.05 0.21     37.38% 12% 9% 31% 34% 49% 47% 100% 31% 17% 34% drill press staple gun 0.073 0.041   0.13% 0.03 0.22     56.25% 3% 21% 52% 60% 27% 34% 31% 100% 28% 43% staple gun laborer -0.020 0.029   0.00% -0.01 0.24     28.59% 4% 19% 25% 27% 17% 18% 17% 28% 100% 25% laborer power tool -0.035 0.034   0.04% 0.02 0.23     41.66% 6% 14% 42% 47% 31% 34% 34% 43% 25% 100% power tool Hired-not hired =  0.16*gender + -0.22*HS-GED + 0.05*screw gun + -0.02*drill + 0.06*band saw + -0.03*table saw + -0.01*drill press + 0.07*staple gun + -0.02*laborer + -0.03*power tool + 0.26 (+/- 0.42) gender HS-GED screw gun drill band saw table saw drill press staple gun laborer power tool False Bitmap

Posted by BD on 15 Apr 2006

	R-squared versus gradient Rate this: (3/5 from 1 vote) The numbers within the resulting multiple regression equation didn't come through in your post.  However, to understand which independent variables have the greatest impact on the dependent, you can look at the independent analysis.  Those with the largest absolute gradient have the greatest impact and their ability to describe the changes in the dependent variable are reflected in their r-squared statistic.
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	Posted by Excel Helper on 18 Apr 2006

	BD Rate this: (3/5 from 1 vote) Thank you for the continued assistance.
	Posted by BD on 20 Apr 2006

	Data Input for Regression Analysis Rate this: (3/5 from 1 vote) Here are some steps you can follow when running your own regression analysis using Excel:Enter your data into Excel.Install Data Analysis ToolPak plugin. Open "Data Analysis" to reveal the dialog box.Enter variable data.Select output options.Analyze your results. ...Create a scatter plot.Add regression trendline. Regards,Rachel Gomez
	rachel
	Posted by RachelGomez161999 on 13 Dec 2022

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