Univariate Statistics on Each Variable Analyze -> Descriptive Statistics -> Frequencies FREQUENCIES VARIABLES=formula p511g /STATISTICS=MEAN MEDIAN /HISTOGRAM . Frequencies Statistics newform p511g N Valid 303 303 Missing 0 0 Getting the Regression Coefficients using the CORRELATION Procedure Note that I checked the [Exclude cases listwise] button. You have to do this if you’re going to use the output to compute regression parameters, as I am here. Correlations [DataSet0] G:\MDBT\InClassDatasets\P5100 Regression example p511g vs newformula.sav Descriptive Statistics Mean Std. Deviation N newform 1179.37 92.164 303 p511g .8687 303 Correlationsb newform p511g newform Pearson Correlation 1 .518** Sig. (2-tailed) .000 p511g Pearson Correlation .518** 1 Sig. (2-tailed) .000 **. Correlation is significant at the 0.01 level (2-tailed). b. Listwise N=303 Computing the regression parameters, b and a, from the correlation output . . . The b of the regression is r * SY/SX = 0.518 * .08011 / 92.164 = 0.000450 The a of the regression is Y-bar - b*X-bar = 0.8687 0.000450*1179.37 = 0.3380 – So the prediction formula is Predicted p511g = 0.3380 + 0.000450*ACTCOMP
Univariate Statistics on Each Variable Analyze -> Descriptive Statistics -> Frequencies FREQUENCIES VARIABLES=formula p511g /STATISTICS=MEAN MEDIAN /HISTOGRAM . Frequencies Statistics newform p511g N Valid 303 303 Missing 0 0 Getting the Regression Coefficients using the CORRELATION Procedure Note that I checked the [Exclude cases listwise] button. You have to do this if you’re going to use the output to compute regression parameters, as I am here. Correlations [DataSet0] G:\MDBT\InClassDatasets\P5100 Regression example p511g vs newformula.sav Descriptive Statistics Mean Std. Deviation N newform 1179.37 92.164 303 p511g .8687 303 Correlationsb newform p511g newform Pearson Correlation 1 .518** Sig. (2-tailed) .000 p511g Pearson Correlation .518** 1 Sig. (2-tailed) .000 **. Correlation is significant at the 0.01 level (2-tailed). b. Listwise N=303 Computing the regression parameters, b and a, from the correlation output . . . The b of the regression is r * SY/SX = 0.518 * .08011 / 92.164 = 0.000450 The a of the regression is Y-bar - b*X-bar = 0.8687 – 0.000450*1179.37 = 0.3380 So the prediction formula is Predicted p511g = 0.3380 + 0.000450*ACTCOMP
p5100_lecture05_regresssion.docx