coincidence or random error. In short, this test informs us whether our regression model as a whole is predicting more variance in the DV than would be expected by chance. (b) Regression Coefficients SPSS Results Coefficientsa Model Unstandardized Standardized t Sig. Coefficients Coefficients B Std. Error Beta (Constant) -.309 3.109 -.099 .922 1 temperature .212 .039 .835 5.475 .000 a. Dependent Variable: chirps_per_min Tested with just like with a t-test. Same decision rule with p-values and (α) applies: If p reject Ho; if p > fail to reject Ho. ≤ α α Ho: = 0.00 β0 Since p > .05 (p = .922 and .922 is greater than .05) fail to reject Ho and conclude that the intercept does not differ from 0.00. Ho: = 0.00 β1 Since p < .05 (p = .000 and is less than .05) we reject Ho and conclude that temp and chirps are related positively 1.7 APA Presentation with Cricket Data
coincidence or random error. In short, this test informs us whether our regression model as a whole is predicting more variance in the DV than would be expected by chance. (b) Regression Coefficients SPSS Results Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -.309 3.109 -.099 .922 temperature .212 .039 .835 5.475 .000 a. Dependent Variable: chirps_per_min Tested with just like with a t-test. Same decision rule with p-values and (α) applies: If p ≤ α reject Ho; if p > α fail to reject Ho. Ho: β0 = 0.00 Since p > .05 (p = .922 and .922 is greater than .05) fail to reject Ho and conclude that the intercept does not differ from 0.00. Ho: β1 = 0.00 Since p < .05 (p = .000 and is less than .05) we reject Ho and conclude that temp and chirps are related positively 1.7 APA Presentation with Cricket Data
EDUR8131Chat09-Notes8a.docx