What I have here is a sample template for an APA write-up for simple logistic regression so the logistic regression analysis to investigate fill in the blank with your research question was conducted the predictor variable was tested AP or e to verify that there was no violation of the assumption of the linearity of the logit the predictor variable this refill in your name of your variable in the logistic regression analysis was found to contribute to the model the unstandardized beta weight equals ww standard error equals xxx the Wold equals y YY p was less than point zero zero 1 the estimated odds ratio favorite it was either a positive or inverse relationship of nearly and fold where the exponentiated beta equals zzzt and the 95% confidence interval ranges from AAA to BBB for every one unit increase in once again you fill in the blank with your variable when i fill in the blanks for my fictitious data it now reads a logistic regression analysis to investigate if there's a relationship between age and retirement planning was conducted the predictor variable age was tested a p or e to verify there was no violation of the assumption the linearity of the logit the predictor variable age in the logistic regression analysis was found to contribute to the model the unstandardized beta wait for the constant the B equals negative two point five zero three standard error equals zero point five three for the Wold statistic equals twenty one point nine four eight and P was less than point zero zero one the unstandardized beta wait for the predictor variable beta equals zero point two nine six standard error equals zero point zero three six the Wald statistic equals sixty seven point eight nine two and the p-value is less than point zero zero one the estimated odds ratio favored in nearly 35% where the exponentiated B beta equals one point three four five with a 95% confidence interval ranging from one 2:54 21.4 43 for retirement planning for every one unit increase in age so.

In summary as stated by Issei bloke and Larson in 1998 in a simple logistic regression model we relate a covariant x1 to the binary response variable Y in the model log P divided by 1 minus P which is equal to beta naught plus beta 1 times X 1 where P is equal to the probability where y equals 1 which is our outcome of interest we're interested in testing the null hypothesis where beta 1 is equal to 0 against the alternative hypothesis where beta 1 equals beta prime where beta prime does not equal 0 that the covariant is related to the binary response variable the slope coefficient beta 1 is a change in the log odds for an increase of 1 unit in the independent variable x1 so when we speak about the null hypothesis where beta 1 equals 0 that refers to the odds ratio essentially the slope so that if there is no change with independent variable then we would reject the alternative hypothesis and accept the null hypothesis but our our alternative hypothesis is that there is a change in the odds so that's why they write it in as the beta prime not equal 0 which relates back to beta 1 not equal 0.