I currently have working code that uses 3rd order polynomial regression for many x's and 1 y. Then, it uses stepwise regression to find which selection of x's minimizes AIC for that y. However, I would like to add more y's and use a for loop to find the minimu...
The problem with the above output is that unlike when, for example, running a Stepwise Regression in R using the step() function on this same dataset where the final output is just the coefficients & their names for the factors selected by that Stepwise, when running ...
Note that there exists another type of regression; the Poisson regression. This type of regression is used when the goal is to estimate the relationship between a dependent variable which is in the form of count data (number of occurrences of an event of interest over a given period of time...
Results of the first stepwise multiple linear regression analysis concerning the predictors of positive affect indicated that the regression equation related to the sense of coherence was significant, [R.sup.2] = .09, F(1,343) = 31.77, p < .001. The role of sense of coherence and physical...
Our third step was to use stepwise linear regression to determine predictors of the CISS scores separately for each of the three CISS factors; a p value of less than 0.05 was considered significant. The scales from the psychological measures mentioned above were used as predictors (TAS-20; TSI...
Since I am conducting the analysis in SPSS perhaps it would be best to find a syntax for ridge regression (although I haven't done this before and interpreting the results would be new to me). If it matters, when I conducted stepwise regression, the same 2 highly correlated ...
i.e. I have tried will all possible combinations. My idea behind using all possible terms is that model first finds all significant data and after removing outliers, it gives a best fit with overall adjusted R2 greater than 0.9. Sir, please let me know that weather I also need to ...
In this category of models, the parameters are modelled separately as functions of the attributes of the gauged basins by mostly (but not exclusively) using linear models. The information is transferred to the ungauged basins through the prediction made by the fitted regression model. This category...
Proteases of human pathogens are becoming increasingly important drug targets, hence it is necessary to understand their substrate specificity and to interpret this knowledge in practically useful ways. New methods are being developed that produce large
Then re-ran my backward stepwise regression, but it gave the same error yet again. 3rd.I also tried to reiterate that each the data set being used for each regression is a regression within my lm function via in this way: >BE_fits<-lapply(seq_along(datasets),\(ds)...