I am doing an analysis of my big data on diabetic patients (n= 168615). The dependent variable is HbA1c blood test. I want to run binary regression on two groups of the dependent variable (HbA1c ≤ 7% coded Zero OR HbA1c > 7% coded One). The predictors are: weekly average maximum weather temperature (continuous) and the categories of the weekly average maximum weather temperature (High temperature coded 1, moderate temperature coded 2, and low temperature coded 3).
NOW: when I run the analysis for each predictor separately, I found statistically significant. But when I run the regression that predictor section contains both (continuous and categorical variables) some of them were not statistically significant. The attached photos are for what I have done: 1- Regression of weekly average maximum weather temperature (continuous) predictor alone. 2- Regression of the categories of weekly average maximum weather temperature predictor alone. 3- Regression of combined both (continuous and categorical variables).
So, please if anyone can help me in this analysis to examine if the model fit or not, which the right one I have done, and how to interpret the results. I would appreciate for any little help.