Help in Binary logistic regression

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.   • What is the purpose of this analysis? Are you just trying to predict the outcome from temperature? Are you trying to estimate a specific parameter? Are you trying to build a causal model? Are you trying to describe a relationship? We need this information to be able to give you a good answer.
– Noah
Jun 13 '19 at 0:20
• Thank you some much for replying. I need to see if there is an effect of weather temperature on HbA1c level (relationship) and then to be able to predict. Jun 13 '19 at 0:23

First, a quick remark: plese stop using the buzz-word "big data" with $$n= 168615$$ I don't know if you expect to have trillions of patients in the future, but for now, this does not look like a big data problem.
Now to the actual question: statistical significance is nothing more than an arbitrary threshold put at $$\alpha=0.05$$, but there is nothing special about that $$5\%$$, so I wouldn't worry too much. What I would make sure of is that the performance of the multi-feature model is better.