1. This data is hospital discharge data from the state of Maryland. These data are being used to predict mortality for patients over 18 years of age with septicemia. The sample size is 105 patients. A binary logistic regression model was fit to the data with Mortality as the dependent variable with predictor variables of age (Age), sex (Sex), number of diagnoses (NDX), and number of procedures (NPR). The dependent variable is coded as 0 = did not die, and 1 = died. The independent variables are all continuous with the exception of sex which is dichotomous. The data analysis output for this study is presented in a publication type table below.

Predictors or independent variables that may predict mortality outcome

	B	SE (B)	Wald	Odds Ratio (OR)	P-value	95% CI for OR
Lower	Upper
Constant	-8.797	2.927	9.032	0.00	0.003
Age	0.079	0.034	5.328	1.08	-	1.012	1.156
Sex	-0.554	0.639	0.751	0.58	0.386	0.164	2.011
NDX NPR	0.207 -0.071	0.104 0.226	3.922 0.097	1.23 0.93	0.048 0.755	1.002 0.598	1.509 1.451
N = 105, Nagelkerke R2 = 0.256 Hosmer-Lemeshow Chi-square test = 7.118, df= 8, P-value= 0.524

1. Write a research question using the dependent and independent variables.
2. Based on the logistic regression model presented in the table, what conclusion can be made?
3. Age is not a significant predictor in the model.
4. For each additional diagnosis, there is a 2.3% less odds of death for a septic patient.
5. For each additional year of life, there is a 8% more odds of death for a septic patient.
6. For each additional procedure, a septic patient is 9.3% less likely to die.

1. Which variable is the most significant predictor in the model, provide statistical evidence?

1. Which variables are significant predictors in the model? Provide statistical evidence.