I am working on a dataset in which I am trying to summarise patient time in hospital by age and any health condition(e.g Cancer, Deprivatin, Depression or any). In my case it's Deprivatin. Patient time contains number of days spent by a patient in hospital. Age contains the actual age of a patient. Health condition contain score from 1 to 5 (1 is low and 5 is highest). I also made some graphs and correlated the dataset but I can't make a strong statistical decision based on the graphs because the data is very close. Below are the graphs generated from the data and first five entries of the dataset. Total number of entries in the dataset is 5000. With the given dataset and graphs, what would be the best statistical test for such data, from which a user can summarise patient time in hospital by age and health condition, and make a strong conclusion based on the result of statistical test. Thanks
Based on the feedback I got from different answers and comments, I applied MLR and got the following results.
Residuals: Min 1Q Median 3Q Max -8.4344 -3.1339 -0.5236 2.5612 22.7393 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.513551 0.199057 12.49 < 2e-15 data$dep 0.423002 0.058672 7.21 3.09e-14 data$age 0.044427 0.002494 18.91 < 2e-15 (Intercept) *** data$dep *** data$age *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 4.221 on 5130 degrees of freedom Multiple R-squared: 0.07203, Adjusted R-squared: 0.07166 F-statistic: 199.1 on 2 and 5130 DF, p-value: < 2.2e-15