Question: Description of model (which is on the left of the image below). Report the results of the model. This includes significance statistics (5 marks), estimate sizes and errors (5 marks) and most importantly a narrative explanation for each significant predictor (25 marks)

Context: How variables affect life expectancy, variables are listed in the model - log(gdp_capita), cause_of_death_communicable, dpt_im, log(hospital_beds), diabetes, overweight

I have already done the code so I've attached a picture of the final model below. I am struggling with answering the question and the description of this, specifically the significance statistics, estimate sizes and errors, inc narrative explanation which i believe is the description of each predictor relationships in words and give an explanation/hypothesis for why this relationship exists, but I don't really know how to explain it with the right terminology. (on the left is linear model diagnostic plots but not sure it needs to be included in this question, I just put it there as an extra)

> model summary (model) call: 1m(formula = life_exp ~ log(gdp-capita) + cause_of_death_communicable + dpt_im + log(hospital_beds) + diabetes + overweight, data = colldata, siduals vs Fitt Normal Q-Q na. action = na. omit) 142 800000 0 6-$20 Residuals : Min 1Q Median 3Q Max Standardized residuals -11. 5358 -1. 5740 0. 0357 1. 7778 Residuals 6. 9381 -4 -2 0 2 -10 -5 0 .....-053 coefficients : Estimate Std. Error t value pr (>|t[) 50 050 (Intercept) 54. 10602 25635 16. 616 plot (model) -4 -2 0 Hit to see next plot: 0809 470 O Hit to see next plot: 0.5 O Hit to see next plot: 100 Cook's distance 0.0 Hit to see next plot: par (mfrow=c (2, 2) ) # Change the panel layout to 2 x 2 65 60 65 70 75 80 0.00 0.05 0.10 0.15 0.20 plot (model) par (mfrow=c(1,1)) # Change back to 1 x 1 Fitted values Leverage