1. Summarize the data related to life expectancy (consider only life expectancy and not the other variables) in a way that will help the audience see a basic picture of life expectancy in the world. Use standard statistical devices, including graphs. Make this a concise readable, summary of the data that will help your audience understand it. You can use in Excel. 2. To find the association between life expectancy and the independent variables, perform the following steps. a) Consider all independent variables provided, use regression (in Excel) methods specify and estimate an equation that adequately predicts life expectancy in the world. Present the relevant statistical results in a neat, understandable way that will enable your audience to learn about life expectancy in the countries from your model. Interpret the regression coefficients and the R. Specify the significant variables in predicting the car price. Note: Your estimate equation should be = bo + b x + b x2 + b3 x3 + ... b) Consider only numerical independent variables provided, use regression (in Excel) methods specify and estimate an equation that adequately predicts life expectancy in the world. Present the relevant statistical results in a neat, understandable way that will enable your audience to learn about life expectancy in the world from your model. Interpret the regression coefficients and the R. Specify the significant variables in predicting the car price. Note: Your estimate equation should be = bo + b x + b x2 + b3 x3 + *** c) Compare the results from part a and b, can we include in some or all the qualitative variables from part a? Why? Compare the model you built in part (a) with the model you built in part (b). What is the difference between the two models? d) Introduce a proper cross-product term that can identify the interaction between the independent (quantitative) variables which you get the results from part c. Check the VIF value or covariance matrix. e) How does Infant death and Adult mortality rates affect life expectancy? f) Does life expectancy has positive or negative correlation with drink alcohol, exercise (BMI), Income composition, or schooling? g) Do densely population countries tend to have lower life expectancy? h) What are the main results of this study? i) What can you do to improve this model (adding/removing any independent variable or interaction term, different analysis methods,...)?