Please answer all problems fully and provide r studio code. If you are interested in answering these problems, please contact me on here so I can provide you with the data set and the data set directory.

Problem 1: Import your dataset into R. Estimate a model where height at age 33 explains income at age 33. Display your results table. Explain Beta hat one and Beta had none.

Problem 2: Create a scatterplot of height and income at age 33. Identify outliers.

Problem 3: Create a scatterpolot of height and income at age 33 but excluse observations with wages per hour more than 400 British pounds and height less than 40 inches. Describe the difference from the earlier plot. Which plot seems the more reasonable basis for statistical analysis? Why?

Problem 4: Re-estimate the bivariate OLS model from problem 1, but excluse four outliers with very high wages and outliers with height below 40 inches. Briefly compare the results to earlier results.

Problem 5: What happens when the sample size is smaller? To answer the question, re-estimate the bivariate OLS model from above (that excludes outliers), but limit the analysis to the first 800 observations. Which changes more from the results with the full sample: the estimated coefficient on height or the estimated standard error of the coefficient on height?

Problem 6: Name a variable, other than height at age 33, that explains income at age 33? Could it be correlated with height at age 33? What do you expect the sign of this variables Betea hat not to be? Why?