To model the price of a flat screen TV, a junior analyst at your firm compiled the retail prices and diagonal measurements of screens. Ascatter plot of the date appears below along with a linear estimate of the relationship: Coefficients Standard Error t Stat P-value Intercept -3683.24 330.0987 11.158 2.45E-07 Diagonal Size 114.7429 6.789886 16.89909 3.23E-09 Diagonal (in inches) vs. TV Price 5000 4000 3000 2000 1000 35 40 45 50 55 60 65 70 75 Your analyst confides, "The boss asked me to estimate the selling price for a 24 inch TV but I'm worried about my estimate. According to my calculations, we would hove to pay consumers almost $1000 to take them off our hands. I double checked my date and the R' of the regression was over 0.95 so I feel confident about the inputs but I'm confused about how to interpret the results they produce." Question 1 Explain what is wrong with the analyst's work. What could you do to improve the estimation of the | relationship between size and price? Be specific Question 2 Explain why such estimates of the relationship between price and size likely to be unstable over a number of years