Let's look at some real data.(a) Is it reasonable to think the boiling point of water would serve to guage altitude above sea level? Use Forbes' data from Problem 7.3.2. (b) Why does the coefficient of determination not have much meaning in the Edwards-Eberhardt study ? (c) Why have we not asked about Wilm's study in Problem 7.2.10?Problem 7.3.2:A mountain climber can determine altitude above sea level by using a barometer to measure atmospheric pressure-lower pressure meaning higher altitude. However, the barometers available in the midnineteenth century were very delicate and difficult to carryon expeditions into mountaineous terrain. The Scottish physicistJames D. Forbes hoped to get around this difficulty by determining barometric pressure from the boiling point of water. Forbes made measurements at 17 locations in the Alps and in Scotland. Here is the data as published in his 1857 paper. See Weisberg for a very complete analysis of this data. Boiling point (BP) is measured in degrees Fahrenheit and pressure (Pr') in inches of mercury,(a) If Forbes wants to use a regression model, which variable is X and which Y? Explain.(b) Without doing any calculations, should b be positive or negative?(c) Give the estimated regression line for the model.(d) Does this data support use of a linear model?(e) How fast does the atmospheric pressure decrease as you climb?Problem 7.2.10:H.G. Wilm wanted to predict the April to July water yield (in inches) in the Snake River watershed in Wyoming from water content of snow on April 1. Here's Wilm's data (after [Weisberg]) for the years 1919 to 1935:(a) Evidently X refers to what? And Y? Explain. (b) Give the estimated regression line for this data. (c) The equation in part (b) is suspicious-it predicts three-quarters of an inch of water yield when there was NO SNOW to yield that water! In other words, we know a = 0, so the model is For this special case of the model, the least squares estimate for β is Give the estimated regression line for Wilm's data with this more realistic version of the model. (d) Plot a scatter diagram for the data with two regression lines, the ones you gave in parts (b) and (c).