Using least-squares regression, find a straight line that best fits the data in Table 1 below. xi yi 0.2 8.2 0.4 8.4 0.6 8.5 0.8 8.6 1.0 8.8 1.2 8.7   b. Using table 1 above, find the second-degree Lagrange interpolating polynomial that goes through the first three data points, and use it to find the interpolated value at xi = 0.48.

A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1 – 2.5 x2, to predict y = the market price of a home (in $1,000s), using independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. The regression coefficient of x2 suggests this: __________. If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes

The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 760 1119 1125 850 965 785 D Temperature (°F) 70.5 90.8 90.8 74.7 77.1 69.9 What is the regression equation? (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.)

The data show the bug chirps per minute at different temperatures. Find the regression​ equation, letting the first variable be the independent​ (x) variable. Find the best-predicted temperature for a time when a bug is chirping at the rate of 3000chirps per minute. Use a significance level of 0.05. What is wrong with this predicted​ value?   Chirps in 1 min 1004 952 1237 1116 1177 1249   Temperature ​(°​F) 83.1 76 95.1 87.5 92.1 88.4 What is the regression​ equation?   (^ over y)=_____+_____x ​(Round the​ x-coefficient to four decimal places as needed. Round the constant to two decimal places as​ needed.)   What is the best-predicted temperature for a time when a bug is chirping at the rate of 3000chirps per​ minute?   The best-predicted temperature when a bug is chirping at 3000 chirps per minute is ____°F. ​(Round to one decimal place as​ needed.)   What is wrong with this predicted​ value? Choose the correct answer below.   A. It is…