Suppose a different family purchases a 2,500 square foot house, and also plans to undertake extensions that will increase the total size of the house by 500 square feet. Again, use the results in column (1), and assume the house has a pool, is in excellent condition, but does not have a view (which will not change after the extensions). Without performing any further calculations, what are you able to conclude about the expected impact of the extensions on this house? a. Both the estimated dollar increase and the estimated percentage increase in price would be the same as for the house described in previous question. b. The estimated percentage increase in price would be the same as for the house described in previous question, but the estimated dollar increase would be higher. C. Both the estimated dollar increase and the estimated percentage increase in price would be higher than for the house described in previous question. d. The estimated percentage increase in price would be the same as for the house described in previous question, but the estimated dollar increase would be lower.

"given a simple regression with slope b=3, s (sub y)=8, and s (sub x)= 2, and n=30. Find the standard error of the estimate."

The U.S. online grocery market is estimating sales worth approximately $29.7 billion by 2021. One of the biggest situational factors that influence the amount spent by a customer is the distance that that customer lives from its closest grocery store. Using the OLS method, the simple regression equation was estimated as: y = 40 + 3.5x. Find 1) the predicted amount a customer spends if they live 10 miles from the closest grocery store, as well as 2) the error amount. Note: the observed amount spent by a customer that lives 10 miles away is $85.50. a) $75.00, $10.50 Ob) $75.00, -$10.50 c) $73.00, $12.50 d) $73.00, -$12.50

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 two independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. With this regression model, the predicted price of a 10-year old home with 2,500 square feet of living area is __________. $205.00 $200,000.00 $205,000.00 $255,000.00

Experimenters wants to assess the association between X and Y, the scatter plot of X and Y showed a quadratic pattern. After applying the appropriate regression model, experimenters want to produce a prediciton interval for X = 20. Using StatCrunch with 95% confiendence, the result interval is 223 to 267. Provide the most appropriate interpretation of this interval.

Use the following linear regression equation to answer the questions. x1 = 2.0 + 3.6x2 – 7.8x3 + 2.1x4   a) Suppose x3 and x4 were held at fixed but arbitrary values and x2 increased by 1 unit. What would be the corresponding change in x1?b) Suppose x2 increased by 2 units. What would be the expected change in x1?c) Suppose x2 decreased by 4 units. What would be the expected change in x1?

The quadratic regression equation shown below is for a sample of n=22. a. Predict Y for X1=3.     ​(Type an integer or a​ decimal.)

In Step 2: Construct an estimated simple linear regression model how did you come up with the column X*X ?

The table lists the average monthly cost to workers for family health insurance for various years. Year, x Average Monthly Cost to Workers for Family Health Insurance $298 a) Use a graphing calculator to fit a regression line to the data. b) Predict the average monthly cost to workers for family health insurance in 2020, and compare the value with $493.3, which is obtained using the points (1,340) and (4,386). c) Find the correlation coefficient for the regression line, and determine whether the line fits the data closely. 2009, 0 2010, 1 2011, 2 340 348 2012, 3 367 2013, 4 2014, 5 386 406 a) The linear equation of the regression line that best models the data is y =x+. (Round to the nearest hundredth as needed.) b) The average monthly cost to workers for family health insurance in 2020 is predicted to be $ (Round to the nearest cent as needed.) Compare the above obtained value with $493.3 This value is $ $493.3 c) The correlation coefficient is (Round to the nearest thousandth as…