Answers provided. I've already ansered, just double checking work. 3. The coefficient of multiple determinationSuppose a researcher is trying to understand what makes living in a particular city desirable. She uses a list from a popular magazine of the currenttop 50 desirable cities to live in. Each city was given a desirability score by the magazine's viewers. The researcher is curious about how well she canpredict those scores using only two independent variables: the number of miles to the closest airport and the median home price. She obtains thevalues for number of miles to the closest airport and median home price for each of the 50 cities and calculates the following values:Zero-Order Correlations: # of Miles to the Closest Airport and Median Home PriceCity Desirability Rating # of Miles to the Closest AirportMedian Home PriceCity Desirability Rating1.000-0.4470.604# of Miles to the Closest Airport1.000-0.066A. interveningB. spuriousC. directD. interactingMedian Home Price1.000A. # of miles to closest airportB. median home priceC. City desirability ratingThe researcher computes the partial correlation of 0.644 between the city desirability rating and the median home price controlling for the number ofmiles to the closest airport. Because this value is not much different from the zero-order correlation between the city desirability rating and the, the researcher can consider the relationship between these two variables to beat leastA. 16%В. 36.5%C. 53.2%'D. 81%with respect to consideration of the number of miles to the closest airport.A.) 0.730 B.) 0.644 C.) 0.532 D.) 0.142Using the provided correlations and the given partial correlationthe coefficient of multiple determination (R) for the multiple regression equationpredicting the city desirability rating from the other two variables is. The researcher can interpret this value to mean thatofthe variance in theis explained byA. city desirabilityB. median home priceC. # of miles to the closestairportA. the median home priceB. # of miles to closest airportC. both # of miles to closest airport and median home priceD. both the city desirability rating anf the median home priceE. both the city desirability rating and the # of miles from the closest airportA. 21%В. -44.7%C. 20.0%The independent variable number of miles to the closest airport predic: 60.4%of the variance in the dependent variable by itself. This suggeststhat also including median home price in the regression equationhow well the equation can predict the dependent variable.A.f improve B.) does not improveThe multiple correlation coefficient (R) for the multiple regression equation predicting the city desirability rating from the other two variables isA. 0.532,В. 0.644C. 0.142D. 0.729

uppose a researcher is trying to understand what makes living in a particular city desirable. She uses a list from a popular magazine of the current top 50 desirable cities to live in. Each city was given a desirability score by the magazine's viewers. The researcher is curious about how well she can predict those scores using only two independent variables: the number of miles to the closest airport and the median home price. She obtains the values for number of miles to the closest airport and median home price for each of the 50 cities and calculates the following values: Zero-Order Correlations: # of Miles to the Closest Airport and Median Home Price City Desirability Rating # of Miles to the Closest Airport Median Home Price City Desirability Rating 1.0001.000 −0.447−0.447 0.6040.604 # of Miles to the Closest Airport 1.0001.0The Excel sheet is: The formulas are: