- Suppose A Researcher Is Trying To Understand What Makes Living In A Particular City Desirable She Uses A List From A Po 1 (49.16 KiB) Viewed 16 times
- Suppose A Researcher Is Trying To Understand What Makes Living In A Particular City Desirable She Uses A List From A Po 2 (52.34 KiB) Viewed 16 times
- Suppose A Researcher Is Trying To Understand What Makes Living In A Particular City Desirable She Uses A List From A Po 3 (44.96 KiB) Viewed 16 times
- Suppose A Researcher Is Trying To Understand What Makes Living In A Particular City Desirable She Uses A List From A Po 4 (44.96 KiB) Viewed 16 times
Zero-Order Correlations: Median Home Price and # of Fast-Food Restaurants within 5 Miles of Downtown City Desirability Median Home # of Fast-Food Restaurants within 5 Rating Price Miles of Downtown City Desirability Rating 1.000 0.509 -0.293 Median Home Price 1.000 -0.060 #of Fast-Food Restaurants within 5 1.800 Miles of Downtown The researcher computes the partial correlation of -0.306 between the city desirability rating and the number of fast-food restaurants within 5 miles of downtown controlling for the median home price. 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 be at least with respect to consideration of the median home price. Using the provided correlations and the given partial correlation, the coefficient of multiple determination (R) for the multiple regression equation predicting the dty desirability rating from the other two variables is The researcher can interpret this value to mean that the variance in the is explained by ol The independent variable median home price predicts of the variance in the dependent variable by itself. This suggests that also including number of fast food restaurants within 5 miles of downtown in the regression equation how well the equation can predict the dependent variable The multiple correlation coefficient (R) for the multiple regression equation predicting the city desirability rating from the other two variables is
Suppose a researcher is trying to understand what makes living in a particular dy 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 median home price and the number of fast-food restaurants within 5 miles of downtown. She obtains the values for median home price and number oftast food restaurants within 5 miles of downtown for each of the so cities and calculates the following values Zero Order Correlations: Median Home Price and of Fast Food Restaurants within 5 Miles of Downtown City Desirability Median Home of Fast Food Restaurants within 5 Rating Price Miles of Downtown City Desirability Rating 1.000 0.500 0.293 Median Home Price 1.000 0.000 + of Fast Food Restaurants within 5 1.000 Miles of Downtown The researcher computes the partial correlation of 0.306 between the city desirability rating and the number of fast food restaurants within 5 miles of downtown controlling for the median home. Because this value is not much different from the recorder correlation between the city desirability rating and the the researcher can consider the relationship between these two variables to be at least with respect to consideration of the median home price Using the provided correlations and the given partial correlation the coefficient of multiple determination (R) for the multiple regression equation predicting the dy desirability rating from the other two variables The researcher can interpret the value to mean that the variance in the is explained by The Independent variable median home price predicts of the variance in the dependent variable by itself. This sugest that also including number of fast food restaurants within 5 miles of downtown in the regression equation how well the equation can predict the dependent variable The multiple correlation coeffident (R) for the multiple regression equation predicting the city desiraboty rating from the other two variables is