The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). The equation CITY = - 3.15 + 0.818HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2790 lb, it has an engine displacement of 1.4 L, and its highway fuel consumption is 37 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? is that predicted value likely to be very accurate? E Click the icon to view the table of regression equations. The best predicted value of the city fuel consumption is (Type an integer or a decimal. Do not round.) The predicted value V likely to be a good estimate and V likely to be very accurate because
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
![Predictor (x) Variables P-Value
WT/DISP/HWY
WT/DISP
WT/HWY
DISP/HWY
WT
R? Adjusted R?
0.942
0.747
0.942
Regression Equation
CITY = 6.88 – 0.00129WT - 0.253DISP + 0.651HWY
CITY = 37.7 - 0.00161WT – 1.27DISP
CITY = 6.67 – 0.00161WT + 0.669HWY
CITY = 1.81 – 0.627DISP +0.705HWY
CITY = 42.2 - 0.00607WT
CITY = 29.4 - 2.98DISP
CITY = - 3.15 + 0.818HWY
0.000
0.932
0.000
0.000
0.000
0.000
0.000
0.000
0.719
0.936
0.935
0.928
0.712
0.697
DISP
0.658
0.640
HWY
0.924
0.920](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3c4a10a4-f78a-4b33-baf7-d4cbff942cfa%2F72181a81-c0b4-4e49-a52c-6913aee1d07a%2Fbiojgvw_processed.png&w=3840&q=75)
![The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds),
DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). The equation CITY = -3.15+0.818HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2790 lb, it
has an engine displacement of 1.4 L, and its highway fuel consumption is 37 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be
very accurate?
E Click the icon to view the table of regression equations.
The best predicted value of the city fuel consumption is
(Type an integer or a decimal. Do not round.)
The predicted value
likely to be a good estimate and
likely to be very accurate because](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3c4a10a4-f78a-4b33-baf7-d4cbff942cfa%2F72181a81-c0b4-4e49-a52c-6913aee1d07a%2F2u11vl_processed.png&w=3840&q=75)
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