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The accompanying data represent the weights of various domestic cars and their gas mileages in the city The linear correlation coefficient between the weight of a car and its miles per gallon in the city is r= - 0979 The least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable is y = -0.0066x+ 43.3079. Complete parts (a) and (b) below. E Click the icon to view the data table. (a) What proportion of the variability in miles per gallon is explained by the relation between weight of the car and miles per gallon? The proportion of the variability in miles per gallon explained by the relation between weight of the car and miles per gallon is %. (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. % of the variance in V is by the linear model. (Round to one decimal place as needed.)

The accompanying data represent the weights of various domestic cars and their gas mileages in the city The linear correlation coefficient between the weight of a car and its miles per gallon in the city is r= - 0979 The least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable is y = -0.0066x+ 43.3079. Complete parts (a) and (b) below. E Click the icon to view the data table. (a) What proportion of the variability in miles per gallon is explained by the relation between weight of the car and miles per gallon? The proportion of the variability in miles per gallon explained by the relation between weight of the car and miles per gallon is %. (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. % of the variance in V is by the linear model. (Round to one decimal place as needed.)

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 2AGP
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(d) Perfect negative linear.corelation 60--1 (g) No linear correlation (linearly uncorrelated) Sep 5-3:11 PM Remember the stork example? Correlation does not always mean causation! Two variables may be strongly correlated because they are both variables, called lurking or confounding variables. June 15, 2020-- Research shows bald men are more llkely to die after they get the coronavirus than their hairier counterparts Since the vtus emerged in China, researchers have noticed a higher incidence of death Telegraph in England. Jn 15 2020 -age bald men reported The -ma >Coronavirus ticws Pta wwe.datymat.co.uknews/articie-854804. Coronavina-study-daims.nomt The Real Cause of Polio! Polio Rates / Ice Cream Sales 1949 0000 ke Creamm Nonth In the late 1940s, before there was a polio vaccine, public health experts In America noted that polio cases increased in step with the consumption of ice cream and soft drinks, Eliminating such treats was even recommended as part of an anti-polio diet. It turned ont that polio outbreaks were m.ost common in the hot months of xummer, when people naturally ate more ice cream, showing only an association. Pollo Cases ee Cream Saes x The accompanying data represent the weights of various domestic cars and their gas mileages in the city The linear correlation coefficient between the weight of a car and its miles per gallon in the city is r= - 0979 The least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable is y = - 0.0066x + 43 3079. Complete parts (a) and (b) below E Click the icon to view the data table. of of (a) What proportion of the variability in miles per gallon is explained by the relation between weight of the car and miles per gallon? The proportion of the variability in miles per gallon explained by the relation between weight of the car and miles per gallon is%. (Round to one decimal place as needed.) elor's (b) Interpret the coefficient of determination. % of the variance in V by the linear model. (Round to one decimal place as needed.)
Transcribed Image Text:(d) Perfect negative linear.corelation 60--1 (g) No linear correlation (linearly uncorrelated) Sep 5-3:11 PM Remember the stork example? Correlation does not always mean causation! Two variables may be strongly correlated because they are both variables, called lurking or confounding variables. June 15, 2020-- Research shows bald men are more llkely to die after they get the coronavirus than their hairier counterparts Since the vtus emerged in China, researchers have noticed a higher incidence of death Telegraph in England. Jn 15 2020 -age bald men reported The -ma >Coronavirus ticws Pta wwe.datymat.co.uknews/articie-854804. Coronavina-study-daims.nomt The Real Cause of Polio! Polio Rates / Ice Cream Sales 1949 0000 ke Creamm Nonth In the late 1940s, before there was a polio vaccine, public health experts In America noted that polio cases increased in step with the consumption of ice cream and soft drinks, Eliminating such treats was even recommended as part of an anti-polio diet. It turned ont that polio outbreaks were m.ost common in the hot months of xummer, when people naturally ate more ice cream, showing only an association. Pollo Cases ee Cream Saes x The accompanying data represent the weights of various domestic cars and their gas mileages in the city The linear correlation coefficient between the weight of a car and its miles per gallon in the city is r= - 0979 The least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable is y = - 0.0066x + 43 3079. Complete parts (a) and (b) below E Click the icon to view the data table. of of (a) What proportion of the variability in miles per gallon is explained by the relation between weight of the car and miles per gallon? The proportion of the variability in miles per gallon explained by the relation between weight of the car and miles per gallon is%. (Round to one decimal place as needed.) elor's (b) Interpret the coefficient of determination. % of the variance in V by the linear model. (Round to one decimal place as needed.)
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