Use the pizza cost and the subway fare in the table below to find the regression equation, letting pizza cost be the predictor (x) variable. (Pizza cost is in dollars per slice, subway fare and CPI are in dollars.) What is the best predicted subway fare when pizza costs $3.96 per slice? Use a significance level of 0.05. Year Pizza Cost Subway Fare CPI 1960 1973 1986 1995 2002 2003 2009 2013 2015 2019 0.151 0.350 1.000 1.249 1.753 2.001 2.247 2.304 2.747 2.999 0.152 0.349 0.998 1.351 1.503 2.003 2.250 2.551 2.752 2.746 29.6 44.4 109.6 152.4 180.0 184.0 214.5 233.0 237.0 252.2 The regression equation is ŷ = + (x. (Round the y-intercept to four decimal places as needed. Round the slope to three decimal places as needed.)

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
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Chapter4: Equations Of Linear Functions
Section4.6: Regression And Median-fit Lines
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Use the pizza cost and the subway fare in the table below to find the regression equation, letting pizza cost be the predictor (x) variable. (Pizza cost is in dollars per slice, subway fare and CPI are
in dollars.) What is the best predicted subway fare when pizza costs $3.96 per slice? Use a significance level of 0.05.
Year
Pizza Cost
Subway Fare
CPI
1960 1973 1986 1995 2002 2003 2009 2013 2015 2019
0.151 0.350 1.000 1.249 1.753 2.001 2.247 2.304 2.747 2.999
0.152 0.349 0.998 1.351 1.503 2.003 2.250 2.551 2.752 2.746
29.6 44.4 109.6 152.4 180.0 184.0 214.5 233.0 237.0 252.2
The regression equation is ŷ = + (x.
(Round the y-intercept to four decimal places as needed. Round the slope to three decimal places as needed.)
Transcribed Image Text:Use the pizza cost and the subway fare in the table below to find the regression equation, letting pizza cost be the predictor (x) variable. (Pizza cost is in dollars per slice, subway fare and CPI are in dollars.) What is the best predicted subway fare when pizza costs $3.96 per slice? Use a significance level of 0.05. Year Pizza Cost Subway Fare CPI 1960 1973 1986 1995 2002 2003 2009 2013 2015 2019 0.151 0.350 1.000 1.249 1.753 2.001 2.247 2.304 2.747 2.999 0.152 0.349 0.998 1.351 1.503 2.003 2.250 2.551 2.752 2.746 29.6 44.4 109.6 152.4 180.0 184.0 214.5 233.0 237.0 252.2 The regression equation is ŷ = + (x. (Round the y-intercept to four decimal places as needed. Round the slope to three decimal places as needed.)
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