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 arein dollars.) What is the best predicted subway fare when pizza costs $3.96 per slice? Use a significance level of 0.05.YearPizza CostSubway FareCPI1960 1973 1986 1995 2002 2003 2009 2013 2015 20190.151 0.350 1.000 1.249 1.753 2.001 2.247 2.304 2.747 2.9990.152 0.349 0.998 1.351 1.503 2.003 2.250 2.551 2.752 2.74629.6 44.4 109.6 152.4 180.0 184.0 214.5 233.0 237.0 252.2The regression equation is ŷ = + (x.(Round the y-intercept to four decimal places as needed. Round the slope to three decimal places as needed.)

The regression equation have the form of y = ax +b, where a and b are the regression coefficients…

Answered: Use the pizza cost and the subway fare…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.6 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) 7.9 8.1 9.2 9.4 7.1 7.6 Weight (kg) 145 183 222 203 133 159 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y=nothing+nothingx. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 1.6 cm is nothing kg. (Round to one decimal place as needed.) Can the prediction be correct? What is wrong with predicting the weight in this case? A. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the…
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Which of the following situations would have data sets or plots that could have a regression line with a positive slope? Select all that apply. Select all that apply: The cumulative number of ships launched by ship builders as a function of the number of months since the start of the year The number of cars produced as a function of the number of days since the end of the last strike. The total shipping orders that are backlogged as a function of the number of ships in drydock. The number of backorders as a function of the number of assembly workers in a manufacturing company that makes the product.
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Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.3 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) 7.2 8.3 8.8 8.1 9.9 9.8 Weight (kg) 121 195 210 156 250 253
Pls help ASAP. Pls show all work.
Use the summary statistics to calculate the regression line for this data. Explain each coefficient. What is the total amount of weight would you expect a weightlifter with a body weight of 84kg to lift? At the 5% significance level, is there evidence that the body weight of the athlete is a useful predictor for the total amount an athlete can lift? Assume the assumptions can be verified.
Researchers studying tigers collected data on the length (in meters) and weight (in kilograms) of the animals. Is there statistically significant evidence that the length of tigers is related to their weight?
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Listed.below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 1000 mm Hg. Use a significance level of 0.05. Right Arm 102 101 94 79 79 O Left Arm 175 168 146 147 145 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y= X. (Round to one decimal place as needed.)

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