---### Regression Analysis: Predicting Subway Fare Based on Pizza Cost#### Objective:Determine the regression equation using the pizza cost and the subway fare from the table below. Here, the pizza cost is the predictor (x) variable. Both pizza cost (measured in dollars per slice) and subway fare (measured in dollars) are given. We aim to predict the subway fare when the pizza cost is $4.01 per slice.#### Data Table:| Year | Pizza Cost | Subway Fare | CPI ||------|------------|-------------|-------|| 1960 | 0.152 | 0.148 | 29.6 || 1973 | 0.354 | 0.352 | 44.4 || 1986 | 0.996 | 0.999 | 109.6 || 1995 | 1.246 | 1.350 | 152.4 || 2002 | 1.754 | 1.502 | 180.0 || 2009 | 1.999 | 1.998 | 184.0 || 2013 | 2.247 | 2.247 | 214.5 || 2015 | 2.299 | 2.547 | 233.0 || 2019 | 3.003 | 2.748 | 252.2 |#### Instructions:1. **Find the regression equation**: Use the given data to calculate the regression equation, where `y` represents the subway fare, and `x` represents the pizza cost.2. **Equation format**: The regression equation will be in the form: \[ \hat{y} = \beta_0 + (\beta_1)(x) \] - Round the y-intercept ($\beta_0$) to four decimal places. - Round the slope ($\beta_1$) to three decimal places.3. **Prediction**: Use the regression equation to predict the subway fare when the pizza cost is $4.01 per slice.#### Solution Setup:- **Regression Equation Calculation Placeholder**: \[ \hat{y} = \square + (\square) x \]#### Example: If after calculations the regression

The provided information is Pizza cost (x) Subway Fare (y) xy x2 0.152 0.148 0.022496 0.023104…

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 the first variable be the predictor (x) variable. Find the best predicted Nobel Laureate rate for a country that has 78.5Internet users per 100 people. How does it compare to the country's actual Nobel Laureate rate of 1.1 per 10 million people? Internet Users Per 100 Nobel Laureates80.3 5.679.6 24.277.3 8.644.6 0.183 6.138.3 0.190.1 25.189.1 7.680.1 8.983.3 12.753.2 1.976.6 12.756.8 3.379.5 1.591.5 11.493.7 25.465.1 3.157.8 1.967.9 1.794.3 31.785.3 31.387.7 1977.7 10.8
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 $3.98 per slice? Year Pizza Cost Subway Fare CPI 1960 1973 1986 1995 2002 2003 2009 2013 2015 2019 0.152 0.353 1.000 1.251 1.753 2.003 2.254 2.300 2.747 2.997 0.151 0.346 1.000 1.347 1.497 2.004 2.253 2.551 2.747 2.750 29.6 44.4 109.6 152.4 180.0 184.0 214.5 233.0 237.0 252.2 The regression equation is y=+x. (Round the y-intercept to four decimal places as needed. Round the slope to three decimal places as needed.) The best predicted subway fare when pizza costs $3.98 per slice is $ (Round to the nearest cent as needed.)
Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.3 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.9 278.3 253.2 258.7 278.7 257.8 274.2 262.2 Height 1784.8 1771.0 1675.6 1645.9 1858.7 1710.1 1789.2 1737.4 the regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 273.3 mm is enter your response here mm. (Round to the nearest integer as needed.)
Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.7 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.3 277.8 252.8 258.7 279.0 258.4 274.1 261.7 Height 1785.0 1771.0 1675.7 1645.7 1859.3 1710.2 1789.2 1737.0 The regression equation is ŷ = + (x. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.7 mm is (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? O A. The result is close to the actual height of 1776 mm. O B. The result is exactly the same as the actual height of 1776 mm. O C. The result is very different from the actual height of 1776 mm. O D. The result does not make sense given the context of the data. C mm.
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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 $4.08 per slice? Year Pizza Cost Subway Fare CPI 1960 1973 1986 1995 2002 2003 2009 2013 2015 2019 0.152 0.354 0.997 1.254 1.748 2.002 2.246 2.297 2.749 3.003 0.150 0.350 0.998 1.348 1.498 1.997 2.252 2.552 2.752 2.752 29.6 44.4 109.6 152.4 180.0 184.0 214.5 233.0 237.0 252.2 The regression equation is y=+x. (Round the y-intercept to four decimal places as needed. Round the slope to three decimal places as needed.)
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Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $105 million. Use a significance level of a = 0.05. Budget ($)in Millions Gross ($) in Millions 41 23 114 75 78 47 120 64 10 59 127 22 12 150 2 0 127 18 111 65 113 112 102 94 64 98 211 41 22 288 57 Click the icon to view the critical values of the Pearson correlation coefficient r. ..... x. (Round to one decimal place as needed.) The regression equation is = + The best predicted gross for a movie with a $105 million budget is $ million. (Round to one decimal place as needed.)
The table below shows the average weekly wages (in dollars) for state government employees and federal government employees for 10 years. Construct and interpret a 90% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $825. The equation of the regression line is y = 1.597x- 169.203. Wages (state), x Wages (federal), y 742 775 783 802 842 890 912 939 949 954 1,002 1,039 1,097 1,145 1,199 1,242 1,271 1,299 1,335 1,398 Construct and interpret a 90% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $825. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) O A. There is a 90% chance that the predicted average weekly wages of federal government employees is between S and S given a state average weekly wage of $825. O B. We can be 90%…

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