12. The table below shows the distance travelled in an airplane flight as it relates to the ticket costfor that flight.Miles37511251525175021502400Ticket Cost ($)56.1131164226231263a) Based on this data, is there a correlation between the distance of the flight and the ticket cost? Ifso, describe the shape and strength of this correlation.b) Find the regression equation for this data.c) Put the regression equation into the context of the problem. What do you know about thisrelationship based on the equation?d) What do the r and r? values tell you about this equation. (Make sure to give the values for theseas well as the descriptions.)e) What would you estimate the ticket price for a 750-mile flight?

Data is given for miles and ticket cost, We have to calculate regression equation and correlation.

Answered: 12. The table below shows the distance…

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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The figure for this exercise is a scatterplot of y with the following formula. Average math = 575 600 Average math SAT 575. 550 $525. 500- - = 1.11(Percent took) average math test score versus x = percent of graduating seniors who took the test for the 50 states. The regression line for these data is expressed 475 10 20 30 40 50 60 70 80 Percent taking test 90 (a) The slope of the equation is -1.11. Interpret this value in the context of how average math test changes when the percent of graduates taking the test changes. Average math test decreases 1.11 points per each 1-percent decrease in the percentage of high school graduates taking the test. Average math test decreases 575 points per each 1-percent increase in the percentage of high school graduates taking the test. Average math test decreases 575 points per each 1-percent decrease in the percentage of high school graduates taking the test. Average math test increases 1.11 points per each 1-percent increase in the percentage of…
Correlation and Regression Analysis Determine the direction of relationship between the following pairs of variables. Is it positive, negative, or zero? 1. Speed and mileage of a car 2. Extent of fatigue and performance in a speed test 3. Income and educational attainment of employees 4. Sense of humor and shoes size of persons 5. Ages of husbands and wives Given the value of 5, describe the relationship in terms of strength and direction. 1. R= 0.86 2. R= 0.35 3. R= 0.65 4. R= -0.56 5. R= 0.72 The following data show the typing speed and the reading speed of nine secretaries. Typing Speed (words per minute) Reading Speed (words per minute) Y 60 370 56 551 52 528 63 348 70 645 58 454 44 503 79 618 62 500 a. Construct a scatter plot for this data. b. Compute the correlation coefficient r. С. Test the significance of r at a = 0.05. %3D
The following table shows the amount of a chemical substance over time. Time (hours) Amount (mg) 20 42 22 41 38 29 43 17 44 16 (a) Find a linear regression equation based on the table.Round each number to two decimal places and use xx for the variable.ˆy= (b) Using your equation from (b), estimate the amount of the substance in 18 hours.________________________ milligrams
please assist, the question states to "click on the datafile logo to reference the data" but the data is listed already in the question. when clicking that link, it opens up the same information in excel so please do not reject because the data is there already.
please answer quickly
ŷ = 1.002x + 1.403 ŷ = 1.403x + 1.002 ŷ = 1.002(1.403)x ŷ = 1.403(1.002)x
If there is a positive correlation between X and Y, what can you conclude about the equation of the regression line? (Hint: it might help to picture the scatterplot, and what we learned about scatterplots in relation to both correlation and regression). Group of answer choices The intercept, a, will be negative The slope, b, will be positive The intercept, a, will be positive The slope, b, will be negative
The chart below shows the correlation between degrees Fahrenheit and degrees Celsius. Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine ºC when ºF = 47.Part d: Does this answer seem reasonable? Explain.
You run a regression of Iowa crop yield per acre on average growing season temperature, average growing season rainfall, and average growing season total precipitation. You get the following results: Data Table Variable Coefficient Std. Error t value p-value (Intercept) 103.4183 42.891 2.411 0.0161 Temp 1.9446 0.0636 30.577 0.000001 Rain 43.988 21.1693 2.078 0.038 Precipitation −46.5995 21.1768 −2.2 0.028 What problem(s) do you think this regression has? Group of answer choices a) Omitted variable of how much fertilizer was used. This will impact the Rain coefficient. b) Multi-collinearity. Rain and precipitation are very highly correlated, their estimates are unreliable. c) Huge outliers are driving the result from years in drought. d) No issues, regression is probably fine.
The table gives the amount of money (in dollars) spent on football by a major university. Letting a represent the number of years since 2005, and letting y represent the amount of money spent on football, in thousands of dollars, use the regression capabilities of a graphing calculator to find the equation of the line of best fit. Round values off to the nearest hundredth. Then, use your equation to make the following predictions. Year 2005 2006 2007 2008 2009 2010 Dollars spent on football 165,000 196,000 210,000 226,000 242,000 279,000 The equation of the line of best fit is: Hint Predict the amount of money that will be spent on football in the year 2024. +A Predict the amount of money that will be spent on football in the year 2041.
The computer output (below) shows a relationship where Y = Sale price for a home, X1 = living area, and X2 = # of bedrooms, X3 = # of bathrooms. We would like to predict Price (Y). There are three 2-variable regression relationships shown and one 4-variable multiple regression relationship shown. Regression Equation Price = 171032 + 120420 Bathrooms Model Summary S R-sq R-sq(adj) R-sq(pred) 267458 14.27% 14.17% 13.82% Regression Equation Price = 200274 + 113.68 Living Area Model Summary S R-sq R-sq(adj) R-sq(pred) 268449 13.54% 13.44% 13.10% Regression Equation Price = 338975 + 40234 Bedrooms Model Summary S R-sq R-sq(adj) R-sq(pred) 286741 1.35% 1.24% 0.92% **Multiple regression output is below: Regression Equation Price = 275641 + 84.7 Living Area - 66797 Bedrooms + 93925 Bathrooms Model Summary S R-sq R-sq(adj) R-sq(pred) 260320 18.97%…

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