Fit the following data using Quadratic Regression2.53.56.7.51012.51517.520f(x)76.55.94.84.542.92.52.11.3

Given data is as follows x 2.5 3.5 5 6 7.5 10 12.5 15 17.5 20 f(x) 7 6.5 5.9 4.8 4.5 4 2.9 2.5…

Answered: Fit the following data using Quadratic…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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Sam had 3 caffeinated drinks on april 24 Use linear regression to predict the number non-caffeinated drink he had on April 24 and calculate the error of estimate for your prediction.
consider the following points (1.3,2.4) (2.5,2.4) (3.3,2) (4.2,1.8) (5.1,1.6) find the equation of the regression line (round the values 2 decimal placed.
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The following multiple regression printout can be used to predict the price (Price) of a used car given how many miles it has on it (Mileage), the size of the engine (Liter), and whether it has leather interior (Leather), where Leather = 1 for cars that have leather interior and 0 otherwise. %3D Regression Analysis: Price Versus Mileage, Liter, Leather Coefficients Term Coef SE Coef T-Value P-Value Constant 6,959 511 13.62 0.000 Mileage -0.0848 0.0178 -4.76 0.000 Liter 3,795 126 30.12 0.000 Leather 1,028 311 3.31 0.001 Regression Equation Price = 6,959 - 0.0848 Mileage + 3, 795 Liter + 1,028 Leather (a) Given this Minitab printout, is the response variable in this multiple regression equation a categorical or a numerical variable? O numerical O categorical (b) Determine whether the following statement is true or false. Given this Minitab printout, the regression coefficient for miles on the car (Mileage) is statistically significant at a = 0.05. O True O False (c) Determine whether the…
Use quadratic regression to find a model for the following data set. (Round the terms to four decimal places.) f(x) = 1 6. 8. f(x) 2.2 9.8 26.3 35.1 59.9 3.
Use cubic regression to model the following data set. (Round your regression parameters to two decimal places.) x 1 3 4 6 7 8 10 y 1.4 3.7 7.4 4.6 8.8 9.1 12.4
The following multiple regression printout can be used to predict a person's average annual salary given his or her years of employment and number of years of education beyond high school. Regression Analysis: Salary Versus YrsEm, Educ Coefficients Term Constant YrsEm Educ Coef 23,171 678 1,918 SE Coef 1,771 142 376 T-Value 13.08 4.77 5.10 P-Value 0.000 0.000 0.000 (a) Is the regression coefficient of education (Educ) statistically significant? (Use a = 0.05.) Given this output, the regression coefficient for education ---Select--- statistically significant. (b) Does the variable education belong in the model? Given this output, the variable education ---Select--- --Select--- belong in the model. (c) Given this output, which of the following is the correct interpretation for Education in this model? O An individual with 4 years of education beyond high school can expect an annual salary of $7,672. O The estimated change in the average value of annual salary of getting an additional…
The following data represent the commute time (in minutes) x and a score on a well-being survey y. The equation of the least-squares regression line is y = - 0.0423x + 69.1961 and the standard error of the estimate is 0.5262. Complete parts (a) through (e) below. 15 25 35 45 60 82 115 D 69.0 68.3 66.8 66.2 66.5 64.2 y 67.4 (a) Predict the mean well-being index composite score of all individuals whose commute time is 30 minutes. y = (Round to two decimal places as needed.) (b) Construct a 90% confidence interval for the mean well-being index composite score of all individuals whose commute time is 30 minutes. Lower Bound (Round to two decimal places as needed.) Upper Bound |(Round to two decimal places as needed.) (c) Predict the well-being index composite score of Jane, whose commute time is 30 minutes. y =(Round to two decimal places as needed.)
Question. Using the following regression result, make your answers to the questions. Call: Im(formula - wage - educ, data - data) Residuals: 10 Median -5.1579 -2.5066 0.3816 2.4539 4.4737 Min 30 Max Coefficients: Estimate Std. Error t value Pr(>1tl) (Intercept) 9.0526 educ 5.1420 1.761 0.11635 2.1842 0.5994 3.644 0.00655 ** --- Signif. codes: 0 ***** 0.001 *** 0.01 ** 0.05 . 0.1' 1 Residual standard error: 3.305 on 8 degrees of freedom Multiple R-squared: 0.6241, F-statistic: 13.28 on 1 and 8 DF, p-value: 0.006549 Adjusted R-squared: 0.5771 1) Using the estimate and standard error of educ variable, perform the following hypothesis test. Ho:Beduc = 0 H1: Beduc # 0 2) Interpret the above hypothesis test result.
Q3. Use multiple regression to find the line of best fit through the following data. What does this tell you? y 420 520 860 740 510 630 650 760 590 680 a 1 3 4 5 6. 7 8. 9. 10 7 3 1 2 6. 6. 23 15 64 52 13 40 36 20 19 24 d. 109 121 160 155 175 90 132 145 97 107
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
2

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Fit the following data using Quadratic Regression 2.5 7.5 3.5 6.5 6 10 12.5 15 17.5 20 f(x) 7 5.9 4.8 4.5 2.9 2.5 2.1 1.3