The following data were collected in an experiment to study the relationship between extrusion pressure (in KPa) andwear (in mg).x150175200225250275y10.412.414.91513.911.9The least-squares quadratic model is y=-32.44571429 +0.43154286x-0.00098286x²+Using the given equation, compute the residuals. (Round the final answer to five decimal places and include a minus sign if necessy15010.417512.420014.92251525013.927511.9Residual

To compute the residuals using the given equation, we substitute each value of extrusion pressure…

Answered: The following data were collected in an…

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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What is the maximum and mininumber of the range regarding the explanatory variable for which the regression model can make valid predictions?
The following information relates to BCD Co. Month Usage Cost Jan. 600 P750 Feb. 650 775 Mar. 420 550 Apr. 500 650 May 450 570Using the least squares regression, what is the fixed cost element (to the nearest whole peso)?
Suppose Wesley is a marine biologist who is interested in the relationship between the age and the size of male Dungeness crabs. Wesley collects data on 1,000 crabs and uses the data to develop the following least-squares regression line where X is the age of the crab in months and Y is the predicted value of Y, the size of the male crab in cm. Y = 8.2052 + 0.5693X What is the value of Ý when a male crab is 21.7865 months old? Provide your answer with precision to two decimal places. Interpret the value of Ý. The value of Ý is the probability that a crab will be 21.7865 months old. the predicted number of crabs out of the 1,000 crabs collected that will be 21.7865 months old. the predicted incremental increase in size for every increase in age by 21.7865 months. the predicted size of a crab when it is 21.7865 months old.
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Obtain the values for A and B by fitting the equation y= Ах to the following data using B + x linear least squares method. Hence, calculate y when x = 100. X 5 7 10 12 y 1.33 1.67 1.75 1.82 1.85
The following Minitab display gives information regarding the relationship between the body weight of a child (in kilograms) and the metabolic rate of the child (in 100 kcal/ 24 hr). Predictor Constant Weight S = 0.517508 Coef 0.8462 0.39512 R-Sq 97.0% (a) Write out the least-squares equation. ŷ = = 0.8462 + 0.39512 X SE Coef 0.4148 0.02978 T 2.06 13.52 P (c) What is the value of the correlation coefficient r? (Use 3 decimal places.) X 0.84 0.000 (b) For each 1 kilogram increase in weight, how much does the metabolic rate of a child increase? (Use 5 decimal places.) 0.39512
A company studying the productivity of its employees on a new information system was interested in determingg if the age (X) of data entry opeertors influenced the number of completed entries made per hour (Y). The regression equation is y = 14.374 - 0.145x Suppose the acyual completed entries per hour for an operator who is 35 years old was 8. The residual is:
Experimenters wants to assess the association between X and Y, the scatter plot of X and Y showed a quadratic pattern. After applying the appropriate regression model, experimenters want to produce a prediciton interval for X = 20. Using StatCrunch with 95% confiendence, the result interval is 223 to 267. Provide the most appropriate interpretation of this interval.
The least-squares line for predicting forearm length (y) from height (x) is y = -0.2967 + 0.2738x How tall must a man be so that we would predict his forearm length to be 19.4 in.? a. 69.77 b. 70.85 c. 65.39 d. 71.94 e. 73.15
A survey is conducted on 700 Californians older than 30 years of age. The study wants to obtain inference on the relationship between years of education and yearly income in dollars. The response variable is income in dollars and the explanatory variable is years of education. A simple linear regression model is fit, and the output from R is below: Im(formula = Income ~ Education, data = CA) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 25200.25 1488.94 16.93 3.08e-10 *** Education 2905.35 112.61 25.80 1.49e-12 *** Residual standard error: 32400 on 698 degrees of freedom Multiple R-squared: 0.7602
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x₁) and newspaper advertising (x₂). The estimated regression equation was ŷ = 83.5+ 2.21x₁ + 1.80x₂. The computer solution, based on a sample of eight weeks, provided SST = 25.4 and SSR = 23.495. (a) Compute and interpret R² and R2. (Round your answers to three decimal places.) The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is variable that can be explained by the estimated multiple regression equation is (b) When television advertising was the only independent variable, R² = 0.653 and R2 = 0.595. Do you prefer the multiple regression results? Explain. Multiple regression analysis ---Select--- preferred since both R² and R2 show ---Select--- ✓percentage of the variability of y explained when both independent variables are used. . Adjusting for the number of…
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