Using the sample data from the accompanying table, complete parts (a) and (b).C...(a) Explain why it does not make sense to construct confidence or prediction intervals based on the least-squares regression equation.Choose the correct answer below.OA. It does not make sense to construct confidence or prediction intervals based on the least-squares regression equation becausethere is a linear relationship between sugar content and calories in high-protein and moderate protein energy bars.OB. It does not make sense to construct confidence or prediction intervals based on the least-squares regression equation becausethere is no linear relationship between sugar content and calories in high-protein and moderate protein energy bars.OC. It does not make sense to construct confidence or prediction intervals based on the least-squares regression equation because theresiduals are not normally distributed.(b) Construct a 95% confidence interval for the mean sugar content of energy bars.The 95% confidence interval for the mean sugar content of energy bars islower bound:upper bound:.(Round to one decimal place as needed.) Data TableCalories, x180200210220220230240Sugar, y111814200282Question 6 of 10Calories, x270320110180200220230PrintSugar, y>Done2N222222010122424It can be shown that there is no linear relationship between sugar content andcalories in high-protein and moderate protein energy bars.Full data setThis qui:This que

The data for the calories and sugar content of energy bars are given.

Answered: Using the sample data from the…

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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A real estate builder wishes to determine how house size (House) is influenced by family income (Income) and family size (Size). House size is measured in meter square and income is measured in IDR millions. The builder randomly selected 50 families and ran the multiple regression. Partial Microsoft Excel output is provided below: Which of the independent variables in the model are significant at the 5% level? Formulate the hypothesis and explain the answer. How far can you rely upon this model? Or, what is the percentage variation in House explained by the model? What is the predicted house size (in hundreds of square feet) for an individual earning an annual income of IDR 400 million and having a family size of 4?
I need help finding the answers for A,B,and C
The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. With the aim of predicting the used selling price from the number of miles driven, we might examine the least-squares regression line, y=41.57 – 0.49.x. This line is shown in the scatter plot in Figure 1. Used selling price, Mileage, x (in thousands) (in thousands of dollars) 25.9 26.1 28.1 26.2 40- 21.1 31.4 24.0 27.5 35 27.2 30.9 38.7 21.4 30. 34.6 25.5 37.2 23.5 15.6 34.0 25- 23.8 28.0 20.9 30.9 20. 23.1 32.7 28.0 30.3 40 29.2 28.1 Figure 1 24.0 29.6 23.0 31.5 Send data to Excel
Moisture content in percent by volume (x) and conductivity in mS/m (y) were measured for 50 soil specimens. The means and standard deviations were = 8.1, sx=1.2, y = 30.4, sy=1.9. The correlation between conductivity and moisture was computed to be r= 0.810. Find the equation of the least-squares line for predicting soil conductivity from moisture content. (Round the final answers to three decimal places.) y = X
The table shows a part of an output of a linear regression model predicting the average fare on different flight routes. Data Table Regression Table Coefficient Constant 95.80976147 COUPON −9.61654124 DISTANCE 0.080733811 PAX −0.000167343 What is the difference in prediction of the following two routes? Route A that is 3,000 miles, with COUPON=1.5 and PAX=6,000 Route B that is 3,000 miles, with COUPON=1.2 and PAX=6,000.
1.)fill in the blanks. Based on the physician's study, the predictor variable, X is_____and the response variable, Y, is________. 2.) As described in the article, the relation between age and peak heart is a______. - positive relation - negative relation - no relation 3.) Provide the regression line, ŷ=a+bx. Show steps/equations used to get answer. 4.) Suppose a 40 year old person is randomely selected. Use your Model to predict their peak heart rate. 5.) Based on your model, as a person ages one year, how much would you expect peak heart rate to change?
The data from the table below gives a regression that is a) reliable. b) unreliable. c) unable to determine the reliability.
Which of these statements is true regarding residuals in regression DA. The sum of the residuals always adds to 1 B. This is a trick question. None of these is true. It depends on the residuals (and the regression line) what they add up to or average. C. The mean of the residuals is always larger than zero D. The mean of the residuals is always equal to zero E. The mean of the residuals is always smaller than zero Which statement about experiments is most accurate? A. Participants are randomly assigned to conditions B. Experiments potentially allow to establish causality (in contrast to observational studies) C. All potential confounds are controlled by randomization D. All of the above E. Participants pick which treatment (independent variable or IV) they receive F. Both B and E
Stoaches are fictional creatures that nest in truffula forests. A researcher wants to know whether there is a relationship between a stoach’s wingspan (?W, the predictor) and its nest height (?H, the response). A sample of 85 stoaches is observed, and for each, the wing-span (in cm) and the nest height (in m) are recorded. The observed data meet the assumptions for a linear regression, so the researcher fits the regression model and obtains a regression equation ℎ̂=−4.273+0.834?,h^=−4.273+0.834w, with standard error for the coefficient of ?w equal to 0.242. Determine the ?p-value from a test for a statistically significant linear dependence of nest height on wing-span. (Give your answer to 4 decimal places.
a. Compute the correlation between price and mileage. (Round answers to 4 decimal points) Correlation: b. Which of the following is NOT an assumption made on the standard regression assumptions? Which of the following is true about the regression line multiple choice 2 The regression line minimizes the MSE of forecast errors. The regression line is the line with the largest prediction accuracy. The regression line minimizes the sum of the squares of the residuals. Regression may only be performed if the standard regression assumptions hold. All of the above. multiple choice 1 They all follow a binomial distribution They are all independent of each other They all have the same standard deviation They all have mean (expected value) 0
In order to explain the U.S. defense budget, the following variables are considered. Data from 1962-1981 is collected. Variable Definition Y Defense budget-outlay for year t, $ billions X2 GNP for year t, $ billions X3 U.S. military sales/assistance in year t, $ billions X4 Aerospace industry sales, $ billions Dependent Variable: Y Method: Least Squares Date: 11/22/21 Time: 11:20 Sample: 1962 1981 Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. X2 0.016703 0.007017 2.380261 0.0301 X3 -0.696174 0.453978 -1.533497 0.1447 X4 1.467729 0.277608 5.287047 0.0001 22.77514 3.311695 6.877186 0.0000 R-squared 0.971088 Mean dependent var 83.86000 Adjusted R-squared 0.965667 S.D. dependent var 28.97771 S.E. of regression Sum squared resid 5.369339 Akaike info criterion 6.376143 461.2769 Schwarz criterion 6.575290 Log likelihood -59.76143 Hannan-Quinn criter. 6.415019 F-statistic 179.1337 Durbin-Watson stat 0.676777 Prob(F-statistic) 0.000000
In this section, we introduced a descriptive measure of the utility of the regression equation for making predictions. a. Identify the term and symbol for that descriptive measure. b. Provide an interpretation.

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