Based on the results of lack-of-fit test (attached image). Assess whether an ANOVA model would be more useful than regression. Analysis of VarianceSourceRegressionSweetnessMoisture1 306.251 1566.451394.30Lack-of-Fit 5 37.308 57.0015 1967.00ErrorPure ErrorDF Seq SS Contribution2 1872.70TotalAdj SS Adj MS F-Value P-Value95.21%1872.70936.35129.0815.57% 306.25 306.2542.22215.9579.64% 1566.45 1566.454.79%1.90%2.90%100.00%94.3037.3057.007.257.467.131.050.0000.0000.0000.453

The lack-of-fit test is used to determine if a model is appropriate for the data. It tests the null…

Answered: Based on the results of lack-of-fit…

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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Let's say you then decide to enter the variables age and age-squared. The following are the regression results after adding the age and age-squared (age2) variables. *see image* Question: Based on the output above, is it proven that age has an influence on teaching evaluation scores?
using 10 years of National Football League (NFL) data, we calculate the following regression line to predict regular season wins( WINS) by number of wins in the 4 pre season games(PRE-SEASON): WINS= 5.3 + 0.3(PRE*SEASON)…
Suppose there is a significant correlation between variables. describe 2 cases under which it might be inappropriate to use the linear regression equation for prediction
A consumer advocacy group recorded several variables on 140 models of cars.The resulting information was used to produce two models for predicting miles per gallon in the city (mpg_city), one based on the engine displacement (in cubic inches) and a second one based the power of the engine (in horsepower). Model 1: mpg vs engine displacementThe regression equation ismpg_city=33.5 - 0.063*displacementS = 3.20422R-squared = 66.1% Model 2: mpg vs horsepowerThe regression equation ismpg_city=32.3 - 0.0572*horsepowerS = 3.30539R-squared = 55.1% The variable horsepower is better because it has a higher residual standard error (S=3.30539). The variable horsepower is better because it has a higher residual standard error (S=3.30539) and a lower R-square (55.1%). The displacement variable is better because it has a higher R-square (66.1%). The displacement variable is better because it has a lower estimate for the residual standard error (S=3.20422). The displacement variable is better because…
Hormone replacement therapy (HRT) is thought to increase the risk of breast cancer. The accompanying data on x percent of women using HRT and y breast cancer incidence (cases per 100,000 women) for a region in Germany for 5 years appeared in the paper *Decline in Breast Cancer Incidence after Decrease in Utilization of Hormone Replacement Therapy." The authors of the paper used a simple linear regression model to describe the relationship between HRT use and breast cancer incidence. HRT Use Breast Cancer Incidence 46.30 103.30 40.60 105.00 39.50 100.00 36.60 93.80 30.00 83.50 (a) What is the equation of the estimated regression line? (Round your numerical values to four decimal places.) 9= (b) What is the estimated average change in breast cancer incidence (in cases per 100,000 women) associated with a 1 percentage point increase in HRT use? (Round your answer to four decimal places.) cases per 100,000 women (c) What breast cancer incidence (in cases per 100,000 women) would be…
Based on the scatter plot above, describe the relationship between the amount of cost damage and the distance from nearest fire station to the residential. Determine the regression line for the above regression output. Interpret the slope coefficient of distance to the nearest station affecting the amount of fire cost damage. Compute the correlation coefficient and interpret its meaning. Does the distance to the nearest fire station has significant influence on the cost damage amount? Conduct a test at 1% significance level. ] Predict the fire cost damage generated if the distance from nearest fire station is 2.5 kilometers. Is this estimate reliable? Explain. []
what is a confidence coefficinent for a simple linear regression line whoes alpha =0.05 and line is y(hat)=-7.45-10x
A consumer advocacy group recorded several variables on 140 models of cars.The resulting information was used to produce two models for predicting miles per gallon in the city (mpg_city), one based on the engine displacement (in cubic inches) and a second one based the power of the engine (in horsepower).Model 1: mpg vs engine displacementThe regression equation ismpg_city=33.8 - 0.063*displacementS = 3.26269R-squared = 66.7%Model 2: mpg vs horsepowerThe regression equation ismpg_city=32.2 - 0.0577*horsepowerS = 4.06386R-squared = 49.7% The displacement variable is better because it has a higher R-square (66.7%).The variable horsepower is better because it has a higher residual standard error (S=4.06386). The variable horsepower is better because it has a higher residual standard error (S=4.06386) and a lower R-square (49.7%).The displacement variable is better because it has a lower estimate for the residual standard error (S=3.26269).The displacement variable is better because it…
The following table shows the starting salary and profile of a sample of 10 employees in a certain call center agency. Run a multiple regression analysis with starting salary as the dependent variable (pesos) and GPA, years of experience and civil service ratings as the independent variables. Use .05 level of significance.What is the equation of the resulting multiple linear regression? starting_salary = 3008.61 + 48.65*GPA + 94.79*years_of_experience + 27.36*civil_service_ratings starting_salary = 15000.00 + 48.65*GPA + 94.79*years_of_experience + 27.36*civil_service_ratings starting_salary = 15001.00 + 41.43*GPA + 84.71*years_of_experience + 37.32*civil_service_ratings starting_salary = 2366.77 + 130.25*GPA + 396.39*years_of_experience + 21.67*civil_service_ratings
A company organizes its sales into territories, each assigned to one sales representative. An analyst theorizes that the on-the-job experience level of the sales rep may be related to the amount of sales in each territory. The analyst decided to use the time the sales reps had been employed at the company to predict sales. After taking a random sample of sales territories, the analyst measured the last quarter's Sales in dollars and the Experience the sales rep had at the company in months. Here are the three tables from the regression results: Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Experience (months) 0.5966 0.3559 0.339 497.9058 40 df 1 38 39 Coefficients 6.6108 SS 5205388.04 5205388.04 9420585.51 Standard Error 4250.9742 180.2332 14625973.55 1.4427 How many territories are in the sample? 247910.15 t Stat MS 23.586 4.5822 P-value 2.71E-24 4.85E-05 F 20.9971 Lower 95% Upper 95% 3886.1113…
Do a linear regression between ‘Weight’ vs ‘Performance.’ Let ‘Performance’ be the dependent/response variable and ‘Weight’ be the independent/predictor variable.
True or False: Extrapolation is using the least square regression line for values outside of our given range of data.

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