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)…
What is a simple way to check for normality of residuals in a regression model?
A multiple regression model has been fit to a data set with p predictor variables, and the adjusted R-squared value is 0.75. What can we conclude about the model fit and the predictor variables in the model?
What is a “residuals plot” and how is it used in assessing linearity of calibration curves?
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…
Write a multiple regression equation that can be used to analyze the data for a randomizedblock design involving three treatments and two blocks. Define all variables
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
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