Q3: Multiple regression using Minitab. interpret the results inbullets step by stepRegression Analysis: y versus x1, x2Regression Equationy 18.4+ 2.010 x1 + 4. 738 x2CoefficientsTermConstantx1x2Model SummaryCoef-18.42.0104.738S12. 7096Analysis of Variance1R-sq92.55%SE Coef18.00.2470.948+T-Value-1.028. 135.00R-sq(adj)90.42%Source DF Adj SS Adj MS F-Value P-ValueRegression 2 14052 7026.1 43.500.000x1110689 10688.7 66.170.000x240314030,924.950.002P-Value VIF0.3410.000 1.000.0021.00R-sq(pred)87.95%+5 ErrorTotal711319 15183161.5

Given thatDependent variable: yIndependent variables: x1 and x2

Answered: Q3: Multiple regression using Minitab.…

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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An educator wants to see how the number of absences for a student in her class affects the student’s final grade by formulating the equation of the regression line. The data obtained from a sample are shown. Required: Slope of the regression line Only the final answer should be rounded-off to TWO decimal places.
What is the answer to part d
(a) Find the coefficient of determination and interpret the result. How can the coefficient of determination be interpreted? (b) Find the standard error of estimate se and interpret the result. How can the standard error of estimate be interpreted?
What is regression R2 ? Explain both with formula and its meaning in a linear regression model? What does it mean if you have a low regression R2 ?
Two variables are defined, a regression equation is given, and one data point is given. Weight Training = = Weight 92 +11.9(Training) = maximum weight capable of bench pressing (pounds) number of hours spent lifting weights a week The data point is an individual who trains 5 hours a week and can bench 150 pounds. Residual (a) Find the predicted value for the data point and compute the residual. Enter the exact answers. Predicted value = M. lbs lbs
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A researcher is investigating possible explanations for deaths in traffic accidents. He examined data from 2000 for each of the 52 cities randomly selected in the US. The variables were death and income. Deaths: The number of deaths in traffic accidents per cityIncome: The median income per city The researcher ran a simple linear regression model: Deaths = Bo+B1(Income). Results shown in photo below. Question: Please help me better understand how to use results from photo to find value of R-squared of this simple linear regression model.
Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed lemon/crash data, where lemon imports are in metric tons and the fatality rates are per 100,000 people, find the best predicted crash fatality rate for a year in which there are 500 metric tons of lemon imports. Is the prediction worthwhile? Find the equation of the regression line. (Round the constant three decimal places as needed. Round the coefficient to six decimal places as needed.) The best predicted crash fatality rate for a year in which there are 500 metric tons of lemon imports is _______ fatalities per 100,000 population. (Round to one decimal place as needed.) Is the prediction worthwhile? A. Since the sample size is small, the prediction is not appropriate. B. Since common sense suggests there should not be much of a relationship between the two variables, the prediction does not make much sense. C. Since all of the requirements…
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Q1: [Regression and Correlation] The index of biotic integrity (IBI) is a measure of water quality in streams. As a data analyst you must monitor, track, and predict changes in water quality. You want to create a simple linear regression model that will allow you to predict changes in IBI in forested area. The following table conveys sample data from a coastal forest region and gives the data for IBI and forested area in square kilometers. Let forest area be the predictor variable (x) and IBI be the response variable (y). IBI X 47 72 21 72234 19 58 49 Forest Area Y 38 59 27 24 63 49 45 Required a. Q2: [Naïve Bayes] a. Write the pseudo-code of the following Naïve Bayes algorithm b. Consider the given dataset that classifies animals into two distinct classes. The classes are labeled as 'cat' and 'dog'. Use Naive Bayes classifier to figure out the class (cat/dog) of an instance if it has the following values of attributed Plot Scatter graph b. Determine the regression equation c. Compute…
The volume (in cubic feet) of a black cherry tree can be modeled by the equation y = - 50.8 + 0.3x, +4.5x,, where x, is the tree's height (in feet) and x, is the tree's diameter (in inches). Use the multiple regression equation to predict the y-values for the values of the independent variables. x, = 70, x, = 8.6 The predicted volume is cubic feet. (Round to one decimal place as needed.)
The trip rate (y) and the corresponding household sizes (x) from a sample are shown in Table Q2. Apply the regression method to find the trip rate for a household size of 10. Table Q2

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Q3: Multiple regression using Minitab. interpret the results in bullets step by step Regression Analysis: y versus x1, x2 Regression Equation