What is the coefficient of determination for a linear model fitting the following bivariate dataset?AOBivariate Fit of y By x6055y50-45-40101214161820(Hint: what's the difference between the correlation and the coefficient of determination ?)Select one:a.-90.097%O b. 22.698%O c. -10.903%O d. 10.903%O e. 90.097%2.

Here we have given the scatterplot between x and y (for bivariate data set). Using this we have to…

Answered: What is the coefficient of…

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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Explain what are the least-squares line and Regression?
Consider a linear regression model that relates school expenditures and family background to student performance in Massachusetts using 224 school districts. The response variable is the mean score on the MCAS (Massachusetts Comprehensive Assessment System) exam given in May 1998 to 10th-graders. Four explanatory variables are used: (1) STR is the student-to-teacher ratio, (2) TSAL is the average teacher’s salary, (3) INC is the median household income, and (4) SGL is the percentage of single family households. The Excel Regression output for the sample regression equation is given below. (a) What proportion of the variation in MCAS score is explained by the explanatory variables? (b) At the 5% level, are the explanatory variables jointly significant in explaining MCAS score? Explain briefly. (c) At the 5% level, which variables are individually significant at predicting MCAS score? Explain briefly. (d) Suppose a second regression model (Model 2) was generated using only…
A nonprofit analyst considered two independent variables as a predictor for the dependent variable Commitment, the percent of total expenses that are allocated to charitable services. The independent variables are Revenue, total revenue in billions of dollars, andEfficiency, the percent of private donations remaining after fundraising expenses. The regression analysis resulted in this ANOVA table. Determine whether there is a significant relationship between commitment and the two independent variables at the 0.01 level of significance. Source Degrees of Freedom Sum of Squares Mean Square F p-value Regression 2 3640.0416 1820.02 51.0429 <.0001 Error 87 3102.1301 35.66 Total 89 6742.1717 Determine the p-value. The p-value is ________ (Round to three decimal places as needed.)
We expect a car’s highway gas mileage to be related to its city gas mileage (in mpg). Data for all 12091209 vehicles in the government’s 2016 Fuel Economy Guide give the regression line highway mpg=7.903+(0.993×city mpg)highway mpg=7.903+(0.993×city mpg) for predicting highway mileage from city mileage. (a) What is the slope of this line? (Enter your answer rounded to three decimal places.) What does the numerical value of the slope tell you? On average, highway mileage decreases by 0.9930.993 mpg for each additional mpg in city mileage. On average, highway mileage increases by 0.9930.993 mpg for each additional mpg in city mileage. For every 7.9037.903 mpg in city gas mileage, highway gas mileage increases about 0.9930.993 mpg. Highway gas mileage increases with city gas mileage by 7.9037.903 mpg for each additional mpg in city mileage. On average, highway mileage increases by 7.9037.903 mpg for each additional mpg in city mileage. (b) What is the intercept?…
We expect a car's highway gas mileage to be related to its city gas mileage (in miles per gallon, mpg). Data for all 1259 vehicles in the government's 2019 Fuel Economy Guide give the regression line highway mpg = 8.720+ (0.914x city mpg) for predicting highway mileage from city mileage. O Macmillan Learning (c) Find the predicted highway mileage, y, for a car that gets 14 mpg in the city. Give your answer to three decimal places. y = mpg Find the predicted highway mileage, y, for a car that gets 21 mpg in the city. Give your answer to three decimal places. y = mpg
A study was conducted on 64 female college athletes. The researcher collected data on a number of variables including percent body fat, total body weight, height, and age of athlete. The researcher wondered if % body fat (%BF), height (HGT), and/or age are significant predictors of total body weight. All conditions have been checked and are met and no transformations were needed. The technology output from the multiple regression analysis is given below. Interpret the coefficient of % body fat
To properly examine the effect of a categorical independent variable in a multiple linear regression model we use an interaction term. True O False
Which Python module and method are used to create a multiple regression model for a given data set? Select one. linregress method from scipy module ols method from statsmodel module ols method from scipy module linregress method from statsmodel module

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