RegressionThe data below are claimed to relate to four experiments, which were conducted with theobjective of investigating the effect of the value of a variable x on the associated value of avariable y. In each experiment the same values of x were used: thus only one column of x-values is given, while there are four columns of y-values, one for each experiment.Xy132y3y410.021-2622.4020-7022.948.020-5721-84 20-4319-8513.020-15 21-3525-3119.659.022-28 22.15 20-5620-2511-021.36 22-2720-8022-3814.022.30 20-43 21.19 23-846.021-35 20-25 20-2120-554.018.81 17-6619.9019.5312-07-05.023-63 21.90 20-95 21.5518.73 21.15 20-31 19.7220-01 19-05 20-09 20-19For each set of data:(i) obtain the equation of the least squares regression line of y on x;(ii) plot the data, and comment on the results.

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Answered: Regression The data below are claimed…

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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Is It Getting Harder to Win a Hot Dog Eating Contest?Every Fourth of July, Nathan’s Famous in New York City holds a hot dog eating contest. The table below shows the winning number of hot dogs and buns eaten every year from 2002 to 2015, and the data are also available in HotDogs. The figure below shows the scatterplot with the regression line. Year Hot Dogs 2015 62 2014 61 2013 69 2012 68 2011 62 2010 54 2009 68 2008 59 2007 66 2006 54 2005 49 2004 54 2003 45 2002 50 Winning number of hot dogs in the hot dog eating contest Winning number of hot dogs and buns Click here for the dataset associated with this question. (a) Is the trend in the data mostly positive or negative? Positive Negative (b) Using the figure provided, is the residual larger in 2007 or 2008?Choose the answer from the menu in accordance to item (b) of the question statement 20072008 Is the residual positive or…
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Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 8.93 3.96 2.26 0.0289 Education 1.34 0.38 3.53 0.0010 Experience 0.48 0.20 2.40 0.0205 Age −0.04 0.05 −0.80 0.4278 a-1. Interpret the point estimate for β1. multiple choice 1 As Education increases by 1 year, Wage is predicted to increase by 1.34/hour. As Education increases by 1 year, Wage is predicted to increase by 0.48/hour. As Education increases by 1 year, Wage is predicted to increase by 1.34/hour, holding Age and Experience constant. As Education increases by 1 year, Wage is predicted to increase by 0.48/hour, holding Age and Experience constant. a-2. Interpret the point estimate for β2. multiple…
The Simple Linear Regression model is Y = b0 + b1*X1 + u and the Multiple Linear Regression model with k variables is: Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + u Y is the dependent variable, the X1, X2, ..., Xk are the explanatory variables, b0 is the intercept, b1, b2, ..., bk are the slope coefficients, and u is the error term, Yhat represents the OLS fitted values, uhat represent the OLS residuals, b0_hat represents the OLS estimated intercept, and b1_hat, b2_hat,..., bk_hat, represent the OLS estimated slope coefficients. QUESTION 16 In a t-test, suppose a researcher sets the significance level at 0.5%. What does this mean? The probability that the null hypothesis is true is 0.5% The researcher would be rejecting the null hypothesis, only if the p-value is less than 0.5% The researcher would be rejecting the null hypothesis, if the t-statistic is higher than 0.5 It does not mean anything, because the significance level can only be set at 5% QUESTION 17 In an MLR…
Amount of fertilizer (x) in pounds for each plot: 12, 5, 15, 17, 20, 14: Bushels of tomatoes harvested (y) 24 22 31 33 21 28: Round all values to three decimal places. a) Find the equation of the regression line (line of best fit). b) Calculate the correlation coefficient (r). c) Determine if there is a significant linear correlation. d) If the linear relation is significant, predict how many bushels of tomatoes would be harvested if 22 pounds of fertilizer was used.
Let sale denote the sale price for a house, sqft its square footage, and days the number of days that it has been on the market. Consider running two different regressions: First regression: sale = B1 + B2sqft + ß3days + e Second regression: sale = Bi + B2sqft + e Which of the following statements is false? In the options below, the quantities SST, SSE, and SSR are as defined when we discussed R?, that is SST = E(Y, – Ỹ)², SSE = E(Y; - Ý )², and SSR = i=1 O a. The SST from the first regression can be strictly smaller than the SST from the second regression. O b. The SSE from the first regression can be strictly smaller than the SSE from the second regression. O c. The R- from the first regression can be strictly larger than the R2 from the second regression. O d. The R2 from the second regression is equal to the R2 from the regression sqft = Yi + Y2sale + e O e. The SSR from the first regression can be strictly larger than the SSR from the second regression.
The Simple Linear Regression model is Y = b0 + b1*X1 + u and the Multiple Linear Regression model with k variables is: Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + u Y is the dependent variable, the X1, X2, ..., Xk are the explanatory variables, b0 is the intercept, b1, b2, ..., bk are the slope coefficients, and u is the error term, Yhat represents the OLS fitted values, uhat represent the OLS residuals, b0_hat represents the OLS estimated intercept, and b1_hat, b2_hat,..., bk_hat, represent the OLS estimated slope coefficients. QUESTION 7 In the MLR model, the assumption of ‘linearity in parameters’ is violated if: one of the slope coefficients appears as a power (e.g. Y = b0 + b1*(X1^b2) + b3*X2 + u) the model includes the reciprocal of a variable (e.g. 1/X1) the model includes a variable squared (e.g. X1^2) the model includes a variable in its logarithmic form (i.e. log(X1) ) QUESTION 8 In the MLR model, the assumption of 'no perfect collinearity'…

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