Consider the following regression model,log(CON)=Bo + B,INC + B2INC2 + B3PRICE + uWhere CON = Household consumption expenditureINC = Household incomeINC? = Household income squaredPRICE = General price levelRegression Estimates- Dependent variable: log(CON)VariablesCoefficient Estimates and (Standard Errors)Equation 1Equation 21.2136(0.1961)Constant1.5731(0.0849)INC0.0029(0.001)INC?0.0013(0.00051)PRICE-0.0081-0.0095(0.0035)(0.0017)N (Number ofObservations)SSR (Sum of SquaredResiduals)R2500500145.63184.090.520.45You wanted to test whether income has significant effect on household consumption. Which of thefollowing represents the approximate critical value for this test at 1% level of significance?Critical value of F with 1% level of significance and 3 and 496 df is: F = 2.60Critical value of t with 1% level of significance and 2 and 498 df is: F = 2.326Critical value of F with 1% level of significance and 2 and 496 df is: F = 4.61Critical value of t with 1% level of significance and 496 df: t = 2.576

Given Information: The regression model is: logCON=β0+β1 INC+β2 INC2+β3 PRICE+u. Number of…

Answered: Consider the following regression…

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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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 425 metric tons of lemon imports. Is the prediction worthwhile? Lemon Imports Crash Fatality Rate 226 270 16 15.8 364 481 521 15.5 15.4 14.9
Suppose a data set of 200 observations (n = 200) was analyzed using OLS to examine the relationship between CEO salary and various measures of firm performance. The regression results are as follows, with standard errors in parentheses: logy = 5 + 0.2logx₁0.03x₂ +0.002x3 (0.2) (0.04) (0.004) where? (0.009) y = CEO salary in thousands of dollars X₁ = annual firm sales X₂ = percentage of sales lost to competitors X3 = return on stock in percent R² = 0.290 Suppose you want to test the null hypothesis that percentage of sales lost to competitors has no ceteris paribus effect on the salaries of CEOS. For the one-sided alternative hypothesis ß₂ < 0 and 1% rejection rule (i.e., at the 1% level), you would_ the null hypothesis that ß₂ = 0.
The flow rate in a device used for air quality measurement depends on the pressure drop x (inches of water) across the device's filter. Suppose that for x values between 5 and 20, these two variables are related according to the simple linear regression model with true regression line y = -0.11 + 0.097x. (a.1) What is the true average flow rate for a pressure drop of 10 in.?(a.2) A drop of 15 in.?(b) What is the true average change in flow rate associated with a 1 inch increase in pressure drop?(c) What is the average change in flow rate when pressure drop decreases by 5 in.?
Given that the systolic blood pressure in the right arm is 90 mm Hg, the best systolic blood pressure in the left arm is how many mm Hg?
please solve it quickly for multiple vote
Log(Wagei) = α + β1Educationi + β2Femalei + β3Non Whitei + β4Educationi × Femalei + εi For males, and holding other variables constant, the regression output from the linear regression model above implies that an extra year of education is associated with what percent change in wages?
What happened to the standard error of educ after adding KWW to the model? Discuss. Do you agree or disagree with the following statement? “If the log of the dependent variable appears in the regression, changing the unit of measurement of any independent variable affects both the slope and intercept coefficients”. Discuss by providing the resource.
Suppose the following estimated regression equation was determined to predict salary based on years of experience. Estimated Salary = 29,136.63 +2257.51(Years of Experience) What is the estimated salary for an employee with 24 years of experience? Answer Keypad Keyboard Shortcuts Tables
The income of farmers depends on various factors. To predict the income of the next year, a study was undertaken and data was gathered considering as many as possible factors that might influence the yearly income. Regression methods are used to create such a prediction function, that is, we want to predict the profit for the next year. The following were determined. X₁ = SIZE - farm size recorded × 1000 hectares X₂ AGE - how long the farm has been in operation in years X3 = RATIO - the ratio of land size to field size recorded as 0.5, 0.75, 0.8 and 0.9 X4 = METHOD - rotational and non rotational method of planting Ŷ INCOME the income per year recorded in × R 1 000 000.00 The partial dataset is as follows INCOME Y 1.3 2.4 3.2 1.5 2.1 a. C. Standardized Residual Sample Quantiles 15 1.0- 0.8- 0.6- 0.4- 0.2- 0.0- 20 SIZE X₁ 25 5 8 2 1.2 1.5 Predicted Theoretical Quantiles. 45 AGE X₂ 1.1. The analyst did some exploratory analysis and below are some of the residual plots he constructed.…
lakhs) of a certain commodity for the year On the basis of quarterly sales (in 2008-12, the following calculations were made : Trend: Y = 20 + 0.5 t with origin at 1st quarter of 2008. where 1 = time unit = one quarter and y = quarterly sales ( Seasonal variations: Quarter I II III IV Seasonal Index 80 90 120 110 Estimate the quarterly sale for the year 1979 using multiplicative model.
Consider the following regression equation representing the linear relationship between the Canada Child Benefit provided for a married couple with 3 children under the age of 6, based on their annual family net income: ŷ =121.09−0.57246xR2=0.894 where y = annual Canada Child Benefit paid (in $100s) x = net annual family income (in $1000s) Source: Canada Revenue Agency a. As the net annual family income increases, does the Canada Child Benefit paid increase or decrease? Based on this, is the correlation between the two variables positive or negative?The Canada Child Benefit paid .The correlation between the two variables is .b. Calculate the correlation coefficient and determine if the relationship between the two variables is strong, moderate or weak.r= , the relationship is . Round to 3 decimal places c. Interpret the value of the slope as it relates to this relationship. For every $1 increase in annual family net income, there is a $0.57246 decrease in…
An economist at Nedbank ran a study of the relationship between FTSE/JSE All Shares index return (JALSH) and consumer price index (CPI) from 2006 to 2017, the data collected is shown in the Table 1 below. FTSE/JSE All Shares index return (JALSH) and consumer price index (CPI) from 2006 to 2017. Year JALSH (Y) CPI (X) 2006 0.41 4.7 2007 0.19 7.1 2008 -0.23 11.5 2009 0.32 7.1 2010 0.19 4.3 2011 0.03 5.0 2012 0.27 5.6 2013 0.21 5.7 2014 0.11 6.1 2015 0.05 4.6 2016 0.00 6.4 2017 0.21 5.3 The estimated regression…

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