25. In a partially destroyed record, for the estimation of the two Iines of regression from abivariate data (X, Y), the following results were available:Coefficient of regression of Y on X =-1-6Standard error of the estimate of Y on X 3Coefficient of regression of X on Y =-04Find:(1) Coefficient of correlation between X and Y.(iii) Standard error of the estimate of X on Y.(ii) Standard deviations oy and Oy

Answered: Image /qna-images/answer/e054f36d-f06d-4802-ad6f-d227859af7ab.jpg

Answered: 25. In a partially destroyed record,…

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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The following regression has been estimated: Dependent Variable: disability 1 Independent Variables: (1) Constant, (2) Person's age (ecage26), (3) male1 "male1" is equal to 1 for males and 0 for females; disability1 is equal to 1 for disabled and 0 for not disabled regress disabilityl ecage26 malel Source | Total | Model | 1137.25995 2 Residual | 9104.04738 47702 disabilityl I ecage26 | malel SS cons df 10241.3073 47704 Coef. Std. Err. .0084218 -.0084567 -.0891858 MS 568.629974 .19085253 .214684457 .0001093 .0040058 .0060068 t 77.04 -2.11 -14.85 47705 Number of obs = F( 2, 47702) = 2979.42 Prob > F R-squared Adj R-squared = Root MSE P>|t| 0.000 0.035 0.000 [95% CI] = = 0.0000 0.1110 0.1110 .43687 .0082076 -.0163082 -.1009592 .0086361 -.0006052 -.0774125 a) Report the estimated values (the actual numbers) of the coefficient estimates (including the constant) and explain what they measure. b) What are some economic reasons why the estimated coefficients might take on the signs (and…
An economist wanted to investigate the factors that determined the price of petrol RON97. He gathered 17 months of data on the four variables below: Y = Price of petrol RON97 (RM/litre) X1 = Price of world oil (USD/barrel) X2 = Exchange rate (RM/USD) X3 = Quantity demand for petrol RON97 (million litre) The Excel results are shown as follows: ANOVA df SS MS Significance F Regression Residual 0.467 0.062 3 0.156 32.763 0.000 13 0.005 Total 16 0.529 Coefficients Standard Error t Stat P-value 0.094 0.000 0.003 Intercept -1.132 0.627 -1.805 X1 0.021 0.003 7.000 X2 -0.334 0.092 -3.630 X3 0.010 0.007 1.429 0.165 (a) Determine the regression equation. (b) Determine the coefficient of determination, and interpret. (c) Which independent variables linearly related to the price of petrol RON 95 in this model? Explain. (d) Is the model valid? Explain.
A researcher collected data on ( X, Y ), and wanted to test if the true relationship is linear. Therefore, she collected replicated values of X. Here is a summary of the data, and some results: Xi No. of replicates yi SSE = 312. 1 3 9 10 5 11 8. 3 2 12 14 4 4. 15 13 3 9 6 6 5 11 10 7 1 8 4 8 3 7 5 At significance level a = 0.05, carry out the test of Lack-of-Fit for linearity of the model. No need to show an ANOVA Table. Calculations: Test statistic: T = Critical value = Conclusion: Since we 2.
The height of women ages 20-29 is normally distributed, with a mean of 64.2 inches. Assume o = 2.6 inches. Are you more likely to randomly select 1 woman with a height less than 66 inches or are you more likely to select a sample of 19 women with a mean height less than 66 inches? Explain. Click the icon to view page 1 of the standard normal table. Click the icon to view page 2 of the standard normal table. What is the probability of randomly selecting 1 woman with a height less than 66 inches? (Round to four decimal places as needed.)
please answer Alll
5. You estimated the following regression. Which of the following is the estimated regression line? Source | SS df MS Number of obs = 379 -------------+---------------------------------- F(1, 377) = 24045.16 Model | 401185353 1 401185353 Prob > F = 0.0000 Residual | 6290117.58 377 16684.662 R-squared = 0.9846 -------------+---------------------------------- Adj R-squared = 0.9845 Total | 407475471 378 1077977.44 Root MSE = 129.17 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 10.08326 .065026 155.07 0.000 9.955396 10.21111 _cons | 87.07298 7.855763 11.08 0.000 71.62637 102.5196…
9:33 AA www-awn.aleks.com O REGRESSION AND CORRELATION Predictions from the least-. Managers of an outdoor coffee stand in Coast City are examining the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y, in dollars) and the maximum temperature (denoted by x, in degrees Fahrenheit) for each of fifteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. Temperature, x (in degrees Coffee sales, y (in dollars) Fahrenheit) 68.3 1787.6 38.9 2311.0 72.9 1684.0 2200 58.8 1979.5 63.4 1816.4 2000 75.8 1997.6 46.9 2131.7 71.0 1973.9 100 38.7 2018.2 1400 53.9 1597.5 45.9 1996.1 75.4 1501.0 50.9 2266.3 81.7 1574.5 Figure 1 45.5 1798.9 Send data to Excel After deciding on the appropriateness of a linear model relating coffee sales and maximum temperature, the managers…
Wh ich of the following regressions represents the weakest linear relationship between x and y? Regression 1y=ax+by=ax+ba=11.9a=11.9b=0.1b=0.1r=0.7944r=0.7944 Regression 2y=ax+by=ax+ba=12.9a=12.9b=19.8b=19.8r=0.0346r=0.0346 Regression 3y=ax+by=ax+ba=9.8a=9.8b=15.6b=15.6r=0.2439r=0.2439 Regression 4y=ax+by=ax+ba=-9.4a=−9.4b=11b=11r=-0.5893r=−0.5893 Regression }1Regression 1 t{Regression }2Regression 2 {Regression }3Regression 3 {Regression }4Regression 4
True/false (please explain). Suppose that a regression of Y on X₁ is unbiased, and the true slope coefficient is 2. Another variable X2 is correlated with Y, but it is uncorrelated with X₁. In expectation, the default t-statistic on B₁ will be larger in the multivariate regression that includes X₂ than in the bivariate regression that omits X₂.
Forest Fires and Acres Burned Numbers (in thousands) of forest fires over the year and the number (in hundred thousands) of acres burned for 6 recent years are shown. The regression line equation is =y′+−45.79501.34704x. The correlation coefficient is =r0.858. Compute the standard error of the estimate rounded to at least two decimal places, if appropriate. Assume =α0.05 The standard error of the estimate should or should not be calculated. S est= Number of fires x 57 45 72 69 58 47 Number of acres burned y 30 15 62 42 19 26
mean of x = 2.882, standard deviation = 1.634 Mean of y = 2.588, standard deviation = 0.246 The correlation coefficient is 0.159 slope coefficient for regression line = .0239 Find the intercept for the regression line.
Supposed that you run a correlation and'find the conrelation coefficient is 0.412 and the regression equation is ŷ = 4.1x-0.84. The mean values of data ĕ = L4,4 and ū = 17.5. your were lj=17.5. %3D IF the critical value is ,708, use the Lappropriate method to predict the y value when x is 2,4.

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