 # Econometrics, version 9

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Chast№2
"Regression Analysis"
According to the data, including 20 cases (20 countries), built regression equations. In these equations, the dependent variable is a socially significant sign of Y. As explanatory variables used features in various combinations. For each equation to calculate the value of the coefficient of determination (R2), the value of F-statistics. Under the coefficients are given values \u200b\u200bof the sample standard deviation.
1. Using Table Fisher-Snedecor distribution, check the level of significance  = 0,05 significance of the regression equation as a whole.
2. Calculate the value of t-statistics of the coefficients using the values \u200b\u200bof sample standard deviation given by each of the factors. Rewrite the regression equation coefficients indicate a value of t-statistics.
In the table, define the Student distribution tcr - critical t-statistics for each of the equations on the significance level  = 0,05. Check the value of the coefficients of the regression equation.
3. Make a conclusion about the "suitability" of the regression equation for the study of feature Y.
Under the values \u200b\u200bof the coefficients shows the values \u200b\u200bof standard deviation.
Var.9
= 90,951 - 0,426x3 - 0,690x4 - 0,210x6 + 10,109x9; R2 = 0,908; F = 29,646;
(0.310) (0.382) (1.309) (5.847)
Test№1
1.Parny correlation coefficient r12 = 0,6, x3 sign overstates the link between x1 and x2. Partial correlation coefficient may be set to:
a) 0.8; b) 0.5; c) 0.6; g) -0.8;
2.Mnozhestvenny correlation coefficient may be equal to:
a) 1.2; b) -1; c) 0.5; g) 0,4.
3.Koeffitsient determination can be set to:
a) 1.2; b) -1; c) 0.5; g) 0,4.
4.Izvestno that at a fixed value between the values \u200b\u200bof x3 x1 and x2 there is a positive relationship. Partial correlation coefficient r12 / 3 may be equal to:
a) 0.8; b) 0; c) 1.3; g) 0,4.
5.Priznak x3 strengthens the link between x1 and x2. Partial correlation coefficient r12 / 3 = 0.45. Pair correlation coefficient may be set to:
a) 0.8; b) 1.8; c) 1.3; d) 0.3.

Test№2
1. Multiple correlation coefficient r1 / 23 = 0.8. Signs of the influence of X2 and X3 is due to the following percentage of the variance x1:
a) 64; b) 80; c) 20; g) 36.
2.Mnozhestvenny correlation coefficient r1 / 23 = 0.8. The influence of factors unaccounted for in the model is due to the following percentage of the variance x1:
a) 64; b) 80; c) 20; g) 36.
3.Parny correlation coefficient significant at = 0.05. It can be argued that it is also significant for the following:
a) 0.1; b) 0.01; c) 0.02; g) 0.001.
4. Pair correlation coefficient r12 = 0,3, partial correlation coefficient r12 / 3 = 0.7. It can be argued that:
a) reinforces the connection between x3 x1 and x2; b) weakens the link between x3 x1 and x2;
c) x3 weakens the bond between X1 and X2, and change its direction;
d) strengthens the link between x3 x1 and x2 and changes its direction.
5.Pri test the significance of pair correlation coefficients and partial distribution is used:
a) Pearson; b) Student; c) Normal; g) Fischer-Snedecor.

Test№3
1. In the method of least squares is minimized:
a); b); c); g)
2.Uravneniyu regression corresponds to multiple correlation coefficient ry / 12 = 0.84. The share of the variation of a productive indicator, due to the influence of X1 and X2 (%):
a) 70.6; b) 16; c) 84; g) 29.4
3.Uravneniyu regression corresponds to multiple correlation coefficient ry / 12 = 0.84. The share of the variation of a productive indicator, due to the influence of random, not included in the model factors is (%):
a) 70.6; b) 16; c) 84; g) 29.4
4.Mnozhestvennoe linear regression equation is considered significant at = 0.05. It can be argued that the equation is also significant for the following:
a) 0.1; b) 0.01; c) 0.02; g) 0.001.
5.Get model
where - the consumption of beef, x2 - the cost of 1 pound of beef, x3 - the cost of 1 pound of pork, x4 - the cost of 1 pound of chicken. With an increase in the cost of beef by 1% on a constant value X3 and X4 beef consumption will decline by an average of (%):
a) 0.63; b) 0.345; c) 11.08; g) 0,8.

Test№4
1. To test the significance of the multiple linear regression equation used distribution:
a) normal; b) Pearson; c) Fischer-Snedecor; d) Student.
2. According to the n = 20 companies obtained the equation regressii.Srednekvadraticheskie deviation and regression coefficients. At = 0.05 can be stated that:
a) significant factor; b) a significant factor;
c) the significant coefficient and; g) and insignificant factors.
3. For the time series residues (i = 1,2, ..., 18)
The value of Durbin-Watson statistic for the number of residues is:
a) 1.9; b) 0.53; c) 2.92; g) 3,9.
4. MNCs to determine the coefficient of multiple linear regression equation
by the expression, where the matrix has the dimensions:
a) [2 2]; b) [to a]; c) [(k + 1) [(k + 1)]; g) [to n].
5. Obtained a significant regression equation
The standard deviation assessment factor () is:
a) 0.42; b) 3.45; c) 0.15; d) 8.

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