Test econometrics

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Uploaded: 11.08.2013
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1. As a parameter called the linear regression equation to stand at factor?
coefficient of elasticity
the coefficient of determination
the regression coefficient
correlation coefficient
coefficient of variation
2. Main factors stepwise selection procedures in multiple regression equation is:
sequential removal procedure
sequential evaluation process
Sequential coupling procedure
serial replacement procedure
Serial averaging procedure
3. If a growth factor variable values \u200b\u200bgrow variance estimates of the parameters of the regression equation, it is called:
autoregression
homoscedasticity
autocorrelation
ergodicity
heteroskedasticity
4. What point are excluded from the time series smoothing procedure
standing at the beginning of the time series
facing both the beginning and end of a time series
standing at the end of the time series
5.KMNK applicable in the case
exactly identified model
unidentifiable models
sverhidentifitsiruemoy model
Whether to apply the method of least squares to calculate the parameters of the exponential dependence?
apply after its reduction to a linear form by integrating
yes
apply after its reduction to a linear form by potentiation
no
apply after its reduction to a linear form by taking the logarithm of correlation coefficient of -1 means that
between variables there is a functional relationship
The situation is not defined
linear relationship between the variables missing
between variables there is a linear correlation
The wrong choice of functional form, or referred to the explanatory variables
forecast errors
heteroskedasticity
error of specification
If the correlation coefficient is negative, the linear model
x decreases with the decrease in
decreases with increasing x in
with increasing x increases from
With the exclusion of uninformative factor value previously calculated coefficient of multiple determination:
significantly reduced
increase significantly
will not change
By using analytical smoothing of the time series is plotted a number of levels from time to time, which is called:
trend
trend
lag
extrapolation
If the time series have trend and cyclical fluctuations, then:
you can just simulate the trend
to exclude random component, and then simulate the trend
to exclude cyclical component, and then simulate the trend
MNCs can not be used under conditions (multiple answers):
lack of effect of multicollinearity
close connecting factor with the result
small volume sets
weak link factor with the result
heterogeneity characteristic values;
the effect of the presence of multicollinearity
a small number of signs
To what extent it changes the coefficient of determination
-1 to 0
0 to 1
-1 to 1
from 0 to 10
If the autocorrelation coefficients of all orders are statistically insignificant, the series includes:
only a random component
random component or strong linear trend
Only a strong linear trend
Only a strong non-linear trend
random component or a strong non-linear trend
As interpreted in a linear model regression coefficient?
coefficient of elasticity
absolute growth rate
relative growth rate

Additional information

How many points is preferable to take to calculate the smoothed value
any
odd
an even
Direction connection statistics classified ......
linear and nonlinear
strengths and weaknesses
legitimate and arbitrary
forward and backward
What are the time series are called interval?
levels which reflect the value of the phenomenon under study at any given time
levels which characterize the phenomenon under study for certain time intervals
levels which characterize the phenomenon under study by means of medium or relative values
Multiple correlation coefficient can be:
0 to 1
any less than zero
any positive
-1 to 0
-1 to 1
In the presence of inverse linear functional relationship between quantitative traits X and Y, the correlation coefficient r = ....
-1
-0.6
0.6
0.9
+1
An index of correlation equal to the value of the linear correlation coefficient if the dependence
Logistics
Exponential
Parabolic
linear
The coefficient of determination is the share .......
intra-group variance in the total variance
Intergroup variance in the residual
Intergroup variance in total
intraclass variance in residual dispersion
As a result of regression analysis exploring the function describing ......... indicators.
ratio
structure
interconnection
growth rate
growth
If effective sign influences two factors during the regression analysis build ....... model.
Multivariate
Univariate
Complex
Pair
The square of the coefficient indicates the proportion of the variance of a random variable, due to variation of another
multiple correlation coefficient
the coefficient of determination
Pair correlation coefficient
partial correlation coefficient
An empirical correlation ratio determines the ... ..
variation feature together
connection direction
variation factor underlying the grouping
close connection
variation of other factors, excluding the factor group
In the analysis of time series containing the seasonal component, the easiest way is the method of its calculation:
average shifted
autoregressive average
MA
arithmetic average
chronological average
If the time series is represented by the product of its components, the model number of the called
multiplicative
variativnoj
attributive
additive
In practice, the presence of multicollinearity is usually judged on a matrix of pairwise correlation coefficients. If one of the pair correlation coefficients between the factor variables more ..., it is considered that there is multicollinearity.
0.5
0.3
0.7

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