WorksheetsRegression Analysis Quiz
Total questions: 65
Worksheet time: 58mins
A change in the unit of measurement of the dependent variable in a model does not lead to a change in:
the standard error of the regression.
the sum of squared residuals of the regression.
the goodness-of-fit of the regression.
the confidence intervals of the regression.
Changing the unit of measurement of any independent variable, where log of the dependent variable appears in the regression:
affects only the intercept coefficient.
affects only the slope coefficient.
affects both the slope and intercept coefficients.
affects neither the slope nor the intercept coefficient.
A variable is standardized in the sample:
by multiplying by its mean.
by subtracting off its mean and multiplying by its standard deviation.
by subtracting off its mean and dividing by its standard deviation.
by multiplying by its standard deviation.
Standardized coefficients are also referred to as:
beta coefficients.
y coefficients.
alpha coefficients.
j coefficients.
If a regression equation has only one explanatory variable, say x1, its standardized coefficient must lie in the range:
-2 to 0.
-1 to 1.
0 to 1.
0 to 2.
In the following equation, gdp refers to gross domestic product, and FDI refers to foreign direct investment. log(gdp) = 2.65 + 0.527log(bankcredit) + 0.222FDI (0.13) (0.022) (0.017) Which of the following statements is then true?
If gdp increases by 1%, bank credit increases by 0.527%, the level of FDI remaining constant.
If bank credit increases by 1%, gdp increases by 0.527%, the level of FDI remaining constant.
If gdp increases by 1%, bank credit increases by log(0.527)%, the level of FDI remaining constant.
If bank credit increases by 1%, gdp increases by log(0.527)%, the level of FDI remaining constant.
In the following equation, gdp refers to gross domestic product, and FDI refers to foreign direct investment. log(gdp) = 2.65 + 0.527log(bankcredit) + 0.222FDI (0.13) (0.022) (0.017) Which of the following statements is then true?
If FDI increases by 1%, gdp increases by approximately 22.2%, the amount of bank credit remaining constant.
If FDI increases by 1%, gdp increases by approximately 26.5%, the amount of bank credit remaining constant.
If FDI increases by 1%, gdp increases by approximately 24.8%, the amount of bank credit remaining constant.
If FDI increases by 1%, gdp increases by approximately 52.7%, the amount of bank credit remaining constant.
Which of the following statements is true when the dependent variable, y > 0?
Taking log of a variable often expands its range.
Models using log(y) as the dependent variable will satisfy CLM assumptions more closely than models using the level of y.
Taking log of variables make OLS estimates more sensitive to extreme values.
Taking logarithmic form of variables make the slope coefficients more responsive to rescaling.
Which of the following correctly identifies a limitation of logarithmic transformation of variables?
Taking log of variables make OLS estimates more sensitive to extreme values in comparison to variables taken in level.
Logarithmic transformations cannot be used if a variable takes on zero or negative values.
Logarithmic transformations of variables are likely to lead to heteroskedasticity.
Taking log of a variable often expands its range which can cause inefficient estimates.
Which of the following models is used quite often to capture decreasing or increasing marginal effects of a variable?
Models with logarithmic functions
Models with quadratic functions
Models with variables in level
Models with interaction terms
Which of the following correctly represents the equation for adjusted R2?
R2 = 1 - [SSR/(n -1)]/[SST/(n+1)]
R2 = 1 - [SSR/(n -k - 1)]/[SST/(n+1)
R2 = 1 - [SSR/(n -k - 1)]/[SST/(n - 1)]
R2 = 1 - [SSR]/[SST/(n - 1)]
Which of the following correctly identifies an advantage of using adjusted R2 over R2?
Adjusted R2 corrects the bias in R2.
Adjusted R2 is easier to calculate than R2.
The penalty of adding new independent variables is better understood through adjusted R2 than R2.
The adjusted R2 can be calculated for models having logarithmic functions while R2 cannot be calculated for such models.
Two equations form a nonnested model when:
one is logarithmic and the other is quadratic.
neither equation is a special case of the other.
each equation has the same independent variables.
there is only one independent variable in both equations.
A predicted value of a dependent variable:
represents the difference between the expected value of the dependent variable and its actual value.
is always equal to the actual value of the dependent variable.
is independent of explanatory variables and can be estimated on the basis of the residual error term only.
represents the expected value of the dependent variable given particular values for the explanatory variables.
Residual analysis refers to the process of:
examining individual observations to see whether the actual value of a dependent variable differs from the predicted value.
calculating the squared sum of residuals to draw inferences for the consistency of estimates.
transforming models with variables in level to logarithmic functions so as to understand the effect of percentage changes in the independent variable on the dependent variable.
sampling and collection of data in such a way to minimize the squared sum of residuals.
Beta coefficients are always greater than standardized coefficients.
If a new independent variable is added to a regression equation, the adjusted R2 increases only if the absolute value of the t statistic of the new variable is greater than one.
F statistic can be used to test nonnested models.
Predictions of a dependent variable are subject to sampling variation.
To make predictions of logarithmic dependent variables, they first have to be converted to their level forms.
A variable is used to incorporate qualitative information in a regression model.
dependent
continuous
binomial
dummy
In a regression model, which of the following will be described using a binary variable?
Whether it rained on a particular day or it did not
The volume of rainfall during a year
The percentage of humidity in air on a particular day
The concentration of dust particles in air
Which of the following is true of dummy variables?
A dummy variable always takes a value less than 1.
A dummy variable always takes a value higher than 1.
A dummy variable takes a value of 0 or 1.
A dummy variable takes a value of 1 or 10.
Refer to the model above. The inclusion of another binary variable in this model that takes a value of 1 if a person is uneducated, will give rise to the problem of
omitted variable bias
self-selection
dummy variable trap
heteroskedastcity
Refer to the model above. The benchmark group in this model is
the group of educated people
the group of uneducated people
the group of individuals with a high income
the group of individuals with a low income
Refer to the above model. If ∂0 > 0,
uneducated people have higher savings than those who are educated
educated people have higher savings than those who are not educated
individuals with lower income have higher savings
individual with lower income have higher savings
If there are 5 ethnic groups in Budopia, how many dummy variables should be included in the regression equation for income determination in Budopia?
1
5
6
4
The quarterly increase in an employee's salary depends on the rating of his work by his employer and several other factors as shown in the model below: Increase in salary= β0+∂0Rating + other factors. The variable 'Rating' is a(n) _____
dependent variable
ordinal variable
continuous variable
Poisson variable
Which of the following is true of Chow test?
It is a type of t test.
It is a type of sign test.
It is only valid under homoskedasticty.
It is only valid under heteroskedasticity.
Which of the following is true of dependent variables?
A dependent variable can only have a numerical value.
A dependent variable cannot have more than 2 values.
A dependent variable can be binary.
A dependent variable cannot have a qualitative meaning.
In the following regression equation, y is a binary variable: y= β0+β1x1+…βk xk+ u In this case, the estimated slope coefficient, β^1 measures
the predicted change in the value of y when x1 increases by one unit, everything else remaining constant
the predicted change in the value of y when x1 decreases by one unit, everything else remaining constant
the predicted change in the probability of success when x1 decreases by one unit, everything else remaining constant
the predicted change in the probability of success when x1 increases by one unit, everything else remaining constant
Consider the following regression equation: y = β0+β1x1+…βk xk+ u In which of the following cases, the dependent variable is binary?
y indicates the gross domestic product of a country
y indicates whether an adult is a college dropout
y indicates household consumption expenditure
y indicates the number of children in a family
Which of the following Gauss-Markov assumptions is violated by the linear probability model?
The assumption of constant variance of the error term.
The assumption of zero conditional mean of the error term.
The assumption of no exact linear relationship among independent variables.
The assumption that none of the independent variables are constants.
Which of the following problems can arise in policy analysis and program evaluation using a multiple linear regression model?
There exists homoscedasticity in the model.
The model can produce predicted probabilities that are less than zero and greater than one.
The model leads to the omitted variable bias as only two independent factors can be included in the model.
The model leads to an overestimation of the effect of independent variables on the dependent variable.
Consider the following regression equation: y = β0+β1x1+…βk xk+ u In which of the following cases, is 'y' a discrete variable?
y indicates the gross domestic product of a country
y indicates the total volume of rainfall during a year
y indicates household consumption expenditure
y indicates the number of children in a family
A binary variable is a variable whose value changes with a change in the number of observations.
A dummy variable trap arises when a single dummy variable describes a given number of groups.
The dummy variable coefficient for a particular group represents the estimated difference in intercepts between that group and the base group.
The multiple linear regression model with a binary dependent variable is called the linear probability model.
A problem that often arises in policy and program evaluation is that individuals (or firms or cities) choose whether or not to participate in certain behaviors or programs.
Which of the following is true of heteroskedasticity?
Heteroskedasticty causes inconsistency in the Ordinary Least Squares estimators.
Population R2 is affected by the presence of heteroskedasticty.
The Ordinary Least Square estimators are not the best linear unbiased estimators if heteroskedasticity is present.
It is not possible to obtain F statistics that are robust to heteroskedasticity of an unknown form.
Consider the following regression model: yi=β0+β1 xi+ui. If the first four Gauss- Markov assumptions hold true, and the error term contains heteroskedasticity, then
Var(ui|xi) =0
Var(ui|xi) =1
Var(ui|xi) = σ^2
Var(ui|xi) =σ
The general form of the t statistic is
t = estimate-hypothesized valuestandard error
t = hypothesized value - estimatestandard error
t = standard error estimate-hypothesized value
t = estimate−hypothesized value
Heteroskedasticity-robust t statistics are justified only if the sample size is large.
True
False
The heteroskedasticity-robust___ is also called the heteroskedastcity-robust Wald statistic.
t statistic
F statistic
LM statistic
z statistic
Which of the following tests helps in the detection of heteroskedasticity?
The Breusch-Pagan test
The Breusch-Godfrey test
The Durbin-Watson test
The Chow test
What will you conclude about a regression model if the Breusch-Pagan test results in a small p-value?
The model contains homoskedasticty.
The model contains heteroskedasticty.
The model contains dummy variables.
The model omits some important explanatory factors.
A test for heteroskedasticty can be significant if.
the Breusch-Pagan test results in a large p-value
the White test results in a large p-value
the functional form of the regression model is misspecified
the regression model includes too many independent variables
Which of the following is a difference between the White test and the Breusch-Pagan test?
The White test is used for detecting heteroskedasticty in a linear regression model while the Breusch-Pagan test is used for detecting autocorrelation.
The White test is used for detecting autocorrelation in a linear regression model while the Breusch-Pagan test is used for detecting heteroskedasticity.
The number of regressors used in the White test is larger than the number of regressors used in the Breusch-Pagan test.
The number of regressors used in the Breusch-Pagan test is larger than the number of regressors used in the White test.
Which of the following is true of the White test?
The White test is used to detect the presence of multicollinearity in a linear regression model.
The White test cannot detect forms of heteroskedasticity that invalidate the usual Ordinary Least Squares standard errors.
The White test can detect the presence of heteroskedasticty in a linear regression model even if the functional form is misspecified.
The White test assumes that the square of the error term in a regression model is uncorrelated with all the independent variables, their squares and cross products.
Which of the following is true?
In ordinary least squares estimation, each observation is given a different weight.
In weighted least squares estimation, each observation is given an identical weight.
In weighted least squares estimation, less weight is given to observations with a higher error variance.
In ordinary least squares estimation, less weight is given to observations with a lower error variance.
Weighted least squares estimation is used only when.
the dependent variable in a regression model is binary
the independent variables in a regression model are correlated
the error term in a regression model has a constant variance
the functional form of the error variances is known
Consider the following regression equation: y=β0 +β1 x1 +u . Which of the following indicates a functional form misspecification in E(y|x)?
Ordinary Least Squares estimates equal Weighted Least Squares estimates.
Ordinary Least Squares estimates exceed Weighted Least Squares estimates by a small magnitude.
Weighted Least Squares estimates exceed Ordinary Least Squares estimates by a small magnitude.
Ordinary Least Square estimates are positive while Weighted Least Squares estimates are negative.
Which of the following tests is used to compare the Ordinary Least Squares (OLS) estimates and the Weighted Least Squares (WLS) estimates?
The White test
The Hausman test
The Durbin-Watson test
The Breusch-Godfrey test
The linear probability model contains heteroskedasticity unless.
the intercept parameter is zero
all the slope parameters are positive
all the slope parameters are zero
the independent variables are binary
The interpretation of goodness-of-fit measures changes in the presence of heteroskedasticity.
True
False
Multicollinearity among the independent variables in a linear regression model causes the heteroskedasticity-robust standard errors to be large.
True
False
If the Breusch-Pagan Test for heteroskedasticity results in a large p-value, the null hypothesis of homoskedasticty is rejected.
True
False
The generalized least square estimators for correcting heteroskedasticity are called weighed least squares estimators.
True
False
The linear probability model always contains heteroskedasticity when the dependent variable is a binary variable unless all of the slope parameters are zero.
True
False
Which of the following correctly identifies a difference between cross-sectional data and time series data?
Cross-sectional data is based on temporal ordering, whereas time series data is not.
Time series data is based on temporal ordering, whereas cross-sectional data is not.
Cross-sectional data consists of only qualitative variables, whereas time series data consists of only quantitative variables.
Time series data consists of only qualitative variables, whereas cross-sectional data does not include qualitative variables.
A stochastic process refers to a:
sequence of random variables indexed by time.
sequence of variables that can take fixed qualitative values.
sequence of random variables that can take binary values only.
sequence of random variables estimated at the same point of time.
The sample size for a time series data set is the number of:
variables being measured.
time periods over which we observe the variables of interest less the number of variables being measured.
time periods over which we observe the variables of interest plus the number of variables being measured.
time periods over which we observe the variables of interest.
The model: Yt = β0 + β1ct + ut, t = 1,2,…….n, is an example of a(n):
autoregressive conditional heteroskedasticity model.
static model.
finite distributed lag model.
infinite distributed lag model.
A static model is postulated when:
a change in the independent variable at time 't' is believed to have an effect on the dependent variable at period 't + 1'.
a change in the independent variable at time 't' is believed to have an effect on the dependent variable for all successive time periods.
a change in the independent variable at time 't' does not have any effect on the dependent variable.
a change in the independent variable at time 't' is believed to have an immediate effect on the dependent variable.
