When using regression to combine forecast of the same

QUESTION

11.When
using regression to combine forecast of the same Y variable derived in with
various methods how do you determine the best forecasts to include in the
model?
a.Forecast
methods that are highly correlated are best to combine with regression
b.Forecasts
with low squared residual correlations are best to combine with regression
c.Forecasts
that are most accurate are best to combine with regression.
d.Qualitative
forecasts of Y should be excluded from the regression to yield a better
combined result.
e. The
model that is best has the most significant constant term
12.What approach can be taken in regression to account for qualitative
factors or events?
a.
transformation of x data
b.
stepwise regression
c.
use of dummy variables
d.
no approach since qualitative factors cannt be used
13.In
the analysis of regression residuals what is an early indication that
heteroscedasticity is present.
a.high
residual value
b.
alternative residuals signs or positive and then negative sign runs in the
residual signs
c.
spikes in the acf early lag periods
d.
megaphone effect in the residual time series

14.What regression method makes use of “Big
Data” in the analysis of total company and state store performance in
various states in the U.S.?
(Points : 3)

Use individual
simple regression models for each store.
Use each store as a
regression X variable in other store performance.
Combine the forecasts for all stores
with an additive approach
Use Panel Data
regression.
Use dummy variables to describe store
performance in each state.

Question 16.16.What
statistic can be used to develop a forecast confidence interval around the
forecast values? (Points :
3)

Standard deviation
Standard Error of the Coefficient
Standard Error of the Estimate
F statistic

17.Which statistic is called the coefficient of
determination and why?
(Points : 3)

t statistic since it
indicated which variables determine the variation in Y.
R2 since it indicates the share
of Y variation determined by X variation.
F statistic since it shows the strength of
the regression relationship.
Mean Squared Regression since it shows the
amount on average of Y determined by X.

18.A multiple regression equation is created with
4 independent variables and 24 data observations. The resulting F value is 4.10.
If all other statistics for the regression are acceptable would you
use the regression to forecast data for your business? (Points : 3)

No, because the F
values it too low.
Yes, because the F value exceeds the table
value.
Yes, because the F value is positive.
It cannot be determined from the above
information.

19.In determining the best variables to choose for
a linear regression model scatter plots can be used. What is an indicator of a good independent
variable candidate for regression analysis? (Points : 3)

A linear scatter
plot relationship between Y and Xwith strong positive or negative slope.
A perfectly vertical scatter plot
relationship between Y and X
A perfectly horizontal scatter plot
relationship between Y and X
A normal distribution relationship between
Y and X

20.Since increasing the number of variables
increases R2 why not include every variable in the regression
equation? (Points :
3)

Yes, you can include
as many significant variables as you wish since computation power is the only
limitation on regression modeling.
No, since the F statistic and Adjusted R2
value degrees of freedom increase and they decrease as a result.
No, since coefficient t-values will
decrease as more variables are added.
No, since the accuracy of the additional
data decreases as more variables are added

ANSWER

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