Question

True or False. JUSTIFY YOUR ANSWER.

The 95% Confidence Interval is constructed using exclusively the estimated parameter estimates of the Linear Regression Model.

Answer #1

True.The 95% confidence iinterval is constructed using exclusively the estimated parameter estimates of the linear regression model.

Note:

An **interval constructed**, based on the observed
data(estimated parameter), with a procedure such that if you repeat
the sample process by which the observed data was obtained and the
**construction** of the **confidence
interval** with the estimated patameter, the fraction of
**confidence intervals** containing the population
parameter would be **95%**.

Are the following statements True, False or Uncertain? You will
also need to justify your answer.
(a) In using cross-sectional data to investigate the
demand for food, prices of food are likely to exhibit little or no
variation which leads to biased econometric estimates of the
parameters of the demand equation.
(b) If the disturbances in a classical linear
regression model are not normally distributed, then the OLS
estimator is no longer BLUE but it is still unbiased.

True or False, and explain why.
Suppose that we obtain a 95% confidence interval [3.58,5.71] for
the parameter β2. It means that the probability that the true value
of β2 is within [3.58, 5.71] is 0.95.

If a confidence interval is constructed at a confidence level of 99% instead of 95%:
a)Estimation precision decreases
b)The precision of the estimate is not altered
c)We can accurately calculate the value of the parameter of interest
d)Estimation accuracy improves significantly

True/False
1. a) For the 95% confidence interval we are sure that the true
mean is in this interval.
b) A sample of 100 fuses from a very large shipment is found to
have 7% of defective fuses. The Q/C inspector can calculate the
confidence interval for defective fuses.

Please answer true / false / uncertain to the statement and
EXPLAIN THE ANSWER.
Assume a regression coefficient has a p-value of 0.02. Then, at
95% confidence we reject the hypothesis that the true parameter is
zero. However, at 99% confidence we accept the same hypothesis.

For a given population with unknown mean
The size of the 95% confidence interval for the mean is
smaller than that of the 99% confidence interval
True
b)
False
In a Least Square Method, the Coefficient of
Determination
Provides the value of the slope of the SLR model
True
b)
False
Can vary between 0 and 1
True
b)
False
Can vary between -1 and 1
True
b)
False
Provides the intercept of the SLR model...

True or False. Explain your answer:
d) Least squares estimates of the regression coefficients b0,
b1, . . . bn are chosen to maximize R2 .
e) If all the explanatory variables are uncorrelated, the
variance inflation factor (VIF) for each explanatory variable will
be 1.
) b0 and b1 from a simple linear regression model are
independent.

8. In the first experiment you construct 95% confidence interval
based A sample size of 50 and in the second experiment you
construct a 95% confidence interval based on a sample size of 80 A)
the probablity that the parameter of interest will be inside the
second confidence interval is higher since the sample size is
bigger True? False? Explain
B) the size of the second confidence interval is wider than the
size of the first confidence interval True? False?...

12.1. State whether the following statements are true or false.
Briefly justify your answer.
a. When autocorrelation is present, OLS estimators are biased as
well as inefficient.
b. The Durbin–Watson d test assumes that the variance of the
error term ut is homoscedastic.
c. The first-difference transformation to eliminate
autocorrelation assumes that the coefficient of autocorrelation rho
is -1.
d. The R2 values of two models, one involving regression in the
first-difference form and another in the level form, are...

Indicate whether each of the following statements is
True or False, and Briefly Justify your answer.
The adjusted R2 is used because the unadjusted
R2 automatically and spuriously increases when extra
explanatory variables are added to the model.

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