Question

**Question
1**

**Heteroskedasticity is a violation of which assumption of
the multiple regression model?**

a)
E(*e*_{i}) = 0

b)
cov(*e*_{i}, *e*_{j}) = 0 where
*i*≠*j*

c) The values of each
independent variable *x*_{ik} are not
random

d)
var(*e*_{i}) = *σ*^{2}

**Question
2**

**You have
estimated a multiple regression model with 6 explanatory variables
and an intercept from a sample with 46 observations. What is the
critical value (or t-crit) if you want to perform a
one-tailed t test at the 1% level of significance?**

a) 2.704

b) 2.423

c) 2.426

d) 2.708

Answer #1

D is right option.

Heteroskedasticity is defined as a violation of assumption of constant variances for different observations of the error term. the error term's variance can change depending on the observation...often occurs when there is a wide disparity between the largest ad the smallest observed value of the dependent variable We would expect the error term variances to e larger for larger observations, nut the error term distribution for small observations might have a small variance.

Sorry for second one i tried hard but couldn't find its answer.. Really sorry for that

Which assumption of the linear regression model is called ‘no
serial correlation’? Select one:
a. et∼N(0,σ2) e t ∼ N ( 0 , σ 2 ) b. E(et)=0 E ( e t ) = 0 c.
cov(et,es)=0,t≠s c o v ( e t , e s ) = 0 , t ≠ s d. var(et)=σ2

Multiple regression model
Consider a dataset for 500 students living in Melbourne with the
following variables measured in 2019 --- expenditure on public
transport, ?E, measured in dollars; number of days he/she attended
school, ?S; number of days not attended school, ??NS (which is
equal to 365−?365−S); income, ?I, measured in dollars; and age in
year, ?A. A researcher is interested in estimating the following
model using Eviews:
??=?1+?2??+?3???+?4??+?5??+??Ei=β1+β2Si+β3NSi+β4Ii+β5Ai+ei. Without
any further information, we know for sure that one assumption...

You have estimated a multiple regression model with 6
explanatory variables and an intercept from a sample with 46
observations. Suppose you are interested in testing that X2, X3,
and X4 are jointly significant at 5% significance level. The
appropriate F test has _____________ numerator degrees of
freedom.

Which of the following statements is correct regarding the
multiple regression model?
A)The assumption var(e_i│X)=〖var(y_i│X)=σ〗^2 implies that the
error variance is constant and therefore heteroscedastic.
B) The assumption var(e_i│X)=〖var(y_i│X)=σ〗_i^2 implies that the
error variance is constant and therefore homoscedastic.
C) If var(e_i│X)=〖var(y_i│X)=σ〗_i^2 then the error variance may
change from observation to observation and therefore
heteroscedastic.
D) If var(e_i│X)=〖var(y_i│X)=σ〗^2 then the error variance may
change from observation to observation and therefore
homoscedastic.

Using 20 observations, the multiple regression model y
= β0 +
β1x1 +
β2x2 + ε was
estimated. A portion of the regression results is shown in the
accompanying table:
df
SS
MS
F
Significance
F
Regression
2
2.12E+12
1.06E+12
55.978
3.31E-08
Residual
17
3.11E+11
1.90E+10
Total
19
2.46E+12
Coefficients
Standard
Error
t
Stat
p-value
Lower 95%
Upper 95%
Intercept
−986,892
130,984
−7.534
0.000
−1,263,244
−710,540
x1
28,968
32,080
0.903
0.379
−38,715
96,651
x2
30,888
32,925
0.938
0.362
−38,578
100,354...

Question 1
How is a residual calculated in a regression model? i.e. what is
the meaning of a residual?
a)The difference between the actual value, y, and the fitted
value, y-hat
b)The difference between the fitted value, y-hat, and the mean,
y-bar
c)The difference between the actual value, y, and the mean,
y-ba
d)The square of the difference between the fitted value, y-hat,
and the mean, y-bar
Question 2
Larger values of r-squared imply that the observations are more
closely...

1. For the following multiple regression which was
conducted to attempt to predict the variable based on the
independent variables shown, answer the following
questions.
Regression Statistics
Multiple R
0.890579188
R Square
0.793131289
Adjusted R Square
0.7379663
Standard Error
30.28395534
Observations
20
ANOVA
df
SS
MS
F
Regression
4
52743.23074
13185.81
14.37743932
Residual
15
13756.76926
917.1179509
Total
19
66500
Coefficients
Standard Error
t Stat
P-value
Intercept
73.33291
62.25276
1.17799
0.25715
X1
-0.13882
0.05353
-2.59326
0.02037
X2
3.73984
0.95568
3.91328
0.00138...

1.Which of the following is true of Chow
test?
a.
It is a type of sign test.
b.
It is only valid under heteroskedasticity.
c.
It is only valid under homoskedasticty.
d.
It is a type of t test.
2.
Consider the following simple regression model
y = β0 +
β1x1 + u. Suppose
Corr(x,u) > 0, Corr(z,x)
> 0, and Corr(z,u) < 0. Then, the IV
estimator has a(n) _____.
a.
substantial bias
b.
downward bias
c.
asymptotic bias...

The statistical model
for simple linear regression is written as μy
= β0 +
β1*x, where μy
represents the mean of a Normally distributed response variable and
x represents the explanatory variable. The parameters
β0 and β1 are estimated,
giving the linear regression model defined by
μy = 70 + 10*x , with standard
deviation σ = 5.
(multiple choice
question)
What is the
distribution of the test statistic used to test the null hypothesis
H0 : β1 =
0...

The following computer printout estimated overhead costs using
multiple regression:
t for
H(0) Std.
error
Parameter Estimate Parameter
= 0 Pr >
t of
parameter
Intercept 1000
1.96 0.0250 510.204
Setup hours
35 81.96 0.0001 0.305
# of
parts 80 9.50 0.0001 10.527
R Square
(R2) 0.95
Standard Error (Se) 75.00
Observations 158
During the year the company used 900 setup hours and 500
parts.
A) Refer to Figure 3-3. The degrees of freedom for the model
is?
B) Refer to Figure 3-3. The model being measured is?
C) What is the predicted...

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