Question
In: Statistics and Probability
a. Upon reviewing the results of a multiple regression involving 30 observations on cross sectional data...
a. Upon reviewing the results of a multiple regression involving 30 observations on cross sectional data the Econometric Society suspected that the variances of the error terms might be related to the predicted values of the dependent variable. Alarmed by the prospects they quickly ran a simple regression yielding the following results:
ei 2 = 10.956 + 3.068?̂? R 2 = 0.096
Using hypothesis testing, test at a 5% level to see if the variance of error terms is related to the predicted values of the dependent variable. - State hypotheses, calculated and critical values for the test statistic, and conclusion.
Solutions
orchestra answered 2 years agoRelated Solutions
a. Upon reviewing the results of a multiple regression involving 30 observations on cross-sectional data the...
a. Upon reviewing the results of a multiple regression involving
30 observations on cross-sectional data the Econometric Society
suspected that the variances of the error terms might be related to
the predicted values of the dependent variable. Alarmed by the
prospects they quickly ran a simple regression yielding the
following results:
ei2 = 10.956 + 3.068?̂? R2 = 0.096
Using hypothesis testing, test at a 5% level to see if the variance
of error terms is related to the predicted...
When estimating a multiple linear regression model based on 30 observations, the following results were obtained....
When estimating a multiple linear regression model based on 30
observations, the following results were obtained. [You may
find it useful to reference the t table.]
Coefficients
Standard Error
t Stat
p-value
Intercept
153.08
122.34
1.251
0.222
x1
12.64
2.95
4.285
0.000
x2
2.01
2.46
0.817
0.421
a-1. Choose the hypotheses to determine whether
x1 and y are linearly
related.
H0: β0 ≤ 0;
HA: β0 > 0
H0: β1 ≤ 0;
HA: β1 > 0
H0: β0 = 0;...
In a regression analysis involving 30 observations, the following estimated regression equation was obtained. ŷ =...
In a regression analysis involving 30 observations, the
following estimated regression equation was obtained.
ŷ = 17.6 + 3.2x1 − 2.2x2 +
7.6x3 + 2.7x4
1. Interpret b1 in this estimated regression
equation. (choose an option)
a)b1 = −2.2 is an estimate of the change in
y corresponding to a 1 unit change in
x1 when x2,
x3, and x4 are held
constant.
b)b1 = 7.6 is an estimate of the change in
y corresponding to a 1 unit change...
Hi, I need to have a cross sectional data in order to do regression analysis. and...
Hi,
I need to have a cross sectional data in order to do regression
analysis. and the data should be as follows. and i cant find the
data online.
note: the data can be for any product
Observation
I
Quantity Demanded/Sold
Y
Price
X1
Income/person
X2
Price of Substitutes
X3
Price of Complements
X4
1
265.2
12.0
6
17
0
2
279.6
20.2
7
18
0
3
311.2
27.0
7
17
1
4
328.0
30.0
8
18
1
5
352.0...
) Consider the following regression results based on 30 observations. y = 238.33 – 0.95x1 +...
) Consider the following regression results based on 30
observations.
y = 238.33 – 0.95x1 + 7.13x2 + 4.76x3; SSE = 3,439
y = 209.56 – 1.03x1 + 5.24(x2 + x3); SSE = 3,559
a. Formulate the hypotheses to determine whether the influences
of x2 and x3 differ in explaining y.
b. Calculate the value of the test statistic.
c. At the 5% significance level, find the critical value(s).
d. What is your conclusion to the test?
why cross sectional regression rule out perfect collinearity among the regressors
why
cross sectional regression rule out perfect collinearity among the
regressors
Exercise 15-6 Algo When estimating a multiple linear regression model based on 30 observations, the following...
Exercise 15-6 Algo
When estimating a multiple linear regression model based on 30
observations, the following results were obtained. [You may
find it useful to reference the t table.]
Coefficients
Standard Error
t Stat
p-value
Intercept
153.35
126.57
1.212
0.236
x1
11.10
2.62
4.237
0.000
x2
2.36
2.06
1.146
0.261
a-1. Choose the hypotheses to determine whether
x1 and y are linearly related.
H0: β1 = 0;
HA: β1 ≠ 0
H0: β0 = 0;
HA: β0 ≠ 0
H0:...
In a multiple linear regression with 40 observations, the following sample regression equation is obtained: yˆy^...
In a multiple linear regression with 40 observations, the
following sample regression equation is obtained:
yˆy^ = 12.5 + 2.4x1 − 1.0x2
with se = 5.41. Also, when
x1 equals 16 and x2 equals
5, se(yˆ0)se(y^0) = 2.60.
[You may find it useful to reference the t
table.]
a. Construct the 95% confidence interval for
E(y) if x1 equals 16 and
x2 equals 5. (Round intermediate
calculations to at least 4 decimal places,
"tα/2,df" value to 3 decimal places, and...
The following is the estimation results for a multiple linear regression model: SUMMARY OUTPUT Regression...
The following is the estimation results for a multiple linear
regression model:
SUMMARY OUTPUT
Regression Statistics
R-Square
0.558
Regression Standard Error
(S)
863.100
Observations
35
Coeff
StdError
t-Stat
Intercept
1283.000
352.000
3.65
X1
25.228
8.631
X2
0.861
0.372
Questions:
Interpret each coefficient.
The following is the estimation results for a multiple linear regression model: SUMMARY OUTPUT Regression...
The following is the estimation results for a multiple linear
regression model:
SUMMARY OUTPUT
Regression Statistics
R-Square
0.558
Regression Standard Error
(S)
863.100
Observations
35
Coeff
StdError
t-Stat
Intercept
1283.000
352.000
3.65
X1
25.228
8.631
X2
0.861
0.372
Question:
1.
A. Write the fitted regression equation.
B. Write the estimated intercepts and slopes, associated with
their corresponding standard errors.
C. Interpret each coefficient.
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