Question

if the val of the samp. coeff of correlation is neg, the estimated slope parameter must be positve. T or F

The total sum of square(s) in a regresssion mod will neverr exceed the regresssion sum of squares T or F

Answer #1

1) False

Slope represents the direction of the regression equation line, if it is positive then association between the variables is also positive. If slope is negative then there is negative correlation exists between the variables.

2) False

SST = SST + SSE

Total sum of square = regression sum of square + error sum of square

Since total sum of square is addition of regression sum of square and error sum of square , it obvious that it is greater than regression sum of square.

Serial correlation (or autocorrelation) causes estimates of
the
slope parameter β to be overestimated on average.
estimates of the standard errors to be understated on
average.
estimates of the T values of be understated on everage.
slope parameter β to be understated on average.

X Y 1 50 8 57 11 43 16 18 20 18 Use the estimated regression
equation is y = 61.5 – 2.21x. A). Compute the Mean Square Error
Using Equation. S^2 = MSE = SSE/(n-2) B). Compute The Standard
Error of the estimate using equation. S = SQRT(MSE) =
SQRT[SSE/(n-2)] C). Compute the estimated Standard Deviation of b1
using equation. Sb1 = S/SQRT[SUM(x-(x-bar))^2] D). Use the t-test
to test the following hypothesis (α = 0.05) H0: β1 = 0...

__________ refers to the degree of correlation among independent
variables in a regression model.
a. Multicollinearity
b. Confidence level
c. Rank
d. Tolerance
__________ is used to test the hypothesis that the values of the
regression parameters ß1,
ß2,
...ßq are all zero.
a. Extrapolation
b. A t test
c. An F test
d. The least squares method
What would be the coefficient of determination if the total sum
of squares (SST) is 23.29 and the sum of squares due...

Given are five observations for two variables, x and
y.
xi
1
2
3
4
5
yi
4
7
6
11
14
The estimated regression equation is = 1.2 + 2.4
x.
Compute the mean square error using the following equation (to
3 decimals).
Compute the standard error of the estimate using the following
equation (to 3 decimals).
Compute the estimated standard deviation b
1 using the following equation (to 3 decimals).
Use the t test to test the following hypotheses...

f. Interpret the slope coefficient for one of the dummy
variables included in your regression model.
g. For the slope coefficient of the variable with the smallest
slope coefficient (ignore sign, use absolute value), test to see if
the “a priori” expectation from part (a) is confirmed. Use alpha =
0.05.
h. Interpret the coefficient of determination in this
situation.
i. Test the explanatory power of the entire regression model.
Please use alpha = 0.01.
j. For the variable with...

Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.816
.666
.629
1.23721
a. Predictors:
(Constant),x
ANOVA
Model
Sum of Squares
df
Mean Square
F
Sig
Regression
Residual
Total
27.500
13.776
41.276
1
9
10
27.500
1.531
17.966
.002b
a. Dependent Variable: Y
b. Predictors: (Constant), X
Coefficients
Model
Understand Coefficients
B
Std Error
Standardized
Coefficients
Beta
t
Sig
1 (Constant)
x
3.001
1.125
.500
.118
.816
2.667...

Given are five observations for two variables, x and y .
xi
1
2
3
4
5
yi
53
58
47
21
11
Use the estimated regression equation is y-hat = 78.01 -
3.08x
A.) Compute the mean square error using
equation.
s^2 = MSE = SSE / n -2
[ ] (to 2 decimals)
B.) Compute the standard error of the estimate
using equation
s = sqrtMSE = sqrt SSE / n - 2
[ ] (to 2 decimals)...

A firm estimated its short-run costs using an average variable
cost function of the form
AVC = a + bQ +
cQ2
And obtained the following results. Total fixed cost is
$1,500.
DEPENDENT VARIABLE:
AVC
R-SQUARE
F-RATIO
P-VALUE ON F
OBSERVATIONS:
40
0.8273
88.65
0.0001
VARIABLE
PARAMETER ESTIMATE
STANDARD ERROR
T-RATIO
P-VALUE
INTERCEPT
38.05
11.87
3.21
0.0028
Q
-4.20
1.56
-2.69
0.106
Q2
0.30
0.09
3.33
0.0020
The estimated marginal cost function is:
MC = 38.05 – 8.4Q +
0.9Q2...

The chief economist for Argus Corporation, a large appliance
manufacturer, estimated the firm’s short- run cost function for
vacuum cleaners using an average variable cost function of the
form
AVC = a + bQ + cQ2
where AVC dollars per vacuum cleaner and Q number of vacuum
cleaners produced each month. Total fixed cost each month is
$180,000. The following results were obtained:
DEPENDENT VARIABLE: AVC
R-SQUARE F-RATIO
P-VALUE ON F
OBSERVATIONS: 19
0.7360
...

Based on the charts below, Determine whether a
statistically reliable oil consumption model can be estimated
Variables
Entered/Removeda
Model
Variables Entered
Variables Removed
Method
1
Number People, Home
Index, Degree Days, Customerb
.
Enter
a. Dependent Variable:
Oil Usage
b. All requested
variables entered.
Model
Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
1
.889a
.790
.766
85.445
a. Predictors:
(Constant), Number People, Home Index, Degree Days, Customer
ANOVAa
Model
Sum of Squares
df
Mean Square...

ADVERTISEMENT

Get Answers For Free

Most questions answered within 1 hours.

ADVERTISEMENT

asked 29 minutes ago

asked 1 hour ago

asked 1 hour ago

asked 1 hour ago

asked 2 hours ago

asked 2 hours ago

asked 2 hours ago

asked 2 hours ago

asked 2 hours ago

asked 2 hours ago

asked 3 hours ago

asked 3 hours ago