Question
In: Statistics and Probability
true or fulse q1 In a regression model it is given that the estimate of intercept...
true or fulse
q1
In a regression model it is given that the estimate of intercept is 5 and the estimate of slope is 4 , then the value of dependent variable y for x = 2 is 14.
True
False
q2
When looking at the waiting line at SEU daam system, we can assume that calling population is limited. True
False
q3
A coffee machine can serve customers at the rate of 20 per hour . The customers arrive at the rate of 10 per hour. The probability of more than one customer in the system is 0.25.
True
False
q4
If the coefficient of determination for some data is 0.3, then the correlation coefficient is
True
False
q5
If we make changes in the technological coefficient from 3x +2y ≤ 50 to 6x + 2y ≤ 50, it may cause a change in the optimal solution.
True
False
q6
The customer who arrives at a bank , sees a long line , and leaves to return another time is called balking.
True
False
q7
A vendor selling vegetables on a street corner is an example of a multi-channel, single-phase system.
True
False
q8
The Graphical Method in Linear programming Problem can also work when there are more than two decision variables.
True
False
Solutions
orchestra answered 1 year agoRelated Solutions
a) State two possible consequences of not including an intercept in an estimated regression model
Question
a) State two possible consequences of not including an intercept
in an estimated regression model
b) When is heteroscadasticity said to occur in a regression
model?
c)State the steps involved in conducting the Goldfeld-Quandt
test
d)State the consequences of using OLS in the presence of
heteroscedasticity
e)State two solutions for heteroscedasticity
f)State the conditions that must be fulfilled for Durban -Watson
test to be a valid test
g)Why mighy lags be required in a regression model?
h) State two...
1) True or False? In multinomial logistic regression analysis, the model fit is given by the...
1) True or False?
In multinomial logistic regression analysis, the model fit is
given by the value of R-squared.
In linear regression analysis, the overall significance test is
an F test.
In logistic regression analysis, the overall significance test
is an F test.
When building a model for regression analysis, the type of data
for the outcome variable
guides you to choose linear, logistic, or multinomial
logistic.
Two variables that covary might not have a causal
relationship.
Causation implies correlation....
You estimate a model with 9 explanatory variables and an intercept from a data set with...
You estimate a model with 9 explanatory variables and an
intercept from a data set with 100 observations. To test hypotheses
on this model you should use a t-distribution with how many degrees
of freedom?
Select one:
a. 1 b. 10 c. 9 d. 90
True or False, The y-intercept in the linear regression equation describes the average outcome of y...
True or False, The y-intercept in the linear regression equation
describes the average outcome of y when x = 1.
True or false, The slope in the linear regression equation
describes the estimated difference in the y-variable if the
explanatory variable x for a case happened to be one unit
larger.
True or false, If the 95% confidence interval for the population
slope is given by 0.22 to 0.33, then the sample slope is 0.275. As
one increases the sample...
6. In the simple linear regression model, the y-intercept represents the: a. change in y per...
6. In the simple linear regression model, the y-intercept represents the:a. change in y per unit change in x.b. change in x per unit change in y.c. value of y when x=0.d. value of x when y=07. In the simple linear regression model, the slope represents the:a. value of y when x=0.b. average change in y per unit change in x.c. value of x when y=0.d. average change in x per unit change in y.8. In regression analysis, the residuals...
1.-Interpret the following regression model Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -7.819e+05 7.468e+04 -10.470...
1.-Interpret the following regression model
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.819e+05 7.468e+04 -10.470 < 2e-16 ***
Lot.Size -5.359e-01 1.163e-01 -4.610 4.67e-06 ***
Square.Feet 1.108e+02 1.109e+01 9.986 < 2e-16 ***
Num.Baths 2.985e+04 9.650e+03 3.094 0.00204 **
API.2011 1.226e+03 9.034e+01 13.568 < 2e-16 ***
dis_coast -7.706e+00 2.550e+00 -3.022 0.00259 **
dis_fwy 1.617e+01 1.232e+01 1.312 0.18995
dis_down 5.364e+00 3.299e+00 1.626 0.10429
I(dis_fwy * dis_down) -4.414e-04 5.143e-04 -0.858 0.39098
Pool 1.044e+05 2.010e+04 5.194 2.59e-07 ***
---
Signif. codes: 0 ‘***’ 0.001...
When we estimate a linear multiple regression model (including a linear simple regression model), it appears...
When we estimate a linear multiple regression model (including a
linear simple regression model), it appears that the calculation of
the coefficient of determination, R2, for this model can be
accomplished by using the squared sample correlation coefficient
between the original values and the predicted values of the
dependent variable of this model.
Is this statement true? If yes, why? If not, why not? Please use
either matrix algebra or algebra to support your reasoning.
For a regression with log(bweight) as the dependent variable, we have Variable Estimate Standard Error Constant/Intercept...
For a regression with log(bweight) as the dependent variable, we
have
Variable
Estimate
Standard Error
Constant/Intercept
4.66
0.029
Male
0.03
0.010
Motheduc
0.002
0.002
lfaminc
0.02
0.006
You expect an increase in family income to increase birth
weight. Test this hypothesis at the 1% level of significance. Make
sure you explicitly include all of the steps.
Estimate a multiple linear regression relationship with the U.K. stock returns as the dependent variable (intercept), and RBUK, U.S.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.727076179
R
Square
0.528639771
Adjusted R Square
0.525504337
Standard Error
3.573206748
Observations
455
ANOVA
df
SS
MS
F
Significance F
Regression
3
6458.025113
2152.67504
168.601791
2.7119E-73
Residual
451
5758.280717
12.7678065
Total
454
12216.30583
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 99.0%
Upper 99.0%
Intercept
-0.250148858
0.359211364
-0.6963835
0.48654745
-0.9560846
0.45578693
-1.1793476
0.67904987
RBUK
0.025079378
0.023812698
1.05319345
0.29281626
-0.0217182
0.07187699
-0.0365187
0.08667745
RSUS
0.713727515
0.042328316
16.8617037
8.0578E-50
0.6305423
0.79691273
0.60423372
0.82322131...
How do you create a linear regression model with any intercept using Matrix operations. The following...
How do you create a linear regression model with any intercept
using Matrix operations. The following points are: (x0, x1, x2, y):
(1, 2, 3, 15), (1, 4, 5, 23), (1, 1, 2, 8), and (1, 3, 5, 21).
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