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Question 1

The strength (degree) of the correlation between a set of independent variables X and a dependent variable Y is measured by

A

Coefficient of Correlation

B

Coefficient of Determination

C

Standard error of estimate

D

All of the above

Question 2

The percent of total variation of the dependent variable Y explained by the set of independent variables X is measured by

A

Coefficient of Correlation

B

Coefficient of Skewness

C

Coefficient of Determination

D

Standard Error or Estimate

E

Multicollinearity

Question 3

A coefficient of correlation is computed to be -0.95 means that

A

The relationship between two variables is weak.

B

The relationship between two variables is strong and positive

C

The relationship between two variables is strong and but negative

D

Correlation coefficient cannot have this value

Question 4

Let the coefficient of determination computed to be 0.39 in a problem involving one independent variable and one dependent variable. This result means that

A

The relationship between two variables is negative

B

The correlation coefficient is 0.39 also

C

39% of the total variation is explained by the independent variable

D

39% of the total variation is explained by the dependent variable

Question 5

Relationship between correlation coefficient and coefficient of determination is that

A

both are unrelated

B

The coefficient of determination is the coefficient of correlation squared

C

The coefficient of determination is the square root of the coefficient of correlation

D

both are equal

Question 6

Multicollinearity exists when

A

independent varialbes are correlated less than -0.70 or more than 0.70

B

An independent variables is strongly correlated with a dependent variable.

C

There is only one independent variable

D

The relationship between dependent and independent variable is non-linear

Question 7

If "time" is used as the independent variable in a simple linear regression analysis, then which of the following assumption could be violated

A

There is a linear relationship between the independent and dependent variables

B

The residual variation is the same for all fitted values of Y

C

The residuals are normally distributed

D

Successive observations of the dependent variable are uncorrelated

Question 8

In multiple regression, when the global test of significance is rejected, we can conclude that

A

All of the net sample regression coefficients are equal to zero

B

All of the sample regression coefficients are not equal to zero

C

At least one sample regression coefficient is not equal to zero

D

The regression equation intersects the Y-axis at zero.

Question 9

A residual is defined as

A

$Y-\hat{Y}$

B

Error sum of square

C

Regression sum of squares

D

Type I Error

Question 10

What test statistic is used for a global test of significance?
http://itfeature.com

A

Z test

B

t test

C

Chi-square test

D

F test

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Question 1

Correlation Coefficient values lies between

A

-1 and +1

B

0 and 1

C

-1 and 0

D

None of these

Question 2

If r_{xy} = -0.84 then r_{yx}=?

A

-0.84

B

0.84

C

0.42

D

None of these

Question 3

In correlation both variables are always

A

Random

B

Non Random

C

Same

D

None

Question 4

If two variables oppose each other then the correlation will be

A

Positive Correlation

B

Zero Correlation

C

Perfect Correlation

D

Negative Correlation

Question 5

A perfect negative correlation is signified by

A

0

B

1

C

0.5

D

-1

Question 6

The correlation coefficient between U=X and V=-X is

A

+1

B

-1

C

0

D

0.5

Question 7

The correlation coefficient between X and X is

A

-1 to +1

B

0

C

-1

D

1

Question 8

The correlation coefficient r is independent of

A

Origin only

B

Scale of Measurement only

C

Both change of origin and scale of measurement

D

None of these

Question 9

If X and Y are independent to each other, the coefficient of correlation is

A

-1

B

0

C

+1

D

None

Question 10

If b_{yx }<0 and b_{xy} =<0, then r is

A

=0

B

<0

C

>0

D

≠ 0

Question 11

If r=0.6, b_{yx}=1.2 then b_{xy}=?

A

0.3

B

0.2

C

0.72

D

0.40

Question 12

When regression line passes through the origin then

A

Regression coefficient is zero

B

Correlation is zero

C

Intercept is zero

D

Association is zero

Question 13

Two regression lines are parallel to each other if their slope is

A

Different

B

Same

C

Negative

D

None of these

Question 14

When b_{xy} is positive, then b_{yx} will be

A

Positive

B

Negative

C

Zero

D

One

Question 15

If $\hat{Y}=a\,$ then r_{xy}

A

$b_{xy}=1$

B

$b_{yx}=-1$

C

$b_{yx}=0$

D

None of these

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Question 1

For a Least Square trend $\hat{Y}=\alpha +\beta X$

A

$\sum Y =\sum \hat{Y}$

B

$\sum \hat{Y}=0$

C

$\sum Y< \sum \hat{Y}$

D

$\sum Y > \sum \hat{Y}$

E

None

Question 2

In Regression Analysis $\sum \hat{Y}$ is equal to

A

0

B

ΣY

C

a

D

bX

E

None

Question 3

The best fitting trend is one for which the sum of squares of error is

A

Zero

B

Minimum (Least)

C

Maximum

D

None

Question 4

If all the values fall on the same straight line and the line has a positive slope then what will be the value of the correlation coefficient ‘r’:

A

0 ≤ r ≤ 1

B

r ≥ 0

C

r = +1

D

r = -1

E

0

Question 5

In a Least Square Regression line the quantity $\sum(Y-\hat{Y})\,$ is always

A

Negative

B

Zero

C

Positive

D

Fractional

E

None

Question 6

Which one is equal to explained variation divided by total variation?

A

Sum of squares due to regression

B

Coefficient of Determination

C

Standard Error of Estimate

D

Coefficient of Correlation

Question 7

In Least Square Regression Line $\sum(Y-\hat{Y})^2\,$ is always

A

Negative

B

Zero

C

Non-Negative

D

Fractional

E

None

Question 8

Regression Line always passes through

A

(X, Y)

B

$(\alpha, \beta)$

C

$(\bar{X},\bar{Y})$

D

$(\bar{X},Y)$

E

None

Question 9

In Regression Analysis two regression lines intersect at the point

A

(0, 0)

B

(α, α)

C

(X, Y)

D

$(\bar{X},\bar{Y})$

E

None

Question 10

If a straight line is fitted to data, then

A

$\sum Y = \sum \hat{Y}$

B

$\sum Y > \sum \hat{Y}$

C

$\sum Y < \sum \hat{Y}$

D

$\sum(Y-\hat{Y})^2=0$

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Question 1

In the model $Y= mX+ a$,Y is also known as the

A

Predicted (dependent) variable

B

Independent variable

C

Explanatory variable

D

Predictor variable

Question 2

If the equation of regression line is $y = 5$, then what result will you take out from it?

A

The line passes through (5, 0)

B

The line is parallel to x-axis

C

The line passes through origin

D

The line is parallel to y-axis

Question 2 Explanation:

$y=k$. for one value of y there are infinite values of x

Student and Instructor of Statistics and business mathematics.
Currently Ph.D. Scholar (Statistics), Bahauddin Zakariya University Multan.
Like Applied Statistics and Mathematics and Statistical Computing.
Statistical and Mathematical software used are: SAS, STATA, GRETL, EVIEWS, R, SPSS, VBA in MS-Excel.
Like to use type-setting LaTeX for composing Articles, thesis etc.

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TEST 4, Q.1 – I THINK THE ANSWER SHOULD BE (D)-PARALLEL TO X-AXIS

Test 4, Q.7. Thank you. Correction is made.

as I think You should explain the answers too.

SO NICE

THANKS

VERY HELPFULL