Congratulations - you have completed Correlation and Regression Analysis.
You scored %%SCORE%% out of %%TOTAL%%.
Your performance has been rated as %%RATING%%

Your answers are highlighted below.

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

Once you are finished, click the button below. Any items you have not completed will be marked incorrect.
Get Results

There are 10 questions to complete.

You have completed

questions

question

Your score is

Correct

Wrong

Partial-Credit

You have not finished your quiz. If you leave this page, your progress will be lost.

Congratulations - you have completed MCQs on Correlation and Regression Analysis.
You scored %%SCORE%% out of %%TOTAL%%.
Your performance has been rated as %%RATING%%

Your answers are highlighted below.

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

Once you are finished, click the button below. Any items you have not completed will be marked incorrect.
Get Results

There are 15 questions to complete.

You have completed

questions

question

Your score is

Correct

Wrong

Partial-Credit

You have not finished your quiz. If you leave this page, your progress will be lost.

Congratulations - you have completed MCQs on Correlation and Regression Analysis III.
You scored %%SCORE%% out of %%TOTAL%%.
Your performance has been rated as %%RATING%%

Your answers are highlighted below.

Question 1

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 2

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

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

Question 3

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 4

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 5

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 6

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

A

Negative

B

Zero

C

Non-Negative

D

Fractional

E

None

Question 7

Regression Line always passes through

A

(X, Y)

B

$(\alpha, \beta)$

C

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

D

$(\bar{X},Y)$

E

None

Question 8

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 9

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

A

0

B

ΣY

C

a

D

bX

E

None

Question 10

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

A

Zero

B

Minimum (Least)

C

Maximum

D

None

Once you are finished, click the button below. Any items you have not completed will be marked incorrect.
Get Results

There are 10 questions to complete.

You have completed

questions

question

Your score is

Correct

Wrong

Partial-Credit

You have not finished your quiz. If you leave this page, your progress will be lost.

Online test on MCQs about Correlation and Regression Analysis by http://itfeature.com

Start

Congratulations - you have completed MCQs Correlation and Regression Analysis.
You scored %%SCORE%% out of %%TOTAL%%.
Your performance has been rated as %%RATING%%

Keep Visiting http://itfeature.com

Your answers are highlighted below.

Question 1

The method of least squares finds the best fit line that _____ the error between observed and estimated points on the line

A

Maximize

B

Reduces to zero

C

Minimize

D

Approaches to infinity

Question 2

If $R^2$ is zero, that is no collinearity/ Multicollinearity, the variance inflation factor (VIF) will be

A

Zero

B

One

C

Two

D

None of the above

Question 2 Explanation:

$VIF = \frac{1}{1-R^2}$

Question 3

The sample coefficient of correlation

A

Has the same sign as the slope, i.e. $\beta$

B

Can range from -3.00 up to 2.00

C

Can range from -1.00 up to 2.00

D

Is also called Peterson's $r$

Question 4

In regression equation $y = a + \beta x + e$, both x and y variables are

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.

Chi-square test is a non-parametric test. The assumption of normal distribution in the population is not required for this test. The statistical technique chi-square can be used to find the association (dependencies) between sets of two or more categorical variables by comparing how close the observed frequencies are to the expected frequencies. In other words, […]

Mean: Measure of Central Tendency The measure of Central Tendency Mean (also know as average or arithmetic mean) is used to describe the data set as a single number (value) which represents the middle (center) of the data, that is average measure (performance, behaviour, etc) of data. This measure of central tendency is also known […]

Probability sampling In probability each unit of the population has known (non-zero) probability of being included in the sample and samples are selected randomly by using some random selection method. That’s why, probability sampling may also be called random sampling. In probability sampling reliability of the estimates can be determined. In probability sampling, samples are […]

It is often required to collect information from the data. There two methods for collecting the required information. Complete information Sampling Complete Information In this method the required information are collected from each and every individual of the population. This method is used when it is difficult to draw some conclusion (inference) about the population […]

Research is inquiry. It is a process of discovering some new knowledge, that involves multiple elements such as theory development and testing, empirical inquiry, and sharing the generated knowledge with others such as experts and colleagues. A short description about elements of theory is: Theory is a set of ideas and perceptions that helps people […]

Question 1: How can I retrieve (load) the work that is saved using history function in R? Answer: The loadhistory() function will load an “.Rhistory“file. > loadhistory(“d:/file_name.Rhistory”) This function will load file named “file_name.Rhistory” from D: drive. The other way may be to access .Rhistory file through the file menu. For this click File and … Continue reading “R Basics”

Question: How one can get help about different command in R Language? Answer: There are many ways to get help about different command (functions). R has built-in help facility which is similar to man facility in Unix. For beginners of R language, help() function or ? can be used to get help about different commands … Continue reading “R FAQs: Getting Help in R”

Question: Can I save my work in R Language? Answer: R language facilitates to save ones R work. Question: How to save work done in R? Answer: All of the objects and functions that are created (you R workspace) can be saved in a file .RData by using the save() function or the save.image() function. … Continue reading “R FAQs: Saving and Loading R workspace”

Question: What are the differences of missing values in R and other Statistical Packages? Answer: Missing values (NA) cannot be used in comparisons, as already discussed in previous post on missing values in R. In other statistical packages (softwares) a “missing value” is assigned some code either very high or very low in magnitude such … Continue reading “R FAQs: Handling Missing values in R”

Question: What is matrix in R? Answer: In R language matrices are two dimensional arrays of elements all of which are of the same type, for example numbers, character strings or logical values. Matrices may be constructed using the built in function “matrix”, which reshapes its first argument into a matrix having specified number of … Continue reading “R FAQS about Matrix | Data Structure for Matrix in R”

Question: Can missing values be handled on R? Answer: Yes, in R language one can handle missing values. The way of dealing with missing values is different as compared to other statistical softwares such as SPSS, SAS, STATA, EVIEWS etc. Question: What is the representation of missing values in R Language? Answer: In R missing … Continue reading “R FAQ missing values”

nice exercise

GOOD JOB FOR INTELGENT STU.

😯

jazakALLAH…….. very good work

Quite informative and good source of quick learning and assessing one’s self…

Salam Sir,

Sir l am going to appear in lecturer test therefore I need your kind help

Please share some books for FPSC test.

Informative but if you add reference of answers, it will be appreciable