cause and effect relationship

no cause and effect relationship

relationship between the independent variable and dependent variable

functional relationship between two variables

independent of origin and scale

independent of origin and not of scale

independent of origin

independent of scale

karl pearson's method

spearman's method

product moment correlation

concurrent deviation method

the degree of variability between the two variables

the nature of the relationship between the two variables

functional relationship between two variables

all of the above

Square of the co-efficient of correlation

Probable error

Scatter diagram

Correlation graph

the average of the product of deviations taken from their averages

a is further divided by the product of their standard deviations

a is further divided by the product of their arithmetic averages

none of the above

a single variable

two variables

three variables

four variables

$4$

$0.5$

$1.0$

$3.32$

difference among two or more sets of data

relationship between variables

variations in the given data

randomness of samples

$0.9$

$0.7$

$0.8$

$1.0$

-0.5

0.57

0.8

None

independent of change of origin

independent of change of scale

independent of origin and not of scale

both (A) and (B)

less

more

equal

very high

5.8

2.9

6.18

4.32

Mean

Mode

Scatter diagram

Coefficient of correlation

Direction and intensity of relationship among variables

It measures covariation, not causation

Both A and B

None of the above

$0.16$

$0.40$

$0.89$

$0.30$

The sign of the regression coefficients are always the same.

Correlation coefficient is the geometric mean of the regression coefficients.

The co-variance between two variables divided by the product of their standard deviations produces the value of coefficient of correlation.

Coefficient of correlation is independent of origin but not of sale.

$0.40$

$0.80$

$0.08$

$0.04$

Correlation means when value of one variable changes, the other variable also changes in the same direction

Correlation studies and measures the direction and intensity of relationship among variables

A scatter diagram visually presents the nature of association without giving any specific numerical value

None of these

primary, secondary

negative, positive

same, opposite

none of the above

unity

linear

high

low

positive

negative

indirect

opposite

negative

direct

same

positive

correlation

negative correlation

direct relationship

positive correlation

Karl Pearsons coefficient of correlation

Scatter diagram

Spearmans rank correlation

All of these

Karl Pearson

Spearman

Sir Francis Golton

Kafta

(0.77)

(-0.77)

(0.99)

(-0.99)

low

unity

linear

none of the above

intersect each other at $90^o$

be positive slope

be negative slope

be convex to the origin

linear

unity

low

none

heights of fathers and sons

weights of fathers and sons

heights of mothers and daughters

weights of mothers and daughters

when one variable increases, the other variable decreases and vice-versa

when one variable increases, the other variable also increases and vice-versa

the study is made on more than two variables

the study is made on less than two variables

partial correlation

total correlation

linear correlation

nonlinear correlation

multiple correlation

negative correlation

linear correlation

non-linear correlation

relationship between price and supply of a commodity

relationship between price and demand for a commodity

relationship between income and saving

relationship between income and consumption

horizontal line

straight line

backward bending curve

'u' shaped curve

simple

multiple

partial

total

Product Moment Correlation

Spearman's Rank Correlation

Concurrent Deviation Method

Karl Pearson's Co-efficient of Correlation

give direction and degree of relationship between the two variables.

calculate the unknown value from a known value.

both (A) and (B).

calculate the known value from an unknown value.

only when the number of variables is smaller

only when the rank is given

to know the talent of the workers of two factories

all of the above

Square of the co-efficient of correlation

Probable error

Scatter diagram

Correlation graph

$r=\dfrac{S _{xy}}{\sqrt{S _xS _y}}$ ; S= Standard error

$ r = \dfrac {{\sigma} _{xy}}{{N}{\sigma} _{x}{\sigma} _{y}}$ ; N=number of variables

$ r = \dfrac {{-r}^2}{\sqrt{N}}$

$ r = \dfrac {{6}{\sigma}^2 _{{d}}}{n({n}^2 - {1})}$

$r = 1 - \dfrac{6\Sigma{{D} _2}}{{{N} _{3}} - N}$

$r = \dfrac{{\Sigma}{x}{y}}{{N}{\sigma}{x}{\sigma}{y}}$

$ r = \dfrac {{\sigma}{x}}{{\sigma}{y}}$

$ r = \dfrac {{\sigma}{y}}{{\sigma}{x}}$

it consumes more time

the value of correlation is affected by extreme values

if the data are not homogeneous, the co-efficient of correlation will give a wrong picture

all of the above

Probable error

the product moment method

concurrent deviation method

short-cut method

concurrent deviation method

correlation co-efficient

technical co-efficient

rank correlation

straight line

not a straight line

concave to the origin

convex to the origin

correlation

regression

sampling distribution

index number

the direction and degree of relationship between two variables

the direction and degree of relationship between two or more variables

the degree of variability between two and more variables

the functional relationship between two variables

act of returning back

act of stepping back

simply average relationship

all of the above

dependent variable

independent variable

both (A) and (B)

none of the above

negative slope

positive slope

running downwards from left to right

both (A) and (C)

be positive slope

move upwards from left to right

be negative slope

both (A) and (B)

Karl Pearson

Sir Francis Galton

M. Blair

Bowley

relationship between the independent variable and dependent variable

no relationship between the independent variable and dependent variable

no cause and effect relationship

none of the above

the degree of variability between the two variables

the direction of relationship between the two variables

the nature of relationship between the two variables

none of the above

act of returning back

method of studying correlation between two variables

the direction of change

simply average relationship

$0.16$

$0.40$

$0.89$

$0.30$

$\sqrt{byx \times bxy}$

$ byx = \dfrac{r sy}{sx}$

$bxy = \dfrac{r sx}{sy}$

none of the above

0 to (-) 1

0 to (-) 0

0 to (-) 0.05

0 to 1

simple correlation coefficient

product moment correlation

both A and B

none of the above

-0.16

-0.50

+0.40

-0.40

Scatter diagrams

Karl Pearsons coefficient of correlation

Spearmans rank correlation

All of these

11

-11

12

10

1,3,5,7,9........

2,4,8,16.........

3,2,4,5,1,3..........

1,3,4,7,6........

total wages will go down in the ratio of 90:84

total wages will go up in the ratio of 84:90

total wages will go up by 5%

no changes in total wages will

-3

1

-1/2

-1/3

0.9

0.7

0.8

1.0

association

location

relative location

variability

b $\space$ d $\space$ a $\space$ b

b $\space$ a $\space$ c $\space$ d

a $\space$ b $\space$ c $\space$ d

d $\space$ b $\space$ c $\space$ a

the product of their standard deviations

the square root of the product of their regression co-efficient

the co-variance between the variables

none of the above

co-efficient of correlation between two sample groups

co-efficient of correlation among more than two sample groups

average between two sample groups

averages of more than two sample groups

$(a) - 4, (b) - 3, (c) - 2, (d) - 1$

$(a) - 4, (b) - 3, (c) - 1, (d) - 2$

$(a) - 4, (b) - 2, (c) - 3, (d) - 1$

$(a) - 4, (b) - 2, (c) - 1, (d) - 3$

Scatter diagram

Spearmans coefficient of correlation

Karl Pearsons Coefficient of Correlation

None