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Regression Analysis

Total questions: 15

Worksheet time: 18mins

Name
Class
Date
1.

Regressionanalysis is the measure of .......................relationship between variables

a)

nature

b)

independence

c)

cause and effect

d)

none of these

2.

Correlation coefficientcis equal to ........................of regression coefficients.

a)

AM

b)

GM

c)

AVERAGE

d)

SUM

3.

If bxybxy   is greater than 1 then 

 byxbyx  is..................

a)

greater than 1

b)

less than 1

c)

equal to 1

d)

equal to -1

4.

 

the range of regression coefficient is.................

a)

 -\infty  to 0

b)

    to  -\ \infty\ \ to\ \ \infty  

c)

-1  to     +1

d)

 1 to 1\ to\ \infty  

5.

if r=0.5     bxybxy  =1   then  byxbyx  = (a)  

6.

 

if r=0.5       byxbyx  =0.5 then   bxybxy  = (a)  

7.

if     bxybxy  =2 and    byxbyx  =0.5 then  r= (a)  

8.

the term regression was introduced by....................

a)

R A Fischer

b)

Sir Francis Galton

c)

Karl Pearson

d)

None of these

9.

In a regression line of Yon X the variable Xis known as

a)

independent variables

b)

regressior

c)

explanatory variable

d)

all of the above

10.

The regression line of Y on X is 5x-7y=10, then the regression coefficient of Y on X is

a)

57\frac{5}{7}

b)

75\frac{7}{5}

c)

75\frac{-7}{5}

d)

107\frac{10}{7}

11.

A linear regression (LR) analysis produces the equation Y = 3 + 0.4X. This indicates that:

a)

When Y = 0.4, X = 3

b)

When Y = 0, X = 3

c)

When X = 3, Y = 0.4

d)

When X = 0, Y = 3

12.

Linear regression is a statistical regression method which is used for

a)

Predictive Analysis

b)

Real Analysis

c)

Complex Analysis

d)

Functional Analysis

13.

12.How many coefficients do you need to estimate in a simple linear regression model (One independent variable)?

a)

1

b)

2

c)

3

d)

4

14.

14.In regression analysis, the variable that is being predicted is;

a)

a) the independent variable

b)

b) the dependent variable

c)

c) usually denoted by x

d)

d) usually denoted by r

15.

When r=0 the regression lines are

a)

coincident

b)

perpendicular

c)

parallel

d)

none of these