Loyola College Econometrics Previous Question Papers Download Econometrics

Loyola College M.Sc. Statistics Nov 2003 Econometrics Question Paper PDF Download

by

LOYOLA COLLEGE (AUTONOMOUS), CHENNAI –600 034

M.Sc., DEGREE EXAMINATION – STATISTICS

THIRD SEMESTER – NOVEMBER 2003

ST-3950/S919 – ECONOMETRICS

12.11.2003                                                                                                           Max:100 marks

1.00 – 4.00

SECTION-A

Answer ALL questions.                                                                                   (10X2=20 marks)

 

  1. What is meant by a generalized least square estimator?
  2. Explain auto regressive process.
  3. What are lagged variables?
  4. What are the properties of OLS estimators of a linear model Y =u?
  5. What is multi collinearity?
  6. Explain specification error.
  7. What is the auto correlation?
  8. What are the reasons for auto correlation disturbances?
  9. What are the sources of non spherical disturbances?
  10. Explain the homoscedasticity property.

 

SECTION-B

Answer any FIVE questions.                                                                           (5X8=40 marks)

 

  1. Consider the linear Y =u with E (u) = 0 and E(uu!) = Prove that an unbiased estimate of  is given by  where r is the residual vector.
  2. Derive the MLE of the parameters of the linear regression model Y =
  3. Derive the variance – covariance matrix of the autocorrelated disturbance terms?
  4. Explain in detail the concept of multi cotnearity.
  5. Explain the effect of excluding the relevant variables is the linear model Y =
  6. Explain clearly the concept of hetroscedasticity property.
  7. Explain the concept of structural change.
  8. Write short notes on (i) dummy variables (ii) seasonal adjustment.

 

SECTION-C

Answer any TWO questions.                                                                           (2X20=40 marks)

 

  1. For the general linear model Y =u, derive the least square estimator and

find Var  .

  1. a) State and prove gauses Markov theorem.
  2. b) Derive the test procedures to test the linear hypothesis H:Rb = S for the general linear

model.

  1. a) What are the properties of OLS estimators under Non spherical disturbances?
  2. b) Explain the Drubin – Watson test to test for auto correlation. (8+12)
  3. a) Explain cochrane – orcutt iterative estimation procedure used in the presence of

autocorrelated disturbance.

  1. b) Describe the ALMON Lag model to estimate the parameters of a distributed Lag

model.                                                                                                              (10+10)

 

 

Go To Main page

Loyola College M.Sc. Statistics April 2007 Econometrics Question Paper PDF Download

by

LOYOLA COLLEGE (AUTONOMOUS), CHENNAI – 600 034

AC 47

M.Sc. DEGREE EXAMINATION – STATISTICS

SECOND SEMESTER – APRIL 2007

ST 2950/3950 – ECONOMETRICS

 

 

 

Date & Time: 28/04/2007 / 1:00 – 4:00      Dept. No.                                       Max. : 100 Marks

 

 

 

Section A

Answer all questions.                                                                        (10*2=20)

  1. What is the difference between a linear and non-linear model?
  2. Give any two reasons for the inclusion of the ‘disturbance term’ in an econometric model.
  3. Interpret the following statement:

Pr{-0.25 < β2 < 1.3} = 0.95.

  1. Show that in a simple linear model the mean of the observed and estimated Y values are equal.
  2. What is ‘cross – section ‘data? Give an example.
  3. What is meant by ‘linear hypothesis’?
  4.  “In a linear model involving dummy variable, if there are ‘m’ categories for the dummy variable, use only ‘m-1’ independent variables”. Justify the above statement.
  5. Define the term ‘autocorrelation’.
  6. For a four variable regression model, the observed and estimated (under OLS) values of Y are given below:

Observed Y:   10         14        13        12        17

Estimated Y:  10         13        11        14        15

Calculate the standard error of the estimate.

  1. Mention any two consequences of multicollinearity.

 

Section B

Answer any five questions.                                                         (5*8=40)

 

  1. Mention the assumptions in the classical linear regression model.
  2. Suppose that a researcher is studying the relationship between gallons of milk consumed by a family per month (Y) and the price of milk each month (X in dollars per gallon). The sample consists of observations in 12 consecutive months. Analysis of the data reveals the following:

SY = 480;        SX=36;            Sxy=-440

Sx2 = 20;         = 528, where x and y are deviates of X and Y from their respective means.

For this sample, find the following:

  • Least squares intercept and slope
  • Standard error of regression.
  • Standard error of the slope.
  • Test the hypothesis that the slope coefficient is zero at 5% level.
  1. Calculate TSS, ESS, RSS and R2 for the following data assuming a linear model of Y on X.

Y:        12        10        14        13        16        14

X:        2          4          7          10        12        13

  1. Explain the concept of structural change with an example.
  2. What is meant by interval estimation? Derive a 100(1-α) % confidence interval for the slope parameter in a simple linear model.
  3. Explain the various methods of detecting multicollinearity.

 

 

 

  1. Consider the following OLS regression results with standard errors in

parenthesis:

S = 12,000 – 3000X1 + 8000(X1 + X2)

(1000)    (3000)                        n = 25

where S = annual salary of economists with B.A. or higher degree

X1 = 1 if M.A. is highest degree; 0 otherwise

X2 = 1 if Ph.D is highest degree; 0 otherwise

  • What is S for economists with a M.A. degree?
  • What is S for economists with a Ph.D degree?
  • What is the difference in S between M.A.’s and Ph.D’s?
  • At 5% level of significance, would you conclude that Ph.D’s earn more per year than M.A.’s?
  1. Explain the method of estimating the regression parameters in the presence of heteroscedasticity.

 

Section C

Answer any two questions.                                                   (2*20=40)

 

  1. a.) State and prove Gauss – Markov theorem.

b.) Show that for a ‘k’ variable regression model, the estimator

= (e1e)/(n-k) is unbiased for σ2.                                           (12+8).

  1. a.) Derive a test procedure for testing the linear hypothesis Rβ = r where R

is a known matrix of order q x k with q ≤ k and r is a known q x 1

vector.

b.) Explain the procedure for testing the equality of the slope coefficients of

two simple linear models using dummy variables.          (10+10)

  1. a.) Consider the following data on weekly income(Y), gender and status.

Y:        110      100      120      115      145      136      102      150

Gender:       1          1          0          1          0          0          1          0

Status:       0          0          1          1          1          0          1          0

where gender = 1 if male; 0 if female

Status = 1 if minor; 0 if major.

Assuming a linear model of Y on gender and status, estimate the

regression coefficients. Interpret the results.

b.) Define the following:

1.) Standard error of the estimate

2.) Ordinal data

3.) Variance Inflating Factor

4.) Coefficient of Determination.                                          (10+10)

  1. a.) For the following data, use Spearman’s rank correlation test to for the

presence of heteroscedasticity.

Y:        10        14        20        25        13        19        10        35

X:        1.3       2.1       2.5       3.0       1.7       1.9       1.0       2.9

b.) Explain the Breusch – Pagan – Godfrey test.                          (10+10).

 

 

 

Go To Main Page

 

Loyola College M.A. Economics April 2006 Econometrics Question Paper PDF Download

by

             LOYOLA COLLEGE (AUTONOMOUS), CHENNAI – 600 034

M.A. DEGREE EXAMINATION – ECONOMICS

RF 38

SECOND SEMESTER – APRIL 2006

                                                             EC 2807 – ECONOMETRICS

 

 

Date & Time : 26-04-2006/9.00-12.00         Dept. No.                                                       Max. : 100 Marks

 

 

PART – A

Answer any FIVE questions in about 75 words each.                   (5 x 4 = 20 Marks)

  1. Distinguish between ‘Mathematical Economics’ and ‘Econometrics’.
  2. What is meant by dummy variable trap?
  3. What is speciafication error?
  4. Define ‘Multicollinearity’.
  5. Define the stochastic error term in an econometric model.
  6. How do you use rank condition in identifying a simultaneous equation model?
  7. What is seasonal adjustment?

 

PART – B

Answer any FOUR questions in about 250 words each.               (4 x 10 = 40 Marks)

  1. Discuss the properties of a good estimator.
  2. Derive multicollinearity and its consequences and discuss the remedial measures.
  3. Derive the consequences of specification error.
  4. Define heteroscedasticity and explain its consequences.
  5. Derive the estimates vector under grouping of observations.
  6. Distinguish between error in the measurement of dependent variable and that of the independent variable.
  7. Discuss Almon’s transformation used in estimating a distributed lag model.

 

PART – C

Answer any TWO questions in about 900 words each.                 (2 x 20 = 40 Marks)

  1. Show that the efficiency of OLS estimate is less than that of GLS under GLS assumptions. (Use estimate of OLS and GLS variances)
  2. is one equation in a three equation model which contains three other exogenous variables  and . Observations gives the following matrices.

 

  1. Derive 2SLS estimates.
  2. Derive the following results:
  • e = Mu
  • e’e = u’Mu
  • E(e’e)=(n-k)s2

 

Go To Main Page

Loyola College M.A. Economics April 2007 Econometrics Question Paper PDF Download

by

LOYOLA COLLEGE (AUTONOMOUS), CHENNAI – 600 034

RF 16

M.A. DEGREE EXAMINATION – ECONOMICS

SECOND SEMESTER – APRIL 2007

EC 2807 – ECONOMETRICS

 

 

 

Date & Time: 24/04/2007 / 1:00 – 4:00      Dept. No.                                       Max. : 100 Marks

 

 

Part – A                                                             5 * 4 = 20

 

Answer any FIVE Questions with not exceeding 75 words each.

 

  1. What do you mean by Econometrics?
  2.  Distinguish between homoscedasticity and hectorscedasticity.
  3. Define Classical Linear Regression Model.
  4. What are the specifications of an error term in a single equation model?
  5. State the First-Order Autoregressive Scheme.
  6. Define Generalized Least-Square Estimator.
  7. Write a note on recursive model. Give an example

 

 

Part – B                                                          4 * 10 = 40

 

Answer any FOUR Questions with not exceeding 150 words each.

 

  1. Explain the importance of Econometrics.
  2. Explain the “Goodness of Fit”
  3. Obtain the Least-Square estimator for K- variables.
  4. Explain the uses of Dummy variables.
  5. Discuss the causes and consequences of Autocorrelation.
  6. State and explain the necessary and sufficient conditions for identification problem.
  7. State and explain the OLS principle.

 

 

Part – C                                                          2 * 20 = 40

 

Answer any TWO Questions with not exceeding 300 words each.

 

 

  1. Explain the extension of CLRM with an illustration.
  2. Analyse the properties of Least-Square Estimator for K-variables.
  3. Examine the method of Indirect Least Method
  4. Explain the Maximum Likelihood Estimator Method.

 

 

Go To Main page

 

Loyola College M.A. Economics April 2008 Econometrics Question Paper PDF Download

by

LOYOLA COLLEGE (AUTONOMOUS), CHENNAI – 600 034

AK 37

M.A. DEGREE EXAMINATION – ECONOMICS

SECOND SEMESTER – APRIL 2008

    EC 2807 – ECONOMETRICS

 

 

 

Date : 29/04/2008            Dept. No.                                        Max. : 100 Marks

Time : 1:00 – 4:00

 

PART – A

 

 Answer any FIVE questions in about 75 words each.                  (5 x 4 = 20 marks)

  1. Define econometrics.
  2. What is a non linear transformation?
  3. Distinguish between extreme and less extreme multicollinearity.
  4. What is homoscedasticity/
  5. What are the sources of dummy variable?
  6. Differentiate between pure and mixed models.
  7. What is an instrumental variable?

PART – B

 

 Answer any FOUR questions in about 250 words each.              (4 x 10 = 40 marks)

  1. Derive the normal equations of a two variable linear model.
  2. Explain the role dummy variable in seasonal adjustment.
  3. Prove that inclusion of irrelevant variables posses a less serious problem than exclusion of relevant variables in the model.
  4. Derive Koyck’s transformation used in estimating a distributed lag model.
  5. Discuss the method of estimation using grouped data.
  6. Derive GLS method of estimation.
  7. Show that errors in the measurement of dependent variables poses a less serious problem than that in the independent variables.

PART – C

 

Answer any TWO questions in about 900 words each.     (2 x 20 = 40 marks)

  1. Discuss the identifiably state of the following model

‘Y’ s are endogenous and ‘X’ s are exogenous and ‘U’s are error terms.

  1. Define autocorrelation and discuss its consequences and remedial measures.
  2. Derive 2SLS estimator.
  3. Derive 3SLS estimator.

 

Go To Main Page

Loyola College M.A. Economics April 2012 Econometrics Question Paper PDF Download

by

LOYOLA COLLEGE (AUTONOMOUS), CHENNAI – 600 034

M.A. DEGREE EXAMINATION – ECONOMICS

SECOND SEMESTER – APRIL 2012

EC 2811 – ECONOMETRICS

 

 

Date : 24-04-2012             Dept. No.                                        Max. : 100 Marks

Time : 9:00 – 12:00

 

Part – A

Answer any Five questions in about 75 words each:                                                              (5 x 4 = 20)

 

  1. Define Econometrics.
  2. What is the use of a non linear transformation in econometrics?
  3. What is Dummy Variable Trap?
  4. What is an instrumental variable?
  5. Distinguish between extreme and less extreme multi-collinearity.
  6. What is meant by simultaneous equation bias?
  7. Distinguish between Structural form and reduced form of a model.

 

Part – B

Answer any Four questions:                                                                                                   (4 x 10 =40)

 

  1. Explain the four non linear transformations commonly used in Econometrics.
  2. Derive the two normal equations of a two variable linear model by OLS.
  3. How do we deseasonalize a series by dummy variable technique?
  4. Derive the GLS estimate.
  5. Explain the consequences of heteroscedasticity.
  6. Discuss the method of ILS.
  7. How do we estimate a distributed lag model by using Koyck’s transformation?

 

Part – C

Answer any Two questions:                                                                                                   (2 x 20 = 40)

 

  1. Show that OLS estimator is BLUE (use a two variable linear model)
  2. Define autocorrelation. How do autocorrelated disturbances lead to violation of the assumption E (u u’) = σ2 Discuss the methods of detecting autocorrelation.
  3. Discuss the identifiability state of the following model (by both structural and reduced form)

y1 = 3y2 – 2x1 + x2 + u1

y2 = y3 + x3 + u2

y3 = y1 – y2 – 2x3 + u3.

  1. Derive the 2SLS estimator.

 

 

 

Go To Main page

 

 

Loyola College B.Sc. Statistics April 2008 Econometrics Question Paper PDF Download

by

LOYOLA COLLEGE (AUTONOMOUS), CHENNAI – 600 034

  B.Sc. DEGREE EXAMINATION – STATISTICS

NO 16

 

FOURTH SEMESTER – APRIL 2008

ST 4207/ ST 4204 – ECONOMETRICS

 

 

 

Date : 25-04-08                  Dept. No.                                        Max. : 100 Marks

Time : 9:00 – 12:00

SECTION- A

Answer all the questions. Each carries TWO marks.                  (10 x 2 =  20 marks)

 

  1. Define sample space and event of a random experiment.
  2. If P(A) = ¼  , P(B) = ½  and P(AB) = 1/6 , find (i) P(AB) and (ii) P(AcB).
  3. Given:

X= x :    0      1        2        3        4

P(X=x): 1/6  1/8     ¼      1/12    3/8

Find E(2X + 11).

  1. If  f ( x, y) is the joint p.d.f. of X and Y, write the marginals and conditional

distributions.

  1. Write any two properties of expected values.
  2. Define BLUE.
  3. Define the population regression coefficient .
  4. Write variance inflating factor of an estimator in the presence of multicollinearity.
  5. Define autocorrelation.
  6. Define point and interval estimation.

 

SECTION –B

Answer any FIVE questions. Each carries EIGHT marks.                (5 x 8 =  40 marks)

 

  1. Consider 3 urns. Urn I contains 3 white and 4 red , Urn II contains 5 white and 4 red and  Urn III contains 4 white and 4 red balls. One ball was drawn from each urn. Find the probability that  the sample will contain 2 white and 1 red balls.
  2. If a fair coin is tossed 10 times, find the chance of getting (i) exactly 4 heads

(ii) atleast 6 heads (iii) atmost  8 heads (iv) not more than 4 heads.

  1. Derive the least square estimators of the linear model Y = 1  + 2 X + u .
  2. State any six assumptions of the linear regression model.
  3. How to fit a non-linear regression model of the form Y = 1 + 2 X + 3 X 2 ?.
  4. Consider the model Y = 1  + 2 X + u  where X and Y denote respectively

consumer income (hundreds of dollars per person) and consumption of purple

oongs (pounds per person) . The sample size is 20 , sum of X is 300, sum of Y

is 120 , sum of squares of deviations of X from its mean is 500 , sum of product

of deviations of X and Y from their respective means is 66. 5 and sum of squares

of is 3.6.

  • Compute the slope and intercept.
  • Compute the standard error of regression.
  • Compute the standard error of slope.

 

  1. In a book of 520 pages , 390 typo- graphical errors occured. Assuming Poisson

law  for the number of errors per page, find the probability that a random sample

of 5 pages contain (i) no error (ii) atleast 3 errors.

 

 

  1. The mean yield for one-acre plot is 662 kg with a standard deviation of 32 kg.

Assuming normal distribution find how many one-acre plots in a batch of 1000

plots will have yield (i) over 700 kg  (ii) below 65 kg .

 

 

SECTION – C

 

Answer any TWO questions. Each carries TWENTY marks.          (2 x 20 =  40 marks)

 

  1. Consider the following joint distribution of (X,Y):

 

X          0            1            2               3

0        1/27      3/27       3/27           1/27

Y      1        3/27      6/27       3/27              0

2        3/27      3/27        0                  0

3        1/27        0           0                  0

 

(a) Find the marginal distributions of X and Y.

(b) Find E( X ) and V ( X )

(c) Find the correlation between X and Y.

(d) Find E ( Y | X = 2 )

(e) Verify whether or not X and Y are independent.

 

 

  1. (a) Explain the following methods of estimation used in the analysis of regression

models:

(i) Maximum likelihood      (ii) Moments

(b) The heights of 10 males of a given locality are found to be 70 , 67, 62 , 68 , 61

68 ,70 , 64 , 64 , 66 inches. Is it reasonable to believe that the average height is

greater than 64 inches ? Test at 5% significance level.

 

 

 

 

 

 

 

  1. For the following data on consumption expenditure (Y ) , income ( X2 ) and wealth

( X3 ):

 

Y($)   : 70     65      90       95      110      115     120     140      155       150

X2 ($) : 80    100    120     140    160      180      200     220      240       260

X3 ($) : 810  1009  1273   1425  1633    1876    2052   2201    2435     2686

  • Fit a regression model Y = 2 X2 + 3 X3 + u .
  • Find the correlation coefficients between Y and X2 , Y and X3 , X2 and X3.
  • Find unadjusted and adjusted R2 .
  • Test H0 : 2 = 3 = 0 at 5% significance level .

 

  1. (a) For the k-variate regression model Y = 1 + 2 X2 +…+k Xk + u

carry out the procedure for testing  H0 : 2 = 3 = … =  k = 0 against

H1: atleast one k 0.

(b) Write the properties of ordinary least square(OLS) estimators under the

normality assumption.

 

Go To Main page

Loyola College B.Sc. Statistics Nov 2012 Econometrics Question Paper PDF Download

by

LOYOLA COLLEGE (AUTONOMOUS), CHENNAI – 600 034

B.Sc. DEGREE EXAMINATION – STATISTICS

FOURTH SEMESTER – NOVEMBER 2012

ST 4207/4204 – ECONOMETRICS

 

 

Date : 07/11/2012             Dept. No.                                        Max. : 100 Marks

Time : 1:00 – 4:00

 

 

Section –A

Answer all the questions:                                                                                                     (10 x 2 = 20)

 

  1. Mention any two property of variance.
  2. Write a note on interval estimation.
  3. Define BLUE

 

  1. Obtain ESS from the following data given that RSS = 133.

 

Y

 

 10 14 17 20 25 30 19 27
  1. Define hypothesis
  2. What is Multiple Regression? Give an Example.
  3. Give the formula for Durbin Watson d – statistic.
  4. What do you mean by bench mark category?
  5. State the reasons under which Multicollinearity
  6. Define lagged variables.

 

                          Section –B                                             

Answer any five questions:                                                                                                      ( 5 x 8 = 40)

 

  1. A card is drawn from a pack of 52 cards. Find the probability of getting a king or a heart or a red card.
  2. The diameter of an electric cable, say X, is assumed to be a continuous random variable with p.d.f:
  • Check that is p.d.f.
  • Determine a number b such that P ( X < b ) = P ( X > b ).
  1. Explain in detail the Goals of Econometrics.
  2. Derive least square estimators for simple linear regression model.
  3. Explain in detail Variance Inflation Factor.
  4. From the following data estimate d-statistic and test for autocorrelation.

et : 0.6, 1.9, -1.7, -2.2, 1.3,3.2, 0.2,0.8, 2.1, -1.5, -1.1

(Given dL = 1.45 and du = 1.65)

  1. What are dummy variables? Explain its usefulness in regression analysis with

example.

 

 

 

  1. Find the value of R2 for the following data:
Y 12 8 9 6 8
X1 8 10 4 3 6
X2 10 12 6 5 7

 

 

                       

                

Section – C

 Answer any two questions:                                                                                                    ( 2 x 20= 40)

 

  1. Two random variable X and Y have the following joint probability density function:

 

Find (i) Marginal probability density functions of  X and Y

  • Conditional density functions
  • Var ( X) and Var ( Y)
  • Covariance between X and Y.

 

  1. Consider the following data on  X and Y

 

X 50 42 71 35 61 45 53 45 38 41 63 34 41
Y 145 123 155 120 150 130 155 120 135 160 165 115 120

 

  1. Estimate the equations of Y on X
  2. Test the significance of the parameters at 5% level of significance.
  3. Given the following data the estimated model is . Test the problem of heteroscedasticity with the help of park test.

 

X 1 2 3 4 5 6
Y 2 2 2 1 3 5

 

  1. Fit a linear regression model for the given data by the use of dummy variables
Aptitude score 4 9 7 3 5 8 9 5 6 8
Education qualification UG PG UG HSC PG UG PG HSC UG PG

 

Go To Main Page

 

 

 

 

 

 

 

 

Loyola College B.Sc. Economics April 2012 Econometrics Question Paper PDF Download

by

LOYOLA COLLEGE (AUTONOMOUS), CHENNAI – 600 034

B.Sc. DEGREE EXAMINATION – ECONOMICS

FOURTH SEMESTER – APRIL 2012

ST 4207 – ECONOMETRICS

 

 

Date : 19-04-2012              Dept. No.                                        Max. : 100 Marks

Time : 1:00 – 4:00

Section –A                                         

Answer all the questions                                                                                           (10 x 2 = 20)

  1. Define Maximum likelihood estimation.
  1. Give any two properties of normal distribution.
  2. Mention the difference between statistic and parameter.
  1. What is level of significance?
  2. Distinguish between R2 and adjusted R2
  3. What is meant by Intercept and Slope?
  4. Define Multicollinearity.
  5. Give any two forms of Glesjer test.
  6. State the reason for lag.
  7. Define specification error.

 

Section –B                                               

         

 Answer any five questions                                                                                         (5 x 8 = 40)

  1. Data on the readership of a certain magazine show that the proportion of male readers under 35 is 0.40 and over 35 is 0.20. If the proportion of readers under 35 is 0.70, find the proportion of subscribers that are female over 35 years. Also calculate the probability that randomly selected male subscriber is under 35 years of age.

 

  1. A random variable X has the following probability distribution function:

 

Value of X, x 0 1 2 3 4 5 6 7 8
P(x) K 3k 5k 7k 9k 11k 13k 15k 17k

 

  1. Determine the value of k
  2. Find P( X < 3) , P( X 3)
  • P ( 0 < X < 5 )

 

  1. Establish the unbiasedness property of OLS estimators for simple linear regression model.
  2. State and prove Gauss Markov theorem.
  3. Derive  by using matrix approach for a multiple regression model.
  4. How do you measure the goodness of fit in the regression model.

 

 

  1. Consider the model with the following observations on Y and X
X 1 2 3 4 5 6 7 8 9 10
Y 2 2 3 3 3 1 4 5 5 2

The estimated model is =1.933+0.194X; Examine the existence of heteroscedasticity

using spearman’s rank correlation test.

  1. Explain lagged variable with an illustration.

 

 

Section – C                                              

         

 Answer any two questions                                                                                         (2 x 20= 40)

  1. a) A variable X is distributed between the values 0 and 4 and its probability density function is given by

Find the value of k, the mean and standard deviation of the distribution.

  1. b) Write short notes on:-
  2. Nature of Econometrics
  3. Structural and reduced forms
  • Applications  of Econometrics

 

  1. Given the following data

 

∑ Yi2 1000
∑ X1i2 200
∑X2i2 100
∑ X1i  Yi 400
∑ X2i  Yi   -100
∑ X1i  X2i 0
50
15
10
n 28

 

  1. Estimate the parameter in the equation,
  2. Estimate S.E. of estimators,
  3. Test the significance of   and
  4. Find R

 

  1. Given the following data test the problem of heteroscedasticity with the help of Goldfeld Quantt

 

X 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Y 2 2 2 1 3 5 8 11 12 10 10 12 15 10 11

 

 

  1. Consider the following data on Y, X1 and X2.

Y:        10        20        40        30        50

X1:       2          5          3          8          7

X2:       1          0          1          2          1

a.) Fit a linear model of Y on X1 and X2. Interpret the regression coefficients.

b.) Calculate R2 and interpret it.

 

 

Go To Main Page

 

 

© Copyright Entrance India - Engineering and Medical Entrance Exams in India | Website Maintained by Firewall Firm - IT Monteur