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A) Form expectation about sign of coefficients
         W = 0 + 1 S + 2 D + 3 L + 

         1 > 0

         2 > 0

         3 > 0

B) Estimate model and write the result in standard form

         W = -478.607 – 12.8052 S + 1.89576 D + 35.9569 L
              [131.5]      [1.350]    [0.4624]   [2.881]

C) Interpret all coefficients


         If number of student in polish (s) goes up by 1 unit (thousands) then number
         of people with collage degree (w) will go down on average by 12.8059

         If number of GNP dynamics (D) goes up by 1 unit (thousands) then number of
         people with collage will go up on average by 1.8957

         If number of people aged 20-24 (L) goes up by 1 unit (thousands) then
         number of people with collage will go up on average by 35.9569

         Variables (S,D,L)if the all independent variable are equal to 0, then W equal to
         -478.607



D) Interpret all standard errors


         When we calculate our coefficient 0 at value -478.607 we on average make
         mistake about  131.5

         When we calculate our coefficient 1 at value -12.8059 we on average make
         mistake about  1.35
When we calculate our coefficient 2 at value 1.8957 we on average make
        mistake about  0.4624

        When we calculate our coefficient 3 at value 35.9569 we on average make
        mistake about  2.881

E) Check precision of estimate of S and L

                            Si            Standard Error
                      Si= | |* 100 = |  |
                            I             Coefficient

                         1.350
                     Ss=  * 100 = 10.543 %
                         12.8052

                         2.881
                     SL=  * 100 = 8.012 %
                         35.9569


F) Interpret and comment R^2


          _        K (no of independent )
          R = R -  * (1- R2)
            2  2

                 N –(K+1)
                 (no of observation)

          _            3
          R = 0.964 -  * (1- 0.964) = 0.959 => 95.9 %
            2

                    25-3-1

        R2 = 0.964
        0.964 > 0.7 is good model
        It means 96% of dependent variable is explained by independent variable and
        is good model
        R2 > 0.7 is good model
        R2 < 0.5 is bad model
        0.5 < R2 < 0.7 is in average
G) Test significance of L,D and of constant term

  H0: 1=0 if parameters 1 is equal to zero variable j is not significant in the model.

  H1: 1=/= 0 if parameters 1 is different to zero variable j is significant in the
  model.
H) Test significance of all variables in the model
I) Test for autocorrelation
J) Check coincidence

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Econometric Exam (Review)

  • 1. A) Form expectation about sign of coefficients W = 0 + 1 S + 2 D + 3 L +  1 > 0 2 > 0 3 > 0 B) Estimate model and write the result in standard form W = -478.607 – 12.8052 S + 1.89576 D + 35.9569 L [131.5] [1.350] [0.4624] [2.881] C) Interpret all coefficients If number of student in polish (s) goes up by 1 unit (thousands) then number of people with collage degree (w) will go down on average by 12.8059 If number of GNP dynamics (D) goes up by 1 unit (thousands) then number of people with collage will go up on average by 1.8957 If number of people aged 20-24 (L) goes up by 1 unit (thousands) then number of people with collage will go up on average by 35.9569 Variables (S,D,L)if the all independent variable are equal to 0, then W equal to -478.607 D) Interpret all standard errors When we calculate our coefficient 0 at value -478.607 we on average make mistake about  131.5 When we calculate our coefficient 1 at value -12.8059 we on average make mistake about  1.35
  • 2. When we calculate our coefficient 2 at value 1.8957 we on average make mistake about  0.4624 When we calculate our coefficient 3 at value 35.9569 we on average make mistake about  2.881 E) Check precision of estimate of S and L Si Standard Error Si= | |* 100 = |  | I Coefficient 1.350 Ss=  * 100 = 10.543 % 12.8052 2.881 SL=  * 100 = 8.012 % 35.9569 F) Interpret and comment R^2 _ K (no of independent ) R = R -  * (1- R2) 2 2 N –(K+1) (no of observation) _ 3 R = 0.964 -  * (1- 0.964) = 0.959 => 95.9 % 2 25-3-1 R2 = 0.964 0.964 > 0.7 is good model It means 96% of dependent variable is explained by independent variable and is good model R2 > 0.7 is good model R2 < 0.5 is bad model 0.5 < R2 < 0.7 is in average
  • 3. G) Test significance of L,D and of constant term H0: 1=0 if parameters 1 is equal to zero variable j is not significant in the model. H1: 1=/= 0 if parameters 1 is different to zero variable j is significant in the model.
  • 4. H) Test significance of all variables in the model
  • 5. I) Test for autocorrelation