OPTIMUM BID

Optimum Bid

Optimum Bid

We apply Linear Regression on the given data set.

Variables Entered/Removedb

Model

Variables Entered

Variables Removed

Method

1

winnindBida

.

Enter

a. All requested variables entered.

b. Dependent Variable: Cost

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.205a

.042

.014

84874.98123

a. Predictors: (Constant), winnindBid

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

1.101E10

1

1.101E10

1.528

.225a

Residual

2.521E11

35

7.204E9

Total

2.631E11

36

a. Predictors: (Constant), winnindBid

b. Dependent Variable: Cost

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

11371.838

42056.212

.270

.788

winnindBid

.978

.791

.205

1.236

.225

a. Dependent Variable: Cost

Forecast

For forecasting we use curve estimation technique

Forecasted values

55571.76716

106323.4556

60852.28966

47259.83361

48726.64542

76596.06968

49215.58268

41490.37385

46379.74653

68870.86085

89699.58846

56354.06679

46281.95908

44912.93473

107692.4799

48922.22032

67501.8365

50486.81958

40219.13696

78942.96857

64763.7878

71315.54719

59678.84021

53029.29337

95175.68586

56256.27934

67990.77377

62025.7391

44912.93473

41685.94876

63688.12581

69750.94793

57918.66605

47748.77088

44815.14727

40610.28677

61634.58928

References

Forbes, Ronald W. and John E. Peterson, 2000. Local Government General Obligation Bond Sales in Pennsylvania: The Cost Implications of low-bid vs. Competitive Bidding. Washington, DC: Government Finance Research Center, Municipal Finance Officers Association.

Joehnk, Michael D. and David S. Kidwell, 1999. Comparative Costs of Competitive and low-bid ...