Simple Regression Analysis on the Relationship Between Price and Quantity of Goods

University:
The University of Melbourne
Course:
ECON 90055 | Computational Economics
Academic year:

2021
Views:

355

Pages:

2
Author:

Berhasil D

Simple Regression Analysis on the Relationship Between Price and Quantity of Goods ModelR Model Summary Adjusted R Std. Error of R Square Square the Estimate 1 .295a .087 -.003 .96385 a. Predictors: (Constant), CS4, BI2, CS3, BI1, CS2, BI3, BI4, CS1 ANOVAa Sum of ModelSquares df 1 Mean Square Regression 7.150 8 .894 Residuals 75.250 81 .929 F Sig. .471b .962 Total 82.400 89 a. Dependent Variable: SB1 b. Predictors: (Constant), CS4, BI2, CS3, BI1, CS2, BI3, BI4, CS1 Coefficientsa Unstandardized Standardized Coefficients Coefficients Beta Model Std. Error t Sig. B 1 (Constant) 5.624 1.260 4.463 .000 BI1 -.130 .151 -.109 -.866 .389 BI2 .154 .150 .134 1.026 .308 BI3 -.297 .155 -.256 -1.919 .058 BI4 .087 .152 .076 .573 .568 CS1 -.130 .179 -.097 -.725 .470 CS2 -.180 .136 -.163 -1.322 .190 CS3 .040 .166 .031 .241 .810 CS4 .019 .119 .019 .162 .872 a. Dependent Variable: SB1 • In the model seen R = 0.295. means the relationship (Relation) between quality and decision is 29.5%. This means that the relationship is not close. The bigger R means the closer the relationship. To ensure the type of relationship between variables can be seen in the following table: Value 0.0 - 0.19 0.2 - 0.39 0.4 - 0.59 0.6 - 0.79 0.8 - 0.99 Interpretation Not very close Not tight Tight enough Erat Very close • R square of 0.087 means that 0.87% of the decision variables can be explained by quality, while the remaining 99.13% can be explained by other factors examined by this study. • Standard Error of Estimate means measuring the variation of the predicted value. Standard Error of Estimate can also be referred to as standard deviation. The Standard Error ofEstimate is 0.96385. then the smaller the standard deviation means the better the model. b). The table shows that the significant value is 0.000 (smaller than 0.05), which means that quality has a positive and significant effect on decisions. This means reject Ho and accept Hi. The regression equation is Y = 5.624 + 10.37X, meaning that if the quality is increased by one unit, it will increase the decision by 10.37.