Rice Case Study
a. Predictors in the Model: (Constant), Rice Seasonal Yield b. Dependent Variable: Quantity Supplied
Again, if we look at the co-efficient table we can see that while doing T-Test the significance value is 0.059, which is also higher than 0.05 and again in this scenario, null hypothesis will be rejected and alternative hypothesis will be accepted. In conclusion, we can say that rice irrigated area have influence on quantity supplied in market. If there is any change in amount of rice irrigated area then it will directly affect quantity supplied of rice.
In the above picture, we can see that the dotted line is quiet equally distributed. In conclusion, rice irrigated area have a significant amount of influence over supply of rice.
Estimated relationship with Price of Wholesalers and the Quantity Supplied of Rice: In the scenario, we will try to find out that if there is any significance between these two variables. In that case, our hypotheses will be: H0: There is no significant relation between price by wholesalers and quantity supplied of rice. H1: There is significant amount of relationship between price of wholesalers and quantity supplied of …show more content…
Error of the Model 1 R .993 a R Square .986
Adjusted R Square .984
a. Predictors: (Constant), Rice Seasonal Yield, Fertilizer Sales b. Dependent Variable: Quantity Supplied
From the above chart, we can see that our R square value is .986 which mean 98.6% of the variation of the quantity supplied of rice is explained by the price Fertilizers sales in the market. In terms of R value, which is .993 meaning there is strong relationship between the fertilizers and the quantity supplied of rice in the market.
ANOVA Model 1 Regression Residual Total 2 Regression Residual Total Sum of Squares 4.453E8 8399671.913 4.537E8 4.472E8 6547251.639 4.537E8 df
Mean Square 1 24 25 2 23 25 2.236E8 284663.115 4.453E8 349986.330
Sig. .000 a 785.468
a. Predictors: (Constant), Rice Seasonal Yield b. Predictors: (Constant), Rice Seasonal Yield, Fertilizer Sales c. Dependent Variable: Quantity Supplied
The amount of significance explains the goodness of fit of the model. Here significance value is .051, which is higher than .05. Here the numerator df tells us how many predictors we have (this time it is 1) and the denominator degrees of freedom is 24. Therefore, we can say that we reject null hypotheses and accept alternative hypotheses. That means there is a direct relationship between fertilizers and supply of rice in the