The coefficient of determination (R-square) is a measure of the goodness of fit of the estimated regression equation.
In the table 1, the R-square value 0. 82722 shows the 82. 72% variation in the dependent variable Y (food expenditure per head) is explained by the estimated regression equation. The reliability of the data set is shown by the value in the standard error column.
This value shows how well the values in the model relate to each other. In this equation the Y intercept (bO) is 504. 4728 .It shows if all variables are zero there will be an autonomous expenditure on food about 504.47 taka. Positive slope coefficient for variable X1 is 0.
05916. It shows if the total family income increases by 1 Taka the partial expenditure on food per head will increase by 0. 05915 taka & vice versa. There is a great effect on food consumption by the Education.
Usually higher educated and experienced labors get higher payment. Positive slope coefficient for education level 27. 49 represents the partial expenditure on food per labor will increase by 27. 49 taka if the education level increase by 1 class (grade) & vice versa.The slope coefficient for the variable family member is negative . It refers if the number of family member in a family increase by one the partial expenditure on food per head will decrease by 114. 58 taka & vice versa.
On the other hand if the earning member of a family increase by one the partial expenditure on food will increase by 50. 279 taka & vice versa which is represented by positive slope coefficient 50. 279.
The slope coefficient for house rent is positive. The value 0. 0999 shows if the house render expense increase by one the expenditure on food per head also increase by 0.
0999 taka & vice versa.This result contradicts our earlier assumption. This might be because the workers decide on the housing depending on the income level.
So, the housing is actually representing the income level of the worker. So, there is a positive relationship. Heteroscedasticity The white Heteroscedasticity test shows that our data do not have heteroscedasticity problem as p>. 05, which is logical as we are dealing with people with the same level of income, their expenditure pattern should not vary much.So, we can see from the correlation matrix that there is a high correlation between total family income and family member, total family income and house rent, family member and house rent. But, these variables are important so, we would not exclude these variables.
Conclusion This study proposed to develop a statistical model on Money Spent on Food by the Female Garment Worker. Finally we decided that all the five variables that started with are important. In our ultimate model we decided that expenditure on food of garments workers depend on Family income, Education Level, family size, Earning member, House rent.