Interpretation of Regression Results - Essay Example

For the hypothesis to be true, the signs of the coefficients are expected to be positive. The positive coefficient signs would indicate the positive relationship between the independent variables (temperature, humidity, wind and dummy variables) and the dependent variable (bundled load). Coefficients: The signs of all coefficients of variables are positive whereas, that of intercept is negative. The negative value of coefficient of intercept means that the regression line intersects with Y-axis below zero.

The positive signs and significant values of coefficients reflect a positive relation between the dependent and independent variables. The value of coefficient of temperature is 47.64. It means that if temperature or x1 variable increases by 1 degree Fahrenheit, the bundled load will increase by 47.64 Megawatt/hour provided all other variables are constant. If humidity increases by 1 percent, the bundled load will increase by 11.52 Megawatt/hour. Similarly, if wind speed increases by 1 mph, the bundled load will increase by 10.

15 Megawatt/hour. Moreover, the coefficient of dummy1 is zero, showing no relation between the dummy1 and bundled load whereas; the coefficient of dummy2 is 49.73, showing a positive relation between dummy2 and bundled load. The standard error shows the amount of variability of the data points around the regression line and in this regression analysis, the standard errors for all the variables is very small. The small values of the standard errors show that the data points are closely distributed around the regression line.

The value of p is greater than 0.05 for three independent variables including temperature, humidity and wind speed. However, for the dummy1, it is zero and for dummy2, it is less than 0.05. If the p-values for all the variables would have been equal or less than 0.0, then the null