Question

Explain why you choose multiple regression with dummy variables but not linear trend model and why do you believe this technique is appropriate to forecast your data?

Answer 1

The multiple regression used in finds relation between several variables and one dependent variable. Using multiple regression we can analyse the relative influence of independent variable in complex data. Most of the dummy variables are qualitative variables, which cannot measure with numerical units. The dummy variables include the nominal scale data like sex, race, colour, region, nationality geographical region, political upheavals and party affiliation. His variables are essential to classify the data into mutually exclusive categories such as male and female. When we are adding the dummy variable to the multiple regression model, the final conclusion will me more accurate than the model without dummy variable. This model used to find the difference between several groups using the equations.
The use of dummy variables helped to forecast the data. Dummy variable are mainly constant. This will avoid the flexibility in the value of the dynamic variables. The dummy variable take value 1 corresponding to yes and o corresponding to no. If there are more than tow variables there are several dummy variables were used. This will automatically handle the case that if you specify the factor variable as a predictor. The dummy variable measure the effect of category relative to the omitted category.