Abstract – Gross domestic product (GDP) is defined as the "value of all goods and services produced in a country in one year". This project helps in predicting the Gross State Domestic Product (GSDP) of Gujarat state based on the factors which affect it on a great scale. A forecasting method called Regression Analysis has been used to create the application. INTRODUCTION Forecasting Frequently there is a time lag between awareness of an impending event or need and occurrence of that event. This lead time is the main reason for planning and forecasting. If the lead time is zero or very small, there is no need for planning. If the lead time is long, and the outcome of the final event is conditional on identifiable factors, planning can perform an important role. In such situations, forecasting is required to determine when an event will occur or a need arise, so that appropriate actions can be taken. Forecasting situations vary widely in their time horizons, factors determining actual incomes, types of data patterns and many other aspects. There are various categories of forecasting methods: Quantitative: Sufficient quantitative information is available.

1) Time series-Predicting the continuation of historical patterns such as the growth in sales or gross national product.

2) Explanatory-Understanding how explanatory variables such as prices and advertising affect sales.

Qualitative: Little or no quantitative information is available, but sufficient qualitative knowledge exists.

1) Predicting the speed of telecommunications around the year 2020.

2) Forecasting how a large increase in oil prices will affect the consumption of oil.

Unpredictable: Little or no information is available.

1) Predicting the effects of interplanetary travel.

2) Predicting the discovery of a new, very cheap form of energy that produces no pollution.

The category of forecasting method we are going to use for GSDP prediction is quantitative since sufficient quantitative information is available. [1] REGRESSION ANALYSIS

Regression analysis is a Statistical Forecasting model that is concerned with describing and evaluating the relationship between a given variable (usually called the dependent variable) and one or more other variables (usually known as the independent variables). Regression analysis models are used to help us predict the value of one variable from one or more other variables whose values can be predetermined. STEPS OF REGRESSION 1. Statement of the problem Prediction of GSDP of Gujarat state based on the factors affecting it on a great scale. We would be able to check the variation of GSDP by increasing or decreasing the input in a factor. It would give the governments an overview of how the factors affect GSDP. 2. Selection of potentially relevant variables The next step after the statement of the problem is to select a set of variables that are thought to explain or predict the response variable. A possible relevant set of predictor variables in the case of manufacturing sector is: working capital, no. of workers, wages to workers, total emoluments, etc. Gross domestic product (GDP) is defined as the "value of all goods and services produced in a country in one year". So, the variables will be the factors that will affect the total capital formation of the state. 3. Data Collection This step includes the collection of data from the environment under study to be used in the analysis. Data has been collected from various web resources. [2][3] 4. Model specification Based on the data collected, the model selected to be used is Stepwise forward multiple linear regression. Stepwise regression is a method which can be used to help sort out the relevant explanatory variables from a set of candidate explanatory variables when the number of explanatory variables is too large to allow all possible regression models to be computed. Stepwise forward regression involves picking the potential explanatory variable that has the highest correlation with GSDP, i.e. the factor affecting GSDP the most. Then we determine the residual GSDP fromthis regression considering only this factor and think of the residual GSDP as the new GSDP. From among the remaining factors, we pick the factor that correlates most highly with the new GSDP. This process is continued until no remaining factor has a significant relationship with the last new GSDP. As there are multiple steps involved in the calculation of GSDP, we call it multiple regression. Linear regression is considered when there is a linear relationship between the factors and GSDP. GSDP is a linear function of the factors. [1]

5. Method of fitting After graphically analyzing the relationship between the factors and GSDP, a linear relation has been found. So, linear regression is the fitting method. And as there are more than one factor affecting GSDP, hence we used multivariate regression analysis. 6. Model fitting In its simplest form regression analysis involves finding the best straight line relationship to explain how the variation in an outcome (or dependent) variable, Y, depends on the variation in a predictor (or independent or explanatory) variable, X.