Market Regression Analysis

Regression Analysis

Primarily, a set of variables that affect sales figures have to be identified. For example, it could be variables such as GNP growth rate or new vehicle purchases or variables like promotion expense and sales figures. Once these figures are made known, data is required for at least 20-25 observations. Then, a linear equation is built using methods like the least squares algorithm. Categorical predictor variables like the levels of education of consumers can also be performed.

Market Regression Research Analysis with numerical predictors.

Once the equation is developed, the resulting coefficients are used to predict the value of sales for a new set of predictor values. This is a quantitative method, and the closer the relationship between the measured variables. The advanced quality of predictions made from this. Some of the common applications include forecasting sales of biscuits, to the sales of cement and heavy machinery. The only requisite condition is the availability of suitable numerical data on predicted variables.

Our approach to Market Regression Research Analysis

The Prediction model thus built must be tested to find out the accuracy in actual conditions or how it executes with data left out during the model-building process. The prediction model is modified by adding or reducing variables if needed.

Informatics Outsourcing has worked with customers in North America and Europe to carry out advanced statistical analysis on market research data and instrumental data.

Market Regression Analysis professionals :

We have well trained software professionals and statisticians to carry out complicated tasks in Market Regression Analysis. We have licensed copies of SAS, MATLAB, and SPSS to perform all statistical analysis.