Demand Forecast

Time Series Analysis.

Simple linear Regression.

Please enter minimum of 4 rows of x and y variable data

Time-x Cost or WOs-y

Please enter x value to predict y variable.

y =m(x)+c


Simple Linear Regression (SLR) is the most widely used statistical technique for estimating a business’ future needs based on past data. It is a way to model a relationship between two sets of historic data in order to make predictions about future data. SLR is the foundation for more complex predictive models.

Why use Linear Relationships?

  • To simplify, regression analysis is used to understand how certain factors in your work process affect company performance and predict how revenue would change over time if you continued the same strategy or pivoted to different methods.

  • Forecasting using Linear regression results help businesses to estimate how the future would look based on past data (e.g. demand, sales numbers). Other examples might include estimating future capital required to achieve desired revenue over a specified time period, or how many labor hours are required to generate revenue or profit goals.



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