_Regression _is one of the mathematical approaches to measure the relationship between independent/predictor variables with their dependent/target variable.

Regression widely used for at least two primary purposes. First, regression is often used as a prediction or forecasting tool. Second, in some events, a regression can be used as an analytical method in data analysis. In this article, we will use regression for its secondary purposes and get insight into how the regression model can be used to analyze past events instead of forecasting future events.

**Multiple Linear Regression** is one of the most straightforward tools which can be used to analyze a dependent variable against several independent/predictor variables. This kind of regression is often used due to the reality of some process is not only built by one factor instead, but several other factors are also involved in every activity.

The linear regression formula’s **slope** can also be interpreted as the *linear relationship strength between the independent variable* *and its dependent variable*. Based on that definition, we can comfortably say that the higher the slope value of the independent variable, the more significant this variable influences its dependent variable.

That will be our basis to analyze and determine which factor is influenced the most in our case in the manufacturing Industry.

Analyzing Raw Material Influence on Production Quality

Case Definition:

Company “P” produces a product that needs to fulfil some quality requirements; one of the essential requirements is that it should have a **defect value** of less than 30. To produce this product, Company “P” uses three raw materials; **Material-A, Material-B, and Material-C. **The composition between those materials is *0-10 % A, 0-10% B, and 80-100% C*.

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