Predictive Modeling Concept Cont'd (Independent & Dependent Variables)

Predictive Modeling Concept Cont'd (Independent & Dependent Variables)

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3 min read

In this article, we will be going a deep further into predictive modeling, we will be explaining independent variables and dependent variables.

According to our previous article, our model uses certain entities, and objects called features to make predictions.

These features are known as independent variables. They are called independent variables because their value does not depend on and is not affected by the state of any other variable in the experiment.

Sometimes you may hear this variable called the "controlled variable" because it is the one that is changed. Do not confuse it with a "control variable," which is a variable that is purposely held constant so that it can't affect the outcome of the experiment.

The dependent variable can simply be seen as our outcome or our predictions. It is called a dependent variable because it can't stand alone, its value is always determined by the independent variables.

Sometimes the dependent variable is called the "responding variable."

In general, the model uses the independent variables to predict a possible dependent variable.

RELATIONSHIP BETWEEN THEM

If you’re experimenting, the independent variable is the condition or factor you manipulate to see an effect.

The dependent variable is the outcome of the manipulation.

In statistics;

They are related by the formula;

y = x+c,

where y is our dependent variable, x is our dependent variable, and c is our constant.

Let's make the equation like this;

y = 3x+1; let x = 2, our y becomes 7;

As seen above, a change in x determines the value of y, simply because y is dependent on x.

CASE SCENARIO

Let's create a case scenario where we identify how change in independent variables affects the dependent variable.

The scenario will be to see if a student will get an EXCELLENT, a PASS OR a FAIL in an exam.

First scenario;

When the student reads from the first day of school, the student gets an EXCELLENT

When the student reads a week before the exam, the student gets a PASS

When the student reads the night before the exam or doesn't read at all, the student gets a FAIL

When the student reads a week before the exam, but the student has a private tutor, the student gets an EXCELLENT

When the student reads the night before the exam, but the student has a private tutor, the student gets a PASS

As seen above, changes in when the student reads, if the student has a private tutor, affect the grade of the student.

From the above scenario;

Independent Variables:

When student reads

If the student has a private tutor

Dependent Variable:

Excellent, pass or fail

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See the previous article on the Concept Of Predictive Modeling here

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