Marketing 0

between variables.
Using any statistical software, marketers can draw an equation
that best fits the data. The most basic equation forms a straight
line, which is known as a linear regression line. Another
common one is the logistic regression, which uses a logistic
function to model a binary dependent variable, such as buy or
not buy and stay or churn. Thus, logistical regression is often
used to predict the likelihood of an outcome, such as the
probability to buy.
3. Interpret the equation to reveal insights and check for
accuracy.
Consider the following example. Let us say the best-fit equation
is defined as follows:
In the formula, Y is the dependent variable while X
1
, X
2
, and X
3
are the independent variables. The a is the intercept, which
reflects the value of Y if there is no influence whatsoever from
independent variables. The b, c, and d are the coefficients of the
independent variables, which indicate how much impact the
variables have on the dependent variables. In the equation, we
can also analyze the error term or residual (written as e). A
regression formula always has errors, as the independent
variables might not entirely explain the dependent variables.
The larger the error term, the less accurate the equation is.
4. Predict dependent variables given independent
variables.
Once the formula is established, marketers can predict the
dependent variables based on the given independent variables.
That way, marketers can envision the outcomes from a mix of
marketing actions.

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