logistic regression with a real-time example related to customer experience
Suppose you own a retail store and you want to understand the relationship between customer experience and the likelihood of a customer making a purchase. To do this, you survey your customers and ask them to rate their experience on a scale of 1 to 10. You also record whether each customer made a purchase or not.
This data can be used to build a logistic regression model. The goal of the model is to predict the probability that a customer will make a purchase based on their reported experience. The customer experience score is used as the independent variable, and the dependent variable is whether or not the customer made a purchase.
The logistic regression model will return a formula that can be used to estimate the probability of a customer making a purchase given their experience score. For example, if the formula is:
P(purchase) = 1 / (1 + e^-(b0 + b1 * experience))
where b0 and b1 are coefficients determined by the model, then the output of the formula will be a probability between 0 and 1, with higher values indicating a higher likelihood of a customer making a purchase.
This model can be useful for understanding the relationship between customer experience and the likelihood of a customer making a purchase, and can also be used to make predictions about future customer behavior.
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