Logistic regression is a mathematical technique that estimates the probability of an event occurring. Using historical data to create a predictive model, you can use regression to predict business, investment, operational, and strategic risks. By understanding how these risks get indicated, you can better assess your company's vulnerabilities and protect them from future losses.
This blog post will provide examples of how you might use regression in your workplace and explain what this technique does in more detail.
Why is Logistic Regression critical?
It is a statistical technique that tries to understand how the probability of an event occurring changes when one or more variables get altered. The method builds predictive models using data about previous incidents to use for proactively predicting future events. For instance, you could use regression to guess which customers are most likely to stop using your products and services.
Logistic regression can use to predict business risks in many ways, including:
- Identifying the likelihood of a bad debt written off.
- Assessing the probability that an IT system will cause downtime.
- Estimating the risk that a new product or service will flop.
For example, suppose you are assessing the risk that a customer will default on their repayments. In that case, your model might include variables such as the loan amount and the borrower's age. If you are trying to assess IT downtime risk, some variables might be how old a system is and its many users.
- Assessing internal risk levels by quantifying how much staff turnover there has been over the past year. By using information about the average time, it takes for employees to complete their tasks.
For example, suppose you are trying to determine which product is most profitable. If you are trying to assess how quickly tasks are completed, some variables might be how long a study takes to complete and how many times it has met before.
- You can use it to quantify the risk that you will not receive payment for goods or services supplied.
- Assessing the likelihood of a customer is likely to leave your company's favor based on variables. Such as their tenure, monthly spending, and how many requests they have made for support.
- Predicting the probability of a new product being successful.
- It determines the likelihood of a new employee bringing in a valuable new business.
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