Lecture 5

P-Value: What It Is, How to Calculate It, and Examples

A p-value, or probability value, is a number describing the likelihood of obtaining the observed data under the null hypothesis of a statistical test.

The p-value serves as an alternative to rejection points to provide the smallest level of significance at which the null hypothesis would be rejected. A smaller p-value means stronger evidence in favor of the alternative hypothesis. P-value is often used to promote credibility for studies by scientists and medical researchers as well as reports by government agencies. For example, the U.S. Census Bureau stipulates that any analysis with a p-value greater than 0.10 must be accompanied by a statement that the difference is not statistically different from zero.

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The differences between Generative AI and Predictive AI go beyond their foundational principles, extending into their training processes and real-world applications. Generative AI, exemplified by powerful models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), has the ability to create new content that resembles existing datasets, supporting applications in image synthesis, style transfer, and text generation. On the other hand, Predictive AI, represented by models like linear regression, decision trees, and neural networks, excels in making accurate predictions and classifications based on historical data, finding its footing in domains such as finance, healthcare, and marketing.
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What Is Logistic Regression? Equation, Assumptions, Types, and Best Practices

Logistic regression is defined as a supervised machine learning algorithm that accomplishes binary classification tasks by predicting the probability of an outcome, event, or observation. This article explains the fundamentals of logistic regression, its mathematical equation and assumptions, types, and best practices. Logical regression analyzes the relationship between one or more independent variables and classifies data into discrete classes. It is extensively used in predictive modeling, where the model estimates the mathematical probability of whether an instance belongs to a specific category or not.

For example, 0 – represents a negative class; 1 – represents a positive class. Logistic regression is commonly used in binary classification problems where the outcome variable reveals either of the two categories (0 and 1).

Few precedents are available to C-suite executives when making decisions concerning generative AI. While leaders across industries are exploring different approaches, the lack of a road map presents a serious challenge when assessing which gen AI decisions are most likely to maximize benefits while mitigating potential risks.This research aims to reduce that uncertainty. We analyzed over 100 actions respondents said they were currently taking related to their gen AI implementations in Deloitte’s second quarter 2024 State of Generative AI in the Enterprise survey and their connections to various business outcomes. Our analysis (see methodology) found that respondents taking a high number of trust-related actions when implementing gen AI processes—among the data, workforce, and customer approaches asked about in the survey—were more likely to report that nearly two-thirds (66%) or more of their organization’s expected gen AI benefits had been achieved
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How Do P-Values Get Calculated for Linear and Logistics Regression? In machine learning we are often just concerned with the accuracy of our models — as long as the model can predict better than the majority class with classification or with a better RMSE than the mean for regression on unseen data, the model has some kind of value. Whether this is for predicting who will default on their loans, flagging at-risk students, determining how much homes will sell for in Ames Iowa, or whatever the type of question you are trying to predict for, data science is primarily interested in accuracy. However, if your goal is to answer a question, you are now entering the realm of inference which leans more on p-values than accuracy to prove statistical significance. While p-values alone are not enough information to demonstrate causation, they are critical for demonstrating a non-zero correlation which is still valuable for understanding how different data connect. This post will discuss how to solve for p-values with linear and logistic regression using Excel and Statsmodels.
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Learning how to do logistic regression in Excel can add a powerful predictive tool to your toolbox. With artificial intelligence and machine learning dominating the technology main stage, logistic regression and other predictive algorithms like linear regression have begun to permeate mainstream business vernacular. Because logistic regression can predict the likelihood of an event, it has a wide range of business applications, from fraud detection to sales revenue forecasting.
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