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Ratios: Unveiling the Power of Mathematical Relationships


Ratios, an elementary yet powerful mathematical concept, permeate nearly every aspect of our lives. From the early stages of human civilization to the cutting-edge technologies of today, the ubiquitous presence of ratios cannot be overstated. In this double-length academic blog post, we shall traverse the intricate world of ratios, exploring their nature, uses, relationship to fractions and odds, historical context, and applications in machine learning, particularly in logits. This intellectual odyssey will elucidate the foundational role ratios play in our ever-evolving understanding of the world and the mathematical structures governing it.

Ratios: A Keyphrase in Mathematical Relationships

A ratio represents the quantitative relationship between two or more quantities, expressing how many times one value is contained within the other (Stapel, 2021). Ratios can be written in several forms, such as “a to b,” “a:b,” or “a/b.” The latter form, “a/b,” is the most commonly used representation in mathematics, highlighting the intimate connection between ratios and fractions.

This connection becomes even more evident when we consider that every fraction is, in fact, a ratio. A fraction is a special type of ratio where the quantities being compared are part of the same whole. For example, the fraction 3/4 represents a ratio comparing the part (3) to the whole (4). This duality of representation emphasizes the importance of ratios in our daily lives, as fractions are frequently employed to express probabilities, odds, proportions, and various other quantitative relationships.

The Intersection of Ratios, Fractions, and Odds

Odds, another fundamental concept in probability and statistics, is intrinsically linked to both ratios and fractions. Odds convey the likelihood of a particular event occurring, represented as the ratio of the number of favorable outcomes to the number of unfavorable outcomes (Jones, 2020). For instance, if we roll a fair six-sided die, the odds of rolling an even number (i.e., 2, 4, or 6) are 3:3, or 1:1, as there are an equal number of even and odd outcomes. This ratio expresses the odds of success versus failure, which can be converted to a probability by dividing the number of successful outcomes by the total number of possible outcomes. In our die-rolling example, the probability of rolling an even number is 3/6 or 1/2.

This conversion from odds to probabilities further underscores the interconnectedness of these three mathematical concepts. Ratios serve as the backbone of fractions and odds, allowing for the expression of complex relationships between quantities in a succinct and easily interpretable manner.

Ratios: An Age-old Discovery

The origins of ratios can be traced back to ancient civilizations, particularly in the realms of commerce and construction. The earliest known recorded use of ratios dates to the Babylonian civilization, where they were used to calculate proportions in the context of trade and architecture (Katz, 1998). Egyptian mathematicians were also known to employ ratios, notably in their groundbreaking work on the construction of pyramids and other monumental structures (Gillings, 1972).

The ancient Greeks made significant contributions to the understanding and formalization of ratios, as exemplified by the work of the renowned mathematician Euclid. In his magnum opus, “Elements,” Euclid laid the foundation for the axiomatic system of geometry and incorporated ratios as a fundamental concept in his proofs (Euclid & Heath, 1956). The exploration of ratios continued to flourish in the subsequent centuries, culminating in the work of mathematicians such as Leonardo Fibonacci and Isaac Newton, who utilized ratios in their respective fields of number theory and calculus.

Ratios in Machine Learning: Logits

In recent years, the application of ratios has extended into the realm of machine learning, particularly in the form of logits. Logits are used in logistic regression, a widely employed statistical method for predicting binary outcomes (Hastie, Tibshirani, & Friedman, 2001). In the context of machine learning, logistic regression is utilized to model the probability of a particular class or event occurring, such as whether an email is spam or legitimate.

The logit function, also known as the log-odds function, is derived from the logistic function and is defined as the natural logarithm of the odds of the probability of an event occurring (Bishop, 2006). Specifically, the logit function can be expressed as:

logit(p) = ln(p / (1 - p))

where p is the probability of the event occurring. This function transforms the probability values, which lie in the range [0, 1], into log-odds values that span the entire real number line. The logit function plays a pivotal role in logistic regression, as it helps establish a linear relationship between the input features and the binary outcome. Consequently, the use of logits enables the application of powerful linear models to predict complex, nonlinear relationships, epitomizing the versatility and adaptability of ratios in modern scientific endeavors.

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Ratios, the bedrock of mathematical relationships, have permeated nearly every aspect of human knowledge and exploration. From their inception in ancient civilizations to their contemporary applications in machine learning, the profound influence of ratios on our understanding of the world remains indisputable. As we continue to push the boundaries of knowledge, the ever-present role of ratios in illuminating complex relationships and elucidating the mathematical structures that govern our existence will undoubtedly persist.

For More Information

To learn more about ratios and their applications, please visit the following resources:

  1. Khan Academy: Ratios, rates, and proportions
  2. MathIsFun: Ratio
  3. StatQuest: Logistic Regression


  1. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
  2. Euclid & Heath, T. L. (1956). The Thirteen Books of Euclid’s Elements. Dover Publications.
  3. Gillings, R. J. (1972). Mathematics in the Time of the Pharaohs. MIT Press.
  4. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The Elements of Statistical Learning. Springer.
  5. Jones, O. D. (2020). An Introduction to Probability and Statistics. Wiley.
  6. Katz, V. J. (1998). A History of Mathematics: An Introduction. Addison Wesley Longman.
  7. Stapel, E. (2021). Ratios and Proportions. Purplemath. Retrieved from https://www.purplemath.com/modules/ratio.htm

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