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Trigonometric Equations01:30

Trigonometric Equations

Trigonometric equations involve one or more trigonometric functions and arise frequently in mathematical modeling. These equations may be either identities, which are valid for all values of the variable, or conditional equations, which hold true only for specific values. The process of solving trigonometric equations typically involves both algebraic techniques and the use of fundamental properties of trigonometric functions.Some trigonometric equations resemble standard algebraic forms and...
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Trigonometric Functions of Real Numbers

The unit circle—a circle with a radius of one, centered at the origin of the coordinate plane—serves as the foundational framework for defining trigonometric functions. In this context, arc length refers to the distance measured along the circumference of the circle between two points, and it provides a way to represent real numbers geometrically. Each real number t corresponds to an arc length measured counterclockwise from the positive x-axis around the circle. The coordinates of a point on...
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Consider a scenario where a circular shaft is subject to torque that remains within the boundaries of Hooke's Law, avoiding any permanent deformation. So, the formula for shearing strain is revisited. This formula is multiplied by the modulus of rigidity, and then Hooke's Law for the shearing stress and strain is applied. As a result, the equation for shearing stress in a shaft can be derived.
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Suppose a car moves on flat ground and turns to the left. The centripetal force causing the car to turn in a circular path is due to friction between the tires and the road. For this, a minimum coefficient of friction is needed, or the car will move in a larger-radius curve and leave the roadway. Let's now consider banked curves, where the slope of the road helps in negotiating the curve. The greater the angle of the curve, the faster one can take the curve. It is common for race tracks for...

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Testing for seasonality using circular distributions based on non-negative trigonometric sums as alternative hypotheses.

Statistical methods in medical research·2011
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Circular distributions based on nonnegative trigonometric sums.

Biometrics·2004
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Related Experiment Video

Updated: Jul 13, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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Models for circular-linear and circular-circular data constructed from circular distributions based on nonnegative

J J Fernández-Durán1

  • 1Department of Statistics, Instituto Tecnológico Autónomo de México, Río Hondo No. 1, Col. Tizapán San Angel, C.P. 01000, México D.F., México. jfdez@itam.mx

Biometrics
|August 11, 2007
PubMed
Summary

This study introduces flexible joint distributions for circular-linear and circular-circular data using nonnegative trigonometric sums. These models capture complex dependence patterns, including multimodality and skewness, with applications in environmental science and bioinformatics.

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Area of Science:

  • Statistics
  • Bioinformatics
  • Environmental Science

Background:

  • Existing methods for constructing joint distributions of circular and linear variables (circular-linear) or pairs of circular variables (circular-circular) have limitations in flexibility.
  • Flexible models require the joining circular density to accommodate multimodality and skewness for diverse dependence patterns.

Purpose of the Study:

  • To present and apply joint distributions for circular-linear and circular-circular data using nonnegative trigonometric sums.
  • To demonstrate the utility of these flexible models in real-world applications.

Main Methods:

  • Construction of joint distributions based on circular distributions derived from nonnegative trigonometric sums.
  • Application of these methods to two distinct datasets: air pollution data (circular-linear) and protein structure data (circular-circular).

Main Results:

  • The developed methods successfully model complex dependence structures in both circular-linear and circular-circular data.
  • The models exhibit flexibility in capturing multimodality and skewness, essential for realistic data representation.

Conclusions:

  • Nonnegative trigonometric sums provide a powerful framework for constructing flexible joint distributions in circular statistics.
  • These distributions offer valuable tools for analyzing complex dependencies in environmental and biological data.