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Correlations

Correlations

Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...

Correlation

Correlation

In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:

Calculating and Interpreting the Linear Correlation Coefficient

Calculating and Interpreting the Linear Correlation Coefficient

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:

Coefficient of Correlation

Coefficient of Correlation

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...

Correlation and Causation

Correlation and Causation

Correlation and CausationStatistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. A relationship between variables shows correlation, but it does not show cause-and-effect. A direct cause-and-effect relationship requires additional controlled experiments. If no consistent relationship exists between the variables, then there is no correlation.Correlation versus CausationIf the dependent variable increases or decreases when the...

Disorders of the Male Reproductive System

Disorders of the Male Reproductive System

Men's health issues are increasingly recognized as significant, with several conditions posing common threats. Among these, testicular cancer is especially prevalent in younger men, particularly those aged 20 to 35 years. The disease often manifests as a painless mass in the testicles, sometimes accompanied by a sensation of heaviness or a dull ache.
Prostate disorders are another major concern. These conditions can impair urinary flow due to the prostate's location around the urethra. Symptoms...

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Related Experiment Video

Updated: Jun 13, 2026

miRNA Expression Analyses in Prostate Cancer Clinical Tissues

miRNA Expression Analyses in Prostate Cancer Clinical Tissues

Published on: September 8, 2015

Correlations between meteorological parameters and prostate cancer.

Sophie St-Hilaire¹, Sylvio Mannel, Amy Commendador

¹Department of Biological Sciences, Idaho State University, Pocatello, ID 83209, USA. sthisoph@isu.edu

International Journal of Health Geographics

|April 23, 2010

Summary

Prostate cancer incidence is higher in colder, drier U.S. regions, suggesting meteorological factors beyond vitamin D influence disease patterns. These climate factors may affect pesticide pollutant levels linked to cancer risk.

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A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer

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Published on: March 6, 2018

Area of Science:

Environmental Epidemiology
Cancer Research
Meteorology

Background:

Prostate cancer exhibits a north-south incidence gradient in the U.S.
Low vitamin D levels partially explain this spatial pattern.
Other meteorological factors may contribute to prostate cancer distribution.

Purpose of the Study:

Investigate the association between meteorological parameters and prostate cancer incidence.
Identify environmental factors beyond vitamin D that explain spatial variations in prostate cancer.

Main Methods:

Ecological study design using U.S. county-level data.
Statistical analysis controlling for covariates like age, race, and pesticide use.
Examination of correlations between climate variables and prostate cancer rates.

Main Results:

Colder temperatures and drier climates correlate with higher prostate cancer incidence.
This association persists after controlling for multiple environmental and demographic factors.
High snowfall counties show a positive correlation between crop land and prostate cancer incidence.

Conclusions:

Meteorological factors partially explain prostate cancer incidence patterns in the U.S.
Climate influences the environmental fate of pollutants linked to prostate cancer.
Findings suggest a role for climate-mediated pollutant exposure in prostate cancer etiology.