Covariance vs. Correlation

Covariance and correlation are two statistical concepts that are closely related, both conceptually and by their name. The excerpts below are from a concise article that differentiates them.

Difference Between Covariance and Correlation

“Correlation is a special case of covariance which can be obtained when the data is standardised. Now, when it comes to making a choice, which is a better measure of the relationship between two variables, correlation is preferred over covariance, because it remains unaffected by the change in location and scale, and can also be used to make a comparison between two pairs of variables.”

Key Differences Between Covariance and Correlation

“The following points are noteworthy so far as the difference between covariance and correlation is concerned:

  1. “A measure used to indicate the extent to which two random variables change in tandem is known as covariance. A measure used to represent how strongly two random variables are related known as correlation.
  2. “Covariance is nothing but a measure of correlation. On the contrary, correlation refers to the scaled form of covariance.
  3. “The value of correlation takes place between -1 and +1. Conversely, the value of covariance lies between -∞ and +∞.
  4. “Covariance is affected by the change in scale, i.e. if all the value of one variable is multiplied by a constant and all the value of another variable are multiplied, by a similar or different constant, then the covariance is changed. As against this, correlation is not influenced by the change in scale.
  5. “Correlation is dimensionless, i.e. it is a unit-free measure of the relationship between variables. Unlike covariance, where the value is obtained by the product of the units of the two variables.”

Source

Difference Between Covariance and Correlation by Surbhi S

Invalidating Bloodletting with Science

Blood on the Tracks – Podcast Episode 38

Learn about a piece of epidemiological history: one of the earliest examples of population-level clinical studies influencing medical practice. This podcast tells the story of how French physician Pierre Charles Alexandre Louis studied a group of patients and ended up discovering quantitative evidence on the detriment of bloodletting. Learning the history helps place these tools in a broader context, which isn’t crucial, but interesting nonetheless.

Listen to the Podcast here

The first population study in history was born out of a dramatic debate involving leeches, “medical vampires,” professional rivalries, murder accusations, and, of course, bloodletting, all in the backdrop of the French Revolution. The second of a multipart series on the development of population medicine, this episode contextualizes Pierre Louis’ “numerical method,” his famous trial on bloodletting, and the birth of a new way for doctors to “know”.


Source

Bedside Rounds: Episode 38: Blood on the Tracks (PopMed #2)

Public Health in the Precision-Medicine Era

Ronald Bayer, Ph.D., and Sandro Galea, M.D., Dr.P.H.

“The NIH’s most recent Estimates of Funding for Various Research, Condition, and Disease Categories report (www.report.nih.gov/categorical_spending.aspx) shows, for example, that total support in fiscal year 2014 for research areas including the words ‘gene,’ ‘genome,’ or ‘genetic’ was about 50% greater than funding for areas including the word ‘prevention.’…The proportion of NIH-funded projects with the words ‘public’ or ‘population’ in their title, for example, has dropped by 90% over the past 10 years, according to the NIH Reporter.”

“Without minimizing the possible gains to clinical care from greater realization of precision medicine’s promise, we worry that an unstinting focus on precision medicine by trusted spokespeople for health is a mistake — and a distraction from the goal of producing a healthier population.”

Read More


Source

NEJM: Public Health in the Precision-Medicine Era by Ronald Bayer, Ph.D., and Sandro Galea, M.D., Dr.P.H.

Using It or Losing It? The Case for Data Scientists Inside Health Care

“As much as 30% of the entire world’s stored data is generated in the health care industry. A single patient typically generates close to 80 megabytes each year in imaging and electronic medical record (EMR) data. This trove of data has obvious clinical, financial, and operational value for the health care industry, and the new value pathways that such data could enable have been estimated by McKinsey to be worth more than $300 billion annually in reduced costs alone…Read More


Source

NEJM Catalyst: Using It or Losing It? The Case for Data Scientists Inside Health Care by Marco D. Huesch, MBBS, PhD & Timothy J. Mosher, MD

The Power of Mindsight

Prolific child neurologist and author, Dr. Siegel uses a variety of images to explain what he thinks should be taught to children in America.


He uses his hand to provide a concrete visual model of the brain.

Related image


He also summarizes his “mindsight” model as a combination of brain, mind, and relationships. He expounds on the over emphasis of brain, or somatic, focus in mainstream culture and medical school. He argues we neglect the education of mind and relationships after children graduate kindergarten.

Image result for siegel brain mind relationships triangle


Source

TEDx: The Power of Mindsight by Daniel J. Siegel, M.D. – 10/18/09

A Practical Introduction to Factor Analysis

A Practical Introduction to Factor Analysis: Exploratory Factor Analysis

Survey questions or “items” (e.g., on a scale from 1 to 5, how strongly do you agree with the following statement…) may be repeated measures of certain underlying “factors”. Where factors are a true underlying construct that a survey attempts to measure. For example, a factor a survey may attempt to measure might be the anxiety caused by learning statistical analysis (using SPSS software). Factor analysis looks at understanding what is really being measured by multiple questions in a survey.

There are a ton of new concepts in this class, but online resources are often a more simple and clear way to learn.

fig03b


UCLA Institute for Digital Research and EducationA Practical Introduction to Factor Analysis: Exploratory Factor Analysis

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