242.4K
ABCorrelation may not equal causation, but it can equal amusement. #correlation #correlationdoesnotequalcausation #logic #random #funny #forthenerds
@abraham.piper
#Correlation One
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EQThe Secret to Understanding Correlation Coefficients #statistics #math #datascience #correlation #Manim Master the Pearson Correlation Coefficient in seconds! This video breaks down the complex world of statistics by visualizing how 'r' values change across different scatter plots. From strong positive correlations (+0.95) to strong negative correlations (-0.95), you will see exactly how data points align with the line of best fit.
@equationsinmotion
171.6K
ERFrom Tyler vigen’s spurious correlations #correlationdoesnotequalcausation
@ericryanericryan
173.6K
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GEThe Pearson correlation coefficient (r) is a statistical measure that indicates the strength and direction of a linear relationship between two continuous variables.
Its values range from -1 to 1, where 1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear correlation.
Generally, values closer to -1 or 1 represent strong correlations, while those near 0 suggest weak or no correlation. A positive correlation means that as one variable increases, the other tends to increase, whereas a negative correlation implies that as one variable increases, the other tends to decrease.
The coefficient of determination (r²) is derived by squaring the Pearson correlation coefficient and represents the proportion of variance in one variable that is predictable from the other. For example, if r = 0.8, then r² = 0.64, meaning 64% of the variability in one variable can be explained by the linear relationship with the other.
C: 3 minute data science
#machinelearning #deeplearning #statistics #computerscience #coding #mathematics #math #physics #science #education
@getintoai
201.3K
CRWhat does "correlation doesn't equal causation" actually mean?
@crashcourse
0
622.4K
SHMistakes in our thinking are more common than we’d like to admit.
Sometimes, we mistakenly connect two things, assuming one caused the other.
It’s a pitfall we encounter all too often, especially when we’re interpreting statistics and research.
And let’s not forge the media’s role in this! Sensationalist headlines that link unrelated topics are like magnets for our attention – hello click bait!
Take, for instance, Time Magazine’s 2014 article. They reported on a study that found bullies had lower risk of chronic diseases, with the headline, “Bullying Is Good For Your Health.” 🤔
So, a word to the wise:
Always check your fact from fiction.
Have you come across this before?👇
(The ‘spurious correlation’ shared in the video was discovered by Harvard Law School student Tyler Vigen).
______
🔥P.S. My LAST FREE MASTERCLASS is in less than 10 hours! Last chance to join me live to level-up your career. Register using the link in my profile & stories!
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#correlation #spuriouscorrelation #criticalthinking #decisionmaking
@shadezahrai
837
ST“Correlation in just 5 seconds!”
#correlation
#statistics
#statisticsmadeeasy
#spearmanrank
@studywithdrdileep
9.3K
FAKARL PEARSON COEFFICIENT OF CORRELATION || BUSINESS STATISTICS-1 || PART-1 || UNIT-5|| SEMESTER-3
@fab_ali_khan
3.6M
DRIn today’s media environment, it’s easy to fall for flashy graphs and bold claims said confidently.
But it’s vital to remember that correlation doesn’t mean causation. Understanding this protects you from being misled by fear tactics, fake science, and propaganda. 🛑
#medicalmisinformation #nutritionmisinformation #correlationdoesnotequalcausation
@drjessicaknurick
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