Correlation Coefficient

  

Categories: Metrics, Trading

The Correlation Coefficient, or r value, is the mathematical measure of the correlation between two variables, or how close the points on a scatterplot are to the line of best fit as determined by linear regression.

Values of r range from -1 to 1. Values of r closer to -1 and 1 represent data points very close to the best fit line, either with a negative slope (for negative r values) or with a positive slope (positive r). Values of r closer to 0 represent data points farther from the line (and more cloud-like in appearance).

We typically calculate r using technology. Almost no one does it by hand. Seriously, use a graphing calculator or spreadsheet or website to do it for you. If the r-value for data relating annual salary to days of vacation per year is 0.94, we can expect the scatterplot of the data to be a set of points in a nearly perfect line from the lower left of the plot to the upper right. We can also assume there’s a very strong correlation between those two variables. The one variable doesn’t have to cause the other to change, but they are correlated...somehow.

Related or Semi-related Video

Finance: What is Inverse Correlation?1 Views

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Finance Allah Shmoop What is inverse correlation All right It's

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the relationship between two variables where we can expect an

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increase in one variable to be paired with a decrease

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in another variable Alright in plain English correlation When it

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rains you get wet Inverse correlation When it rains you

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get dry Correlation You have a big brain So your

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smart inverse correlation like we're thinking dinosaurs Maybe they had

00:30

big brains all of it But if they had big

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brains the bigger their brain Well the dumber thing God

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Well that would be an inverse correlation right Correlation You

00:38

drive a fast flashy cars so you probably have a

00:41

small garage Alright Inverse correlation You drive a fast flashy

00:45

car and everything else about you is enormous Yeah So

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in a word in versus just opposite check out inverse

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correlations in this table showing to data sets Note how

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the returns on investment in gold increase while the returns

00:58

on investment in Pat's for cats decrease over the same

01:02

timeframe And instead of hats for cats you could have

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seen the stock market because people typically retreat into gold

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when they're nervous about equities So this is really not

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a bad inverse correlation right Okay So let's take a

01:13

look at a scatter plot of the same two data

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sets See how the data points get lower and lower

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as we go farther to the right Yeah that's because

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the X values or the returns on the gold investment

01:22

increase or go farther to the right Well then the

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Y values or returns on the hats for cats equities

01:28

investment decrease or go closer to the Y axis Well

01:31

sometimes we want to put a number on how strong

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or weak The inverse correlation is between any two pairs

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of variables So basically we're trying to determine if the

01:40

inverse correlation is one that follows a very steady amount

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of decrease in one variable for a fixed amount of

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increase in the other or if the amount of decrease

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in one variable fluctuates for a fixed increase in the

01:53

other or more simply how closely the points on the

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scatter plot are to an imaginary line like this thing

01:59

that best represents them Right That's an r squared correlation

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there we'll get to it So the measure of how

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strong the correlation is between these two variables is called

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yes the correlation coefficient or our value Well a strong

02:13

inverse correlation would have the data points all cozied up

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to the best fitting line coincidently called the line of

02:21

best fit Kind of like all of your new you

02:23

know friends after you win the ninety million dollars Powerball

02:27

lottery Well a week inverse correlation would have the data

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spread out away from the line and best fit So

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there's really no clustering here It's just a whole bunch

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of dots on a graph that don't really tell you

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much of anything you know like the location of kids

02:41

at a middle school dance compared to the location of

02:44

the dance floor So how do we find the our

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value to determine how strong our correlation is inverse or

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otherwise Well typically people use some sort of technological gadgets

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such as a graphing calculator spreadsheet or a nap Let's

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take that investment data from before comparing Gold returns to

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returns on equity and hats for cats and walked through

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how you'd find the R value using a spreadsheet So

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open your favour like Excel or Google sheets or OpenOffice

03:08

Cal Core sells for days or and whatever you use

03:11

We're using Excel for this demo but they all work

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in basically the same Put the data for the gold

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returns without the presented signs in the first column Put

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the data from hats for cats also without the presented

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signs In the second column like that go to any

03:22

blank cell like the top selling the third column C

03:25

one there Then click on the formula's tab Choose the

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Mohr Functions button and Anju Statistical See the coral option

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there you selected Once we choose correlation we'll get a

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pop up asking us to define the two sets of

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data highlight on ly the first column of data It

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should load that set of cells in the top row

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of the pop up All right now left Click in

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the second row of the pop up Highlight on ly

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the second column of data and well should slide those

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cells right into place Got it Okay So click okay

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and boom instant our value Well it should show up

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in the cell you picked to enter the Correlation Command

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See one If you followed our Russians to et You

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know t looks like our correlation has the strength of

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negative point eight four Oh one Okay But like what

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does that mean Is that strong or weak Positive Negative

04:10

Well for inverse correlations there's a range of values between

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zero and negative one that we consider strong medium and

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weak Inverse correlations like strong inverse correlation is generally run

04:21

from about negative point seven The negative one These will

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be scatter plots where the points are quite close to

04:26

the best fit line like they're extremely counter or inversely

04:30

correlated Like if you found that every guy over forty

04:34

who drove a red convertible portion wore a gold chain

04:37

necklace there had a really big garage well then it

04:40

would be negatively correlated to our expectations Okay medium inverse

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correlation is generally run from about negative point For the

04:48

negative point seven seas will be scattered plots with points

04:50

group less closely around the line A best fit Think

04:53

about it like well maybe half to two thirds of

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all the guys have a small garage versus a big

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garage Yeah something like that Weak inverse correlation is generally

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run from well zero toe negative point for easily scatter

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plots with almost no riel tight grouping Maybe there's some

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trend if you really study it hard and think about

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Roar Shack But there's really no correlation between the size

05:13

of your garage and when you're driving a convertible red

05:15

portion you wear a gold chain necklace All right one

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thing we have to be careful about With the inverse

05:19

correlation XYZ thie implied value judgments that mistakenly get applied

05:22

to the two variables that are inversely correlated like in

05:25

our correlation calculation on returns we saw the gold investment

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rise while the equity in hats for cats investment had

05:31

decreasing returns and basically was saying that people retreated putting

05:36

there cash into gold when they were nervous about the

05:38

equity markets Well that doesn't mean gold will always be

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the one to increase While hats for cats decreases Beyond

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the obvious changes in the market that might make gold

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suddenly tank an inverse correlation means that gold could be

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the investment with decreasing returns While hats for cat shows

05:54

increasing returns right the correlation thing is just showing that

05:57

they're inversely correlated When one goes up the other goes

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down It could be that well when one goes down

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the other one goes up Got it by the biggest

06:04

takeaway smelling That inverse correlation means that as one variable

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increases in general the other variable decreases particularly when you

06:12

have high R squared correlations there Also we can calculate

06:15

how strong the correlation is by finding the R value

06:18

which we typically do using some technological do Dad Yeah

06:21

thank you Google Sheets and excel in all that stuff

06:23

Inverse correlation Czar values run from zero to negative one

06:27

with strong being in close to negative one in week

06:30

being close to zero And we're hoping there's an infamous

06:33

correlation between the number of matches we get on tinder

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and the number of dates when we get that in

06:37

badly But well so far the data is not backing 00:06:41.135 --> [endTime] us up Change our picture what Oh

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