Statistics: Covariance vs correlation
(Covariance:)
>> It is a method to find the variance between two variables.1. It is the relationship between a pair of random variables where a change in one variable causes change in another variable.
2. It can take any value between -infinity to +infinity, where the negative value represents the negative relationship whereas a positive value represents the positive relationship.
3. It is used for the linear relationship between variables.
4. It gives the direction of the relationship between variables.
5. It has dimensions.
#success #tableausoftware #dataanalytics
R-VALUE
--------------
** The main result of a correlation is called the correlation coefficient (or "r"). It ranges from -1.0 to +1.0. The closer r is to +1 or -1, the more closely the two variables are related.
**-If r is close to 0, it means there is no relationship between the variables. If r is positive, it means that as one variable gets larger the other gets larger. If r is negative it means that as one gets larger, the other gets smaller (often called an "inverse" correlation)
P-VALUE
------------------------------
If p > .10 → “not significant”
If p ≤ .10 → “marginally significant”
If p ≤ .05 → “significant”
If p ≤ .01 → “highly significant.”
--------------
** The main result of a correlation is called the correlation coefficient (or "r"). It ranges from -1.0 to +1.0. The closer r is to +1 or -1, the more closely the two variables are related.
**-If r is close to 0, it means there is no relationship between the variables. If r is positive, it means that as one variable gets larger the other gets larger. If r is negative it means that as one gets larger, the other gets smaller (often called an "inverse" correlation)
P-VALUE
------------------------------
If p > .10 → “not significant”
If p ≤ .10 → “marginally significant”
If p ≤ .05 → “significant”
If p ≤ .01 → “highly significant.”
P-value
--------------
The p-value is a measure of significance for the trend line. A p-value of 0.05 or less is often considered significant; the smaller the p-value the more significant the model is. A large p-value can indicate that the apparent trend in the data is due to chance, not the factors in the model.
--------------
The p-value is a measure of significance for the trend line. A p-value of 0.05 or less is often considered significant; the smaller the p-value the more significant the model is. A large p-value can indicate that the apparent trend in the data is due to chance, not the factors in the model.
R-squared
-------------
The R-squared is also an important measure when assessing if the model is suitable and tells us whether the model effectively fits our data. The R-squared is measured on a scale from 0-1; the closer to 1 the more effective the model.
#data #analytics #tableau
Statstics:(Correlation):
* It shows whether and how strongly pairs of variables are related to each other.
* Correlation takes values between -1 to +1, wherein values close to +1 represent strong positive
* correlation and values close to -1 represent the strong negative correlation.
* In this variable are indirectly related to each other.
* It gives the direction and strength of the relationship between variables.
* It is the scaled version of Covariance.
* It is dimensionless.
#statsreview#Tableau#Data Analytics
----------------------------------------------------------------
** A correlation coefficient is a value that quantifies the relationship of two or more variables. In linear correlation, the coefficient quantifies the strength and direction of the correlation between the variables.
**One type of correlation coefficient is the Pearson product-moment correlation coefficient, also known as r, which measures linear correlation and provides a value between -1 and +1
** 1 is a total positive correlation
**0 is no correlation
** −1 is a total negative correlation
No comments:
Post a Comment