## Correlation Statistics

## Metric | ## Formula | ## Description | ## Result |
---|---|---|---|

## Pearson Correlation Coefficient / R Correlation |
r = Σ(xi - x̄)(yi - ȳ) / √(Σ(xi - x̄)2Σ(yi - ȳ)2 ) | ## where, x̄ is the mean of x, ȳ is the mean of y and xi,yi are the individual values in sets of x and y | ## 0.9424 |

## Count of dataset 1 | ## Count = n | ## where, n is the Number of items in dataset 1 | ## 18 |

## Count of dataset 2 | ## Count = n | ## where, n is the Number of items in dataset 2 | ## 18 |

## Sample Std Deviation | ## S = Square root((1/ n - 1 ) * ∑ i( x i - x ¯ ) 2) | ## where, n is Number of observations in sample, xi is individual values in sample and x ¯ is Sample mean | ## 6.546 |

## XY Scatter Plot

### Data shows Strong Positive Correlation

## Correlation Analysis / Inference

## Correlation Range | ## Description |
---|---|

## 1 | ## 100% Positive Correlation |

## >= 0.7 < 1 | ## Strong Positive Correlation |

## >= 0.5 < 0.7 | ## Moderate Positive correlation |

## >= 0.3 < 0.5 | ## Low Positive Correlation |

## > 0 <= 0.3 | ## No Correlation |

## 0 | ## No Correlation |

## < 0 >= -0.3 | ## No Correlation |

## >= -0.3 < -0.5 | ## Low Negative Correlation |

## >=-0.5 < -0.7 | ## Moderate Negative Correlation |

## >= -0.7 < - 1 | ## Strong Negative Correlation |

## -1 | ## 100% Negative Correlation |

## Significance of R

### Significance of R is a measure of reliability. It is used to indicate if there is significant linear relationship between the variables and if it can be used for predictions

### If R is significant, it indicates that the coefficient value and Scatter plot can be used for prediction of outcomes.based on the significant linear relationship between 2 variables

### If R is NOT SIGNIFICANT, it means insignificant relationship between the variables and it cannot be used reliably to predict any outcome

### This is used as a pre-requisite test before applying the Pearson's coefficient formula and analyzing

## Frequently Asked Questions on Correlation Coefficient Calculator

Correlation Coefficient is the strength and relationship between two variables and the direction of it (+ve or -ve)

Age vs Height have a positive correlation during the growing period

Calories Consumed vs Weight have a positive correlation

Relative Humidity and Temperature have a negative correlation

Correlation Coefficient is the strength and relationship between two variables and the direction of it (+ve or -ve)

Age vs Height have a positive correlation during the growing period

Calories Consumed vs Weight have a positive correlation

Relative Humidity and Temperature have a negative correlation

R stands for Regression and is the correlation Coefficient between 2 sets of variables.

It is a quantitative measure representing linear correlation coefficient

It indicates whether the 2 sets of data have positive, negative or no correlation.

R stands for Regression and is the correlation Coefficient between 2 sets of variables.

It is a quantitative measure representing linear correlation coefficient

It indicates whether the 2 sets of data have positive, negative or no correlation.

correlation coefficient r = Σ(xi - x̄)(yi - ȳ) / √(Σ(xi - x̄)2Σ(yi - ȳ)2 )

where,where, x̄ is the mean of x, ȳ is the mean of y and xi,yi are the individual values in sample sets of x and y

It ranges between -1 to 1

correlation coefficient r = Σ(xi - x̄)(yi - ȳ) / √(Σ(xi - x̄)2Σ(yi - ȳ)2 )

where,where, x̄ is the mean of x, ȳ is the mean of y and xi,yi are the individual values in sample sets of x and y

It ranges between -1 to 1

100% Positive Strong Correlation

100% Positive Strong Correlation

100% Negative Strong Correlation

100% Negative Strong Correlation

No Correlation

No Correlation

Significance of R is a measure of reliability. It is used to indicate if there is significant linear relationship between the variables and if it can be used for predictions

If R is significant, it indicates the trend line can be used for prediction of outcomes.based on the significant linear relationship between 2 variables

If R is NOT SIGNIFICANT, it means insignificant relationship between the variables and it cannot be used reliably to predict any outcome

Significance of R is a measure of reliability. It is used to indicate if there is significant linear relationship between the variables and if it can be used for predictions

If R is significant, it indicates the trend line can be used for prediction of outcomes.based on the significant linear relationship between 2 variables

If R is NOT SIGNIFICANT, it means insignificant relationship between the variables and it cannot be used reliably to predict any outcome

1

1

1 (positive) and -1 (negative)

1 (positive) and -1 (negative)

Strong Positive Correlation

Strong Positive Correlation

Moderate Positive correlation

Moderate Positive correlation

No Correlation

No Correlation

1 (positive) and -1 (negative)

1 (positive) and -1 (negative)

<-0.5

<-0.5

weak correlation

weak correlation

strong correlation

strong correlation

no correlation

no correlation