Compute mean, median, std deviation - Enter your data as comma separated values or with spaces. You can also import csv or any data file with delimiters

## Statistical Analysis Result Table

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

## Count | ## Count = n | ## n is the Number of items in dataset | ## 12 |

## Sum | ## S = a1+a2+...+an | ## where, a1,a2,an, are data values | ## 2218.15 |

## Min | ## Min = 1/2 (r+s-|r-s|) | ## where, r is first number and s is second number | ## 50.78 |

## Max | ## Max = 1/2(r+s+|r-s|) | ## where, r is first number and s is second number | ## 469.82 |

## Range | ## Range = Max - Min | ## where, max is maximum number, min is minimum number | ## 419.04 |

## Mean | ## M = 1/n(a1+a2+...+an) | ## where, n is the number of observation and a1,a2,an are data values | ## 184.846 |

## Median | ## M = [(n + 1) / 2]th term | ## where, n is the number of observation | ## 151.05 |

## Mode | ## M = L + (fm-f1)h /(fm-f1)+(fm-f2) | ## where L = Lower limit Mode of modal class,fm = Frequency of modal class,f1 = Frequency of class preceding the modal class, f2= Frequency of class succeeding the modal class,h = Size of class interval | ## 378.32,## 257.63,## 469.82,## 184.9,## 50.78,## 240.71,## 57.35,## 87.8,## 117.2,## 105.11,## 53.82,## 214.71 |

## Quartile | ## Q1 = ((n+1)/4)## Q2 = ((n+1)/2)## Q3 = (3(n+1)/4) | ## where, q1 is first quartile, q2 is second quartile, q3 is third quartile and n is count numbers | ## 64.963## 151.05## 253.4 |

## Outlier | ## U = q3 + 1.5 (q3-q1)## L = q1 - 1.5 (q3-q1) | ## where, U is Upper Limit and L is Lower Limit and q1 and q3 are quartiles | ## none |

## Variance | ## S2 = 1 n - 1 ∑ i = 1 n ( x i - x ¯ ) 2 | ## where, n is Number of observations in sample, xi is ith observation in the sample and x ¯ is Sample mean | ## 16641.75 |

## Standard Deviation | ## S = 1 n - 1 ∑ i = Square root(1 n ( x i - x ¯ ) 2) | ## where, n is Number of observations in sample, xi is ith observation in the sample and x ¯ is Sample mean | ## 129.003 |

## Standard Error | ## SE = SD/Square root(n) | ## where, SE is Standard Error, SD is standard deviation and n is total numbers | ## 37.24 |

## Sample Variance | ## S2 = (1/ n - 1 ) * ∑ i( x i - x ¯ ) | ## where, n is Number of observations in sample, xi is individual values in sample and x ¯ is Sample mean | ## 18154.636 |

## Sample Standard 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 | ## 134.739 |

## Sample Standard Error | ## SSE = SSD/Square root(n) | ## where, SSE is Sample Standard Error, SSD is sample standard deviation and n is total numbers | ## 38.896 |

## Population Statistics

### In Population Statistics, the dataset represents the full data and the exact variance, standard deviation is calculated

### Example is Scores of Students in a class, temperature recorded in a month

### Population Statistics

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

## Variance | ## S2 = 1 n - 1 ∑ i = 1 n ( x i - x ¯ ) 2 | ## where, n is Number of observations in sample, xi is ith observation in the sample and x ¯ is Sample mean | ## 16641.75 |

## Standard Deviation | ## S = 1 n - 1 ∑ i = Square root(1 n ( x i - x ¯ ) 2) | ## 129.003 | |

## Standard Error | ## SE = SD/Square root(n) | ## where, SE is Standard Error, SD is standard deviation and n is total numbers | ## 37.24 |

## Sample Statistics

### In Sample Statistics, it represents a sampling of the data and that is used to project for an entire category

### Example is Average Height of Male and Females in different countries

### Sample Statistics

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

## Sample Variance | ## S2 = (1/ n - 1 ) * ∑ i( x i - x ¯ ) | ## where, n is Number of observations in sample, xi is individual values in sample and x ¯ is Sample mean | ## 18154.636 |

## Sample Standard Deviation | ## S = Square root((1/ n - 1 ) * ∑ i( x i - x ¯ ) 2) | ## 134.739 | |

## Sample Standard Error | ## SSE = SSD/Square root(n) | ## where, SSE is Sample Standard Error, SSD is sample standard deviation and n is total numbers | ## 38.896 |

### 68-95-99.7 rule states that in a typical normal distribution, 68% of the data points fall within +/- 1 standard deviation of the mean.

### 95% within 2 standard deviation from the mean Almost 100% within 3 standard deviations from the mean