Venn diagram is a way to visually represents relationship between groups of entities or objects. Venn diagrams are comprised of circles where each circle represents a whole set. Venn diagram can have unlimited circles but generally two or three circles are preferred otherwise the diagram becomes too complex.

## Steps to draw a Venn Diagram

Consider the following sets of people:

**Cricket Players**– $ C = \{ Ram, Shyam, Mohan, Rohan, Ramesh, Suresh \} $**Hockey Players**– $ H = \{ Ramesh, Naresh, Mahesh, Leela, Sunita \} $

Step 1: Draw a rectangle and label it as players.

Step 2: Draw two circles and label them as Cricket and Hockey. Make sure that circles are overlapping each other.

Step 3: Write Names inside the circle as relevant. Common name(s) should fall within common region.

## Union

Union ($ \cup $) represents a set where items are present in all categories but are not repeated.

### Example

**Problem Statement:**

Draw a Venn diagram of $ C \cup H $.

**Solution:**

Step 1: Determine players who are either playing cricket or hockey. Draw them as following:

$ C \cup H = \{ Ram, Shyam, Mohan, Rohan, Ramesh, Suresh, Naresh, Mahesh, Leela, Sunita \} $.

## Intersection

Intersection ($ \cap $) represents a set where items are present in both categories.

### Example

**Problem Statement:**

Draw a Venn diagram of $ C \cap H $.

**Solution:**

Step 1: Determine players who are playing cricket and hockey both. Draw them as following:

$ C \cap H = \{ Ramesh \} $.

## Difference

Difference ($ – $) represents a set where items are present only in one category and not in other one.

### Example

**Problem Statement:**

Draw a Venn diagram of $ C – H $.

**Solution:**

Step 1: Determine players who are playing cricket only. Draw them as following:

$ C – H = \{ Ram, Shyam, Mohan, Rohan, Suresh \} $.

Table of Contents

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2.statistics analysis of variance

4.statistics arithmetic median

8.statistics best point estimation

9.statistics beta distribution

10.statistics binomial distribution

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13.statistics central limit theorem

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18.statistics cluster sampling

19.statistics cohens kappa coefficient

21.statistics combination with replacement

23.statistics continuous uniform distribution

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25.statistics coefficient of variation

26.statistics correlation coefficient

27.statistics cumulative plots

28.statistics cumulative poisson distribution

30.statistics data collection questionaire designing

31.statistics data collection observation

32.statistics data collection case study method

34.statistics deciles statistics

36.statistics exponential distribution

40.statistics frequency distribution

41.statistics gamma distribution

43.statistics geometric probability distribution

46.statistics gumbel distribution

49.statistics harmonic resonance frequency

51.statistics hypergeometric distribution

52.statistics hypothesis testing

53.statistics interval estimation

54.statistics inverse gamma distribution

55.statistics kolmogorov smirnov test

57.statistics laplace distribution

58.statistics linear regression

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60.statistics logistic regression

63.statistics means difference

64.statistics multinomial distribution

65.statistics negative binomial distribution

66.statistics normal distribution

67.statistics odd and even permutation

68.statistics one proportion z test

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73.statistics poisson distribution

74.statistics pooled variance r

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78.statistics probability multiplicative theorem

79.statistics probability bayes theorem

80.statistics probability density function

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92.statistics residual analysis

93.statistics residual sum of squares

94.statistics root mean square

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99.statistics signal to noise ratio

100.statistics simple random sampling

102.statistics standard deviation

103.statistics standard error se

104.statistics standard normal table

105.statistics statistical significance

108.statistics stem and leaf plot

109.statistics stratified sampling

112.statistics tdistribution table

113.statistics ti 83 exponential regression

114.statistics transformations

116.statistics type i amp ii errors

119.statistics weak law of large numbers