# CBSE Revision Notes for Class 11 Statistics Economics Chapter 3 – ORGANIZATION OF DATA – Free PDF Download

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Chapter Name | ORGANIZATION OF DATA |

Chapter | Chapter 3 |

Class | Class 11 |

Subject | Statistics Economics Revision Notes |

Board | CBSE |

TEXTBOOK | Statistics Economics |

Category | REVISION NOTES |

**CBSE Class 11 Statistics Economics Revision Notes for ORGANIZATION OF DATA of Chapter 3**

**Organization** of data refers to the systematic arrangement of collected figures (raw data), so that the data becomes easy to understand and more convenient for further statistical treatment .

**Classification** is the process of arranging data into sequences and groups according to their common characteristics of separating them in to different but related parts.

**Characteristics of classification:**

1. Homogeneity

2.Suitability

3. Clarity

4. Flexibility

5. Diversification

**A variable** is a characteristic which is capable of being measured and capable of change in its value from time to time.

**Basis of classification: Raw data can be classified as: 1. Chronological classification:** In such a classification data are classified either in ascending or in descending order with reference to time such as years, quarters, months weeks etc.

**2. Geographical/Spatial classification:**The data are classified with reference to geographical location/place such as countries, states , cities, districts, block etc.

**3. Qualitative classification:**Data are classified with reference to descriptive characteristics like sex, caste, religion literacy etc.

**4. Quantitative classification:**Data are classified on the basis of some measurable characteristics such as height, age, weight, income, marks of students.

**5. conditional classification:**When data are classified with respect to condition, the type of classification is called conditional classification.

A mass of data in its original form is called

**raw data**. It is an unorganized mass of various items.

A characteristic which is capable of being measured and changes its value overtime is called a variable. It is of two types.

(a) Discrete

(b) Continuous

**Discrete:**Discrete variable are those variables that increase in jumps or in complete numbers and are not fractional. Ex.-number of student in a class could be 2, 4, 10, 15,, 20, 25, etc. It does not take any fractional value between them.

**Continuous variable:**Continuous variables are those variables that can takes any value i.e. integral value or fractional value in a specified interval.Ex- Wages of workers in a factory.

**A frequency distribution**is a comprehensive way to classify raw data of a quantitative variable. It shows how different values of a variable is distributed in different classes along with their corresponding class frequencies.

**The class mid-point or class mark**is the middle value of a class. It lies halfway between the lower class limit and the upper class limit of a class and can be ascertained in the following manner.

**Class mid-point = upper class limit + lower class limit / 2.**

**Class frequency:**It means the number of values in a particular class.

**Class width:-**It is the difference between the upper class limit and lower class limit

**Class width = upper class Limit – Lower class Limit**

**Class Limits:-**There are two ends of a class. The lowest value is called lower class limit and highest value is called upper class limit.

The classes, by the exclusive method is formed in such a way that the upper class limit of one class equals the lower class limit of the next class. eg 0-10, 10-20.

In comparison to the exclusive method, the inclusive method does not excludes the upper class limit in a class interval. It includes the upper class in a class. Thus both class limits are parts of the class intervals e.g., 0-9, 10-19.

The classification of data as a frequency distribution has an inherent short coming. While it summarizes the raw data making it concise and comprehensible. It does not show the details that are found in raw data. So there is a loss of information in classifying raw data.

Classification of data implies conversion of raw data in to statistical series.

**The difference between Univariate and Bivariate Frequency distribution**

Basis | Univariate Frequency distribution | Bivariate Frequency distribution |

Meaning | When data is classified on the basis of single variable,the distribution is known as univariate frequency distribution. | when data is classified on the basis of two variables, the distribution is known as bivariate frequency distribution. |

Alternate Name | One-way frequency | Two-way frequency |

Example | Height of students in a class | Height and weight of students in a class |

Broadly statistical series are of two types.

**Types of series**

1. Individual series

2. Frequency series

a. Discrete series Or frequency array

b. Frequency distribution or continuous series

**Individual series** are those series in which the items are listed singly. For example:

Sr. No. of workers | Daily wages(in Rs.) |

1 | 25 |

2 | 50 |

3 | 35 |

4 | 40 |

5 | 20 |

6 | 45 |

**A discrete series** or frequency array is that series in which data are prescribed in a way that exact measurements of items are clearly shown. The example in following table illustrates a frequency array.

**Frequency array of the size of household**

Size of the household | Number of household (Frequency) |

1 | 5 |

2 | 15 |

3 | 25 |

4 | 35 |

5 | 10 |

6 | 5 |

**A continuous series:** It is that series in which items cannot be exactly measured. The items assume a range of values and are placed within the range of limits. In other words, data are classified into different classes with a range, the range is called class-intervals.

Frequency distribution or continuous series

Marks | Frequency |

10-20 | 4 |

20-30 | 5 |

30-40 | 8 |

40-50 | 5 |

50-60 | 4 |

60-70 | 3 |