## Categories of Statistics

There are two **Categories of Statistics**. These are statistics in singular and statistics in plural.

# Statistics in Singular as one of the Categories of Statistics

Statistics in singular is one of the Categories of Statistics. It is the scientific study of the principles and methods applied in collection, organization, presentation, analysis and interpretation of numerical data. Statistics as a field of study is concerned with the following activities:

- Collection, organization, presentation, and analysis of data
- Making inferences about a body of data when only a part of the data is observed, and
- Interpretation and communication of the results of the first two activities

**Statistics in Plural as one of the Categories of Statistics**

Statistics in plural is one of the Categories of Statistics. Systematically collected and aggregated numerical data. Statistical data have the following main characteristics;

- aggregates of facts e.g. total sales in a month
- multiplicity of causes e.g. total demand is a function of several factors
- numerically expressed
- enumerated or estimated according to a reasonable standards of accuracy e.g. 90% accuracy
- collected in a systematic manner
- collected for a predetermined purpose e.g. to determine economic growth rate
- comparable i.e. placed in relation to each other e.g. chronological (time wise) and geographical comparisons

## Types of Data in Categories of Statistics

a) Source of data

There are two sources of data in Categories of Statistics. Primary data is data collected directly or indirectly for the first time. It comprises of measurements observed and recorded as part of an original study and secondary data is data already collected and recorded data by other individuals or agencies for a purpose

b) Measurement of data

There are also two sources of measurement of data in Categories of Statistics. They are quantitative data that can be measured directly and expressed in numerical terms e.g. age, height and qualitative data that cannot be measured directly and expressed in numerical terms e.g. Intelligence, gender, attitudes etc

**Variable in Categories of Statistics**

A variable in Categories of Statistics is a characteristic that assumes different values for different entities e.g. heights of people. Types of variables are:

- Quantitative variable which can be measured directly and expressed in numerical terms e.g. height
- Qualitative variable that cannot be measured directly and expressed in numerical terms e.g. Intelligence, gender,
- Discrete variable that assumes only whole numbers e.g. number of people.
- Continuous variable that assumes both whole numbers and fractions e.g. height

**Limitations of Categories of Statistics**

The limitations of Categories of Statistics are;

- Statistics does not deal with isolated measurement
- Statistics deals only with quantitative characteristics
- Statistical results are true only on average
- Statistics is only a means and not an end.
- Statistics can be misused

**Statistical Inquiries in Categories of Statistics**

Statistical Inquiries in Categories of Statistics are statistical investigation and research.

Types of statistical inquiries in Categories of Statistics are:

- Primary or secondary
- Census or a sample
- Open or confidential – in terms of results
- Direct or indirect – in terms of measuring
- Regular – regular period or once a while
- Initial or repetitive
- Official, semi-official or non-official- by government, bodies supported by government or private bodies`

**Organization Data in Categories of Statistics**

Organization Data in Categories of Statistics is the classification and tabulation of data

**Classification of Data in Categories of Statistics**

Classification of Data in Categories of Statistics is the arrangement of related data/facts into different groups/classes with respect to some characteristics (basis of classification)

Objectives of Classification in Categories of Statistics

The objectives of classification in Categories of Statistics include:

- eliminate unnecessary details
- highlight points of similarity and dissimilarity comparison and inferences
- highlight important aspects of the data
- utilize data for further statistical analysis

Types of Classification in Categories of Statistics

The types of classification in Categories of Statistics are;

- Geographical or spatial classification – with respect to place/area e.g. district
- Chronological or temporal classification with respect to time
- Qualitative- on the basis of some attribute or quality such as sex, literacy etc.
- Quantitative – on the basis of measurable characteristics such as age

**Frequency Distribution in Categories of Statistics**

The frequency distribution in Categories of Statistics includes;

- Grouping of statistical data according to size or magnitude. Its features include:
- Number of classes – minimum of 6-8 and max of 20 -25
- Class intervals -span of a class-upper limit – lower limit
- Class limits and boundaries
- Class mid-point- (UCL +LCL)/2
- Class frequency – No. of values/items in each class
- Cumulative frequency

There are two methods of classifying the data according to class-intervals. They are exclusive method which is upper limit of one class is the lower limit of the next and is not included in that former class and inclusive method which is upper limit of one class is included in that class itself and not repeated in the next class

**Class Interval in Categories of Statistics**

Class interval = (maximum value – minimum value) / desired number of people.

E.g. maximum number = Kshs 100, Minimum number = Kshs 10, desired number of classes is 15.

The class interval will be (100 – 10)/ 15 = 6

**Class Limits in Categories of Statistics**

These are the values within which variables of a class lie. They include:

- Upper class limit
- Lower class limit
- Upper class boundary.
- Lower class boundary.

**Frequency **

This is the number of values occurring in each class.

Example:

Make a frequency distribution table fro the data below with class intervals of five:

2, 4, 3, 1, 5, 7, 9, 21, 13, 15, 18, 17, 14, 10, 12, 16, 7, 6, 19, 22, 11, 23, 22, 24, 2, 5, 3, 4, 3, 2.

Class |
Frequency |

1 – 5 | 11 |

6 – 10 | 5 |

11 – 15 | 5 |

16 – 20 | 4 |

21 – 25 | 5 |

f 30 |

**Making Frequency Distribution in Categories of Statistics**

**Steps:**

- Determine the range of the data.
- Range = highest value – lowest value.

- Determine the appropriate number of classes if not given. Too few classes may lead to omission of vital information while too many classes may present too much information that might be unnecessary.
- Determine the size of the classes (class width / class interval).
- In general;
- Class width = range / number of classes
- = (highest value – lowest value) / number of classes

**Frequency Distribution for Single Valued Classes in Categories of Statistics**

This is frequency distribution for data that are arranged into single valued classes. These are as many classes as there are different data values.** **

**Graphs of Frequency distribution in Categories of Statistics**

- Ogive curve.
- Histogram
- Frequency polygon
- Frequency curve

**Ogive curve in Categories of Statistics**

**Steps:**

- Compute the cumulative frequency of the distribution.
- Prepare a graph with the cumulative frequency on the vertical axis.
- Plot a starting point at zero on the vertical scale of the lower class limit of the first class.
- Plot the cumulative frequencies on the graph against the upper class limits of their respective classes.
- Then join all this points by the help of a curve.
- An ogive curve is used to find out the values of median, quartiles, deciles and percentiles graphically.

**Example:**

**Class Frequency Cumulative frequency**

0-10 5 5

10-20 10 15

20-30 15 30

30-40 8 38

40-50 7 45

Cumulative frequencies (cf) of each group are marked against upper class limit of the respective group.

Business Training in Kenya has more on this topic.

### Conclusion on Categories of Statistics

In conclusion *Categories of Statistics *is a scientific study of the principles and methods applied in collection, organization, presentation, analysis and interpretation of numerical data