The document discusses different types of data classification. It defines classification as arranging things into groups based on similarities. The key types of classification discussed are:
- Geographical classification, which groups data by location.
- Chronological classification, which groups data by time period.
- Qualitative classification, which groups non-numerical data by attributes.
- Quantitative classification, which groups measurable numerical data into ranges.
- Alphabetical classification, which lists data in alphabetical order.
For a detailed explanation Watch the Youtube video:
https://youtu.be/YK0GPKuYVfU
Classification of Data, methods- geographical classification, Chronological Classification, Qualitative and Quantitative classification, discrete and continuous variable, grouped frequency distribution, inclusive , exclusive series, cumulative frequency distribution
For a detailed explanation Watch the Youtube video:
https://youtu.be/YK0GPKuYVfU
Classification of Data, methods- geographical classification, Chronological Classification, Qualitative and Quantitative classification, discrete and continuous variable, grouped frequency distribution, inclusive , exclusive series, cumulative frequency distribution
This presentation gives you a brief idea;
-definition of frequency distribution
- types of frequency distribution
-types of charts used in the distribution
-a problem on creating types of distribution
-advantages and limitations of the distribution
Topic: Types of Data
Student Name: Duwa
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
This presentation includes an introduction to statistics, introduction to sampling methods, collection of data, classification and tabulation, frequency distribution, graphs and measures of central tendency.
UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
Calculates the middle value of the data
Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
MEASURE OF DISPERSION
Measures of central tendency do not explain distribution of variables
4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
Responses of two questions are combined
Spearman’s rank order correlation coefficient.
Used in case of ordinal data
This presentation gives you a brief idea;
-definition of frequency distribution
- types of frequency distribution
-types of charts used in the distribution
-a problem on creating types of distribution
-advantages and limitations of the distribution
Topic: Types of Data
Student Name: Duwa
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
This presentation includes an introduction to statistics, introduction to sampling methods, collection of data, classification and tabulation, frequency distribution, graphs and measures of central tendency.
UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
Calculates the middle value of the data
Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
MEASURE OF DISPERSION
Measures of central tendency do not explain distribution of variables
4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
Responses of two questions are combined
Spearman’s rank order correlation coefficient.
Used in case of ordinal data
#2 Classification and tabulation of dataKawita Bhatt
The placement of data in different homogenous groups formed on the basis of some characteristics or criteria is called classification. The Table is a systematic arrangement of data in rows and/or column. Here, few basic concepts of classification and tabulation such as class interval, variable, frequency, frequency distribution and cumulative frequency distribution have been explained in a nutshell. This presentation also deals with the basic guidelines for preparing a table. Any suggestion and query are welcomed please drop them in the comments.
2. “Classification is the process of arranging
things(either normally or notionally) in
groups or classes according to their
resemblances and affinities and give
expressions of the unity attributes that may
subsist amongst a diversity individuals”. -
Conner
3. Bulk of the data
Simplifies of the data
Facilitates comparison of characteristics
Renders the data for statistical analysis
7. When the data classified according to
geographical location or region (like states,
cities, regions, zones , areas etc) It is called
a geographical classification. For example,
the production of food grains in INDIA may be
presented state- wise in following manner.
8. S.NO. Name of states Total food grains
(thousands tones)
1 ANDHRA
PARDESH
1093.90
2 BIHAR 12899.89
3 KARNATAKA 1834.78
4 PUNJAB 21788.20
5 UTTER
PRADESH
41828.30
9. When data are observed over a period of time
the type of classification is known as
chronological classification ( on the basis of its
time of occurrence ). Various the serious such as
National income figures , annual output of
wheat monthly expenditure of a house hold ,
daily consumptions of milk, etc. Are some
examples of chronological classification . For
examples we may present the figures of
population (or production , sales,etc.) as
follows……
10. S.No. year Population in
crores
1 1941 31.87
2 1951 36.11
3 1961 43.91
4 1971 54.82
5 1981 68.33
11. We may first divide the population in to
males and females on the basis of the
attribute ‘sex’, each of this class may be
further subdivide into ‘literate’ and
‘illiterate’ on the basis of attribute
‘literacy’ further classification can be made
on the basis of same other attribute ,say ,
employment.
12. Quantitative classification is refers to the
classification of data according to some
characteristics that can be measured, such
as height, weight ,income, sales profit,
production,etc. For example, the student of
a college may be classified according to
weight as follows:
13. Weight (kg) No. of
Students
40-50 60
50-60 50
60-70 28
70-80 20
80-90 12
Total 170
14. When the data are arranged according to
alphabetical order, it is called alphabetical
classification. For example state-wise density
of population in India is depicted in an
alphabetical order below;
15. Names of States Density of Population
(Per Sq. Km)
Andhra Pradesh 157
Assam 150
Bihar 324
Gujarat 136
Haryana 225
Himachal Pradesh 62
Kerala 548