Statistics 103 : Definition ,Limitations, Functions,Applications and Various Graphs

Hello all,
This presentation includes the Definition, Functions, Limitations, and Applications of Statistics.
Additionally, It consists of Classification of Data, its types and methods of collection of data.
Graph Covered:- Bar Graph, Histogram, Frequency curve, Ogives, and Pie Chart.

1. Statistics 103 .

2. Statistics is a form of mathematical analysis that uses quantified models, representations and synopses for a given set of experimental data or real-life studies. Statistics studies methodologies to gather, review, analyse and draw conclusions from data.  What is statistics a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data . page 2

3. Functions of Statistics Understanding off nature It helps in providing a better understanding and exact description of a phenomenon of nature. Planning It helps in the proper and efficient planning of a statistical inquiry in any field of study. . Presenting Data It helps in presenting complex data in a suitable tabular, diagrammatic and graphic form for easy and clear comprehension of the data. Collecting Data Statistics helps in collecting appropriate quantitative data. D rawing I nferences It helps in drawing valid inferences, along with a measure of their reliability about the population parameters from the sample data.   page 3

4. Limitations of Statistics Accuracy  If sufficient care is not exercised in collecting, analysing and interpreting the data, statistical results might be misleading. Need Expert Only a person who has an expert knowledge of statistics can handle statistical data efficiently. A ggregates of Facts Statistics are aggregates of facts, so a single observation is not a statistic. Statistics deal with groups and aggregates only. Limitation in Data Statistics cannot be applied to heterogeneous data. page 4

5. Methods to collect data in statistics Information Age, data is no longer scarce – it’s overpowering.  Simple Surveys In- person Interviews Experiments page 5 Focus Groups Observational data collections methods

6. Applications of Statistics The scope of statistics is confined to two main aspects – the classification and application of statistics.  Actuarial science is the discipline that applies mathematical and statistical methods to assess risk in the insurance and finance industries . Environmental statistics is the application of statistical methods to environmental science. Weather, climate, air and water quality are included, as are studies of plant and animal populations. Machine learning is the subfield of computer science that formulates algorithms in order to make predictions from data . Business statistics is a specialty area of statistics which are applied in the business setting. It can be used for quality assurance, financial analysis, production and operations, and many other business areas. page 6

7. Types of Statistical Data Numerical Data These data have meaning as a measurement, such as a person’s height, weight, IQ, or blood pressure; or they’re a count Numerical data can be further broken into two types: discrete and continuous. Discrete data  represent items that can be counted; they take on possible values that can be listed out. The list of possible values may be fixed (also called  finite ); or it may go from 0, 1, 2, on to infinity (making it  countably infinite ).  Continuous data represent measurements; their possible values cannot be counted and can only be described using intervals on the real number line. 2. Categorical Data Categorical data represent characteristics such as a person’s gender, marital status, hometown, or the types of movies they like. Categorical data can take on numerical values page 7 3. Ordinal D ata mixes numerical and categorical data. The data fall into categories, but the numbers placed on the categories have meaning.

8. Classification of Data Geographical classification When data are classified on the basis of location or areas, it is called geographical classification   Example:  Classification of production of food grains in different states   in India. Quantitative classification Quantitative classification refers to the classification of data according to some characteristics, which can be measured such as height, weight, income, profits etc. Qualitative classification In Qualitative classification, data are classified on the basis of some attributes or quality such as sex, colour of hair, literacy and religion. In this type of classification, the attribute under study cannot be measured. It can only be found out whether it is present or absent in the units of study. page 8

9. Bar Graph page 9 X f 10-15 6 15-20 11 20-05 9 25-30 7 30-35 5 35-40 2

10. X Lower Limit f 10-15 10 6 15-20 15 11 20-05 20 9 25-30 25 7 30-35 30 5 35-40 35 2 page 10 Histogram and Frequency Curve

11. X f Lower Limit Upper Limit CF (less than ogives) 10-15 6 10 15 6 15-20 11 15 20 17 20-05 9 20 25 26 25-30 7 25 30 33 30-35 5 30 35 38 35-40 2 40 40 page 11 Ogives

12. Pie chart   2018-19   2017-18   2019-20 page 12 Years % 2017-18 20 2018-19 40 2029-20 20

13. Thank You Aayush Namdev [email protected]