STATISTICS.pptx
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This document provides an overview of statistics and its uses. It discusses how statistics is the study of collecting, analyzing, and presenting data. It then describes some common uses of statistics like simplifying data, facilitating comparisons, and testing hypotheses. The document also lists some key terms in statistics and different types of data. It discusses different types of statistical analyses like descriptive statistics, inferential statistics, and frequencies distributions. Finally, it provides examples of common ways to visually represent data through tables, bar graphs, pie charts, histograms, and other diagrams.
STATISTICS.pptx
- 1. STATISTICS (Introductory Statistics) Dr. Senthilvel Vasudevan, M.Sc., M.Phil, DST, PGDBS, Ph.D., Fellow of Royal Statistical Society (London), MISMS, IAPSM, IPHA, NIN, IMS, IBMS, ISI Assistant Professor of Statistics (Biostatistics), Department of Community Medicine, Sri Venkateshwaraa Medical College Hospital & Research Centre, Ariyur, Pondicherry – 605 110. Email ID: senthilvel99@gmail.com
- 2. Definition of Statistics & Its uses Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. Uses of Statistics: oStatistics presents facts in a definite form oIt facilitates comparisons oIt simplifies the masses of figures oIt helps in formulating and testing hypothesis oIt helps in prediction
- 3. Statistics and its tools used in various sectors Statistics is a tool, and it is used in any fields then it will take its own in the field. • Biostatistics – Medicine • Educational Statistics – Education • Agricultural Statistics – Agriculture • Econometrics – Economics • Mathematical Statistics – Mathematics • Public Health Statistics – Public Health and so on………
- 4. Applications of Statistics Applications of Statistics in health statistics as follows: o Defining normal and not normal in context of various aspects related to health and illness. o Establishing the accuracy of diagnostic procedures o Planning of experiments and analysis of results. o Observations on the natural history of a disease, namely its signs, symptoms, course, variations and etc. o Assessment of treatment protocol and different interventions used for care and treatment o Collection, analysis, and dissemination of various population health statistics.
- 5. Terms Related to Statistics • Data: A set of values recorded on one or more observations units (or) the factual information collected during research studies. • Quantitative Data: Discrete Data (data in whole number – blood sugar, no of family members) and Continuous Data (Data which can be measured in fractional values. Ex: Height, Weight, body Temp. • Qualitative Data: The variables that yield observations on which individuals can be categorized according to certain characteristics. Ex: gender, occupation, marital status, and educational status/level. • Parameters: Characteristics of a population (Ex: Average age of all nurses in Pondicherry Union Territory) • Parametric Tests: Statistical tests that involve assumption about the distribution of the variables and estimation of a parameter. • Non-Parametric Tests: It doesn’t involve assumptions about distribution.
- 6. Scales of Measurement Quantitative Data (interval – Age Range and ratio – Height, Weight) Data Qualitative Data (Nominal - Types of Commodities and Categorical/ordinal – Income Status )
- 7. Classification of Statistics Statistics is mainly divided into two categories. 1. Descriptive Statistics; 2. Inferential Statistics Descriptive Statistics: It deals with the enumeration, organization, and graphical representation of data. Inferential Statistics: It provides the procedures to draw an inference about the conditions that exist in a large set of observations, that is an entire population from study of a part of that set (sample). This branch of statistics is also known as “Sampling Statistics” (The corresponding statistical tests will be seen afterwards)
- 8. Descriptive Statistics Descriptive Statistics is divided into as following ways: • Measures of data condensation: Frequency distribution and graphical presentation of data. • Measures of Central Tendency • Measures of Dispersion • Measures of relationship (Correlation Co-efficient)
- 9. Frequency Distribution • An appropriate presentation of data involves organization of data in such a manner that meaningful conclusions and inferences can be drawn to answer the research question. • Unsorted and ungrouped records don’t allow us to draw clear conclusion. • Quantitative data are generally condensed, and frequency distribution is presented through tables, charts, graphs, and diagrams.
- 10. Table A table presents data in a concise, systematic manner from masses of statistical data. General Principles of Tabulation: • A table should be precise, understandable, and self explanatory. • Table should have a proper title and it placed at the top of the table. It should be clear, concise and precisely. • Rows and columns to be compared with one another should be brought together. • The content of the table, as a whole as well as the items in each column and row, should be defined clearly and fully.
- 11. Table (Contd…) • The unit of measurement must be clearly stated. • Percentage can be given in the parenthesis or can be worked out to one decimal figure to draw the reader’s attention. • Totals can be placed at the bottom of the columns. • Reference symbols can be directly placed beneath the table for explanatory footnotes.
- 12. Parts of a Table • Table Number – It should be placed at the top of the table. • Title – Top of the table • Head Notes – Below the title
- 13. Parts of a table (Contd…) Captions and Stubs – Captions are the headings designed for vertical columns and stubs are the headings for horizontal rows. Body of the table – Arrangement of the data headings designed for vertical columns and stubs are the headings for horizontal rows. Foot Notes - Characteristics or items of the table are not adequately explained, then footnotes are used to explain those items. Source Note – When we use the secondary data then we have to mention the source from which the data for the table or the table itself is retrieved.
- 14. Types of Tables • Simple Table Gender Marks in Exam f (%) N = 100 Male 54 (54.0) Female 46 (46.0)
- 15. Composite Table
- 16. Type of tables • Frequency distribution table
- 17. Formation of Frequency Distribution Table
- 18. Contingency table Bowel Movements Mode fo ventilation Total Frequency N Chi – Square Value & p - value Spontaneous Ventilation f (%) Mechanical Ventilation f (%) Present 391 (64.0) 32 (29.4) 423 45.87 0.045 (<0.05) Sig. Absent 220 (36.0) 77 (70.6) 297 Total 611 109 720
- 19. Graphical Presentation of Data Main reasons for using the diagrammatic and graphic representation of data are as follows: • Graphical presentation is the most convenient and easy way to present any data or statistical data. • It gives the clear view of entire data. Layman is also understood easily. • It is visually more attractive way than other ways of representing data. • It is easy to understand and to memorize the data. • By this anyone compare the data relating to different periods of time of different origins (or) different regions.
- 20. Types of Diagrams • Bar Diagram – Simple, Multiple, Sub-divided • Pie Chart/Sector diagram • Histogram • Frequency Polygon • Line graphs/diagram • Cumulative Frequency Curve (or) Ogive • Scattered Diagram (or) Dotted Diagram • Pictograms (or) Picture Diagram • Map Diagram (or) Spot Map
- 21. Bar Diagram Bar diagram is a convenient graph that is particularly useful for displaying nominal (or) ordinal data.
- 22. Keep in mind at the time of making bar diagram • The width of the bars should be uniform throughout the diagram • The gap between the bars should be uniform throughout the diagram. • Bars may be vertical (or) horizontal. There are 3 types of bar diagrams: Simple, Multiple and Proportion/Sub-divided bar diagram.
- 25. Pie Diagram/Sector Diagram Pie diagram is another useful pictorial diagram/device for presenting discrete data of qualitative characteristics, such as age-groups, gender, and occupational groups in a population. The total area of the circle represents the entire data under consideration. Researcher must remember that only percentage data must be used to prepare pie diagrams. It gives comparative differences at a glance. Size of each angle is calculated by multiple class percentages with 360° degree (or) calculated by the following formula: Class Frequency ------------------------- X 360° Total Observation
- 26. Pie Diagram
- 27. Histogram Histogram is the most commonly used graphical representation of grouped frequency distribution. Variables characters of the different groups are indicated on the horizondal line (X – axis) and frequencies (number of observation) are indicated on the vertical line (Y – axis). Frequency of each group forms a column (or) rectangle. This diagram is called HISTOGRAM.
- 28. Frequency Polygon Frequency polygon curve obtained by joining the middle top points of the rectangles in a histogram by straight lines. It can be drawn by using following steps: • Draw the histogram with the given data • Join the midpoints of upper horizondal sides of each rectangle with the adjacent one by a straight line. • Close the polygon at both ends of the distribution by extending them to base line. • Hypothetical classes at each end would have to be included with a frequency of zero.
- 29. How to draw a frequency polygons? • Draw a histogram with the given data • Join the midpoints of upper horizontal sides of each rectangle with the adjacent one by a straight line. • Close the polygon at both endsof the distribution by extending them to base line. • Hypothetical classes at each end would have to be included with a frequency of zero.