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Statistics

Statistics. Meena Ganapathy. Meaning. Statistics Latin-status Italian statistica Germany Statistik French statistique Statistic – Singular- One value associated e.g., wt of one person Plural e.g., wt of more values

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Statistics

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  1. Statistics Meena Ganapathy

  2. Meaning • Statistics • Latin-status • Italian statistica • Germany Statistik • French statistique • Statistic – Singular- One value associated e.g., wt of one person • Plural e.g., wt of more values • Statistics as singular branch of science- It is the combination of logic & Mathematics.

  3. Diff. branches of statistics • 1) Medical Statistics • 2) Health statistics • 3) Vital statistics • 4) Biostatistics

  4. Statistics • It is the branch of Science which deals with technique of collection, compilation, presentation, analysis of data & logical interpretation of the result.

  5. Use of statistics • 1.To collect the data in best possible way. • 2.To describe the characteristics of a group or a situation. • 3.To analyze data & to draw conclusion from such analysis.

  6. Definition • Variable :- A characteristic that take different values in different person places or things. • E.g. Ht, Wt, B.P., Age;’ • It is denoted by capital x = x • E.g., x: ht • X1, x2, x3, x4…….xn • N= total numbers of observation

  7. Attribute • A qualitative characteristic like age, sex, nationality is called as attribute

  8. Constant • The characteristic which does not change its value or nature is considered as constant • E.g. blood group, sex

  9. Observation • An event or its measurement such as BP., Is as event & 120/80 mm of Hg. Is as measurement

  10. Observation unit • The source that gives observation such as object person etc.

  11. Data • A set of values recorded on one or more observational unit is called as data. It gives numerical observation about observational unit. • e.g., HT, WT, Age. • = equal to • < Less than • > greater than • =< less that & equal to • => greater than & equal to • ≠ not equal to • ∑ Summation

  12. Short forms • A.M.- arithmetic mean • H.M.- harmonic mean • G.M.- Geometric mean • C.V.- Coefficient of variation • S.E.- Standard error • S.D.- Standard deviation • D.F.- Degree of freedom • C.I.- Confidence interval

  13. E :- Expected value of cell of contingency table • O :- Observed value of cell of contingency table. • N :- Population size • N :- Sample size • L :- Level of significance (I.O.S) • Ho :- Null hypothesis • H1 Alternative hypothesis

  14. Types of data • Qualitative and quantitative • Discrete and continuous • Primary and Secondary • Grouped and ungrouped

  15. Qualitative & quantitative data • Qualitative data :-It is also called as enumeration data. It represents particular quality or attribute there is no notion of measurement. It can be classified by counting individuals having the same characteristics. • E.g. Sex, religion, blood group

  16. Quantitative data • It is also called as measurement data. This can be measures by counting the characteristics in the variable. • E.g. Ht, Wt, BP, HB

  17. Discrete & Continuous • Discrete :- Here we always get a whole number. • E.g. no of people dying in road accidents no. of vials of polio vaccine. • Continuous :- In this data there is possibility of getting fraction like 1.2, 2.1,3.81. i.e. it takes all possible values in a certain range. • E.g., Ht, WT, temp

  18. Primary and Secondary • Primary :- The data obtained directly from a individual gives precise information. i.e., when the data is collected originally by the investigator for the first time is called primary data. • E.g. to find no. of alcoholic person in Karvenagar area. By the investigator. • Secondary :- When the data collected by somebody or other person is used the data is called secondary data. • E.g. Census hospital records

  19. Ungrouped and Grouped • Ungrouped :- When the data is presented in raw way , it is called as ungrouped data • E.g. Marks of 5 students • 20,30,25,20,30 • Grouped :- When the ungrouped data is arranged according to groups, then it is called as grouped data. • E.g. Marks Students • 20 2 • 30 2 • 25 1

  20. Methods of data Collection • Observation Visual • Instrument • Instrument Properties • Reliability Validity • Interviews & self administered questionnaires • Use of documentary sources (secondary data)

  21. Classification of data • Definition :- The process of arranging data in to groups or classes according to similar characteristics is called as classification & the group so formed are called as class limits 1 class interval.

  22. Objectives of classification of data • 1.It condense the data • 2.It omits unnecessary information. • 3.It reveals the important features of the data. • 4.It facilities comparison with other data • 5.It enables further analysis like competition of average, dispersion (Variables ) data.

  23. Frequency • A) Frequency • Definition :- No. of times variable value is repeated is called as frequency. • B) Cumulative class frequency • Definition :-Cumulative frequency is formed by adding frequency of each class to the total frequency at the previous class. It indicates the no. of observations < upper limit of the class limit.

  24. Representatives Symbol • Sample Population • 1. Mean X bar M • 2. SD $ o 2 • 3. Variance $2 o2 • 4. Proportion p P • 5. Complement of • proportion 2 Q

  25. Data presentation Meena Ganapathy

  26. Methods of presentation of data • Tabulation. • Charts and diagrams.

  27. Methods of presentation of data

  28. Important points in making a table • Table No. :- If many tables are present • Title :- Should be small • Head note :- Whatever is not covered in title can be written in head note. • E.g. expressing units • Caption :- column heading • According to characteristics • Stub :- raw • Subheading • Body :- content • Foot note:- Short forms or • Source note :- resource it is important because it shows reliability of table.

  29. Rules and guidelines fortabular presentation • 1. Table must be numbered • 2. Brief & self explanatory title must be given to each table. • 3.The headings of columns & rows must be clear, sufficient, concise & fully defined.

  30. 4. The data must be presented according to size or importance chronologically alphabetically or geographically. • 5. Table should not be large. • 6. Foot note should be given whenever necessary providing additional information sources or explanatory notes.

  31. Types of table • 1.One way table/simple table • 2.Two way table • 3.Complex table

  32. 1.One way table/ Simple table • When there is only one characteristics is described in a table then it is called as simple table

  33. Example of one way table

  34. Two way table • In this table data is classified according to two characteristics it given information about two interrelated characteristics.

  35. Frequency distribution table qualitative data distribution of types of anemia • According to sex

  36. Complex table • Information collected regarding 3 or 4 characteristics & tabulated according to these characteristics such a type of table is called as complex table.

  37. Example of Complex Table

  38. Advantages of a Graphs & Diagrams • 1. Information is presented in condensed form • 2. Facts are presented in more effective & impressive manner as compared to tables • Easy to understand for a layman. • Create effect which last for longer time • Facilitate the comparison. • Help in revealing patterns.

  39. Disadvantages • Approximate results instead of accuracy • Gives only a general idea • Not sufficient for statistical analysis

  40. Types of diagrams for qualitative data • Bar: Simple, Multiple or complex, Component & Proportional • Pie or Sector • Pictograms • Shaded Map / Contour / Spot Maps

  41. Bar Diagrams • It is used to compare variables possessed by one or more groups.

  42. Simple Bar Diagram • Here only one variable is presented • Bars are at uniform distance from one another • It can be drawn vertically or horizontally • Each should have title & source note

  43. Pie or Sector diagrams • When the data is presented as sum of different components for one qualitative characteristics we use pie diagrams.

  44. Pictograms • This diagrams are useful for lay people. E.g., Village map indicating temple, trees etc…

  45. Spot Maps • In this diagram a map of an area with location of each case of an illness, death etc… are identified with spots or dot or any other symbol.

  46. Types of diagrams for quantitative data • Histograms • Frequency polygon • Frequency curve • Cumulative frequency curve • Line graph • Scatter diagram • Population Pyramid • Growth chart

  47. Histograms • It is the graphical representation of frequency distribution. It is a series of adjacent rectangles erected on bars • Areas of these bars denote the frequency of respective class interval. • X axis base of bars shows class width of class interval • Y axis frequency / No of observations

  48. Frequency Polygon • It is representation of categories of continuous & ordered data similar to histogram. It can be drawn in two ways: Using histograms, with out using histograms. • Uses: it is used when sets of data are illustrated on the same diagram such as temperature, & pulse, birth & death rate etc…

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