Lecture-2{This tell us about the statics basic info}_JIH.pptx

This is a static power point presentation and this teaches us the basics of statics and guides us the way of learning statistics

1. Statistics Lecture 2 Javed Hasan Lecturer Department of Pharmaceutical Sciences

2. Historical Perspectives of Statistics The GREEKS had records of CENSUS of adult males in war times and of the general population every time when the food supply was not adequate. The ROMANS registered adult males and their properties for military and administrative purposes . SERVINUS TULLIUS , THE SIXTH KING OF ROME, was the first one to institute population gathering data . Each male in the Roman Empire returned to the city of his birth to be counted and taxed. WILLIAM THE CONQUEROR , KING OF ENGLAND compiled information on population and resources . THE DOOMSDAY BOOK (it is a Great survey ordered by WILLIAM THE CONQUEROR) is the first landmark in British statistics . GOTTFRIED ACHENWALL first introduced the word “ STATISTIK ” IN A CONTEXT OF STATISTICAL SURVEY. GIROLAMO CARDANO wrote “ LIBER DE LUDO ALEAE ” in which appeared the first known study of applying statistics in the principles of probability .

3. Period / Time / Year Remarks Around in 3800 B.C RECORDS OF POPULATION CENSUS IN BABYLONIA AND CHINA In 1491 B.C. BIBLICAL POPULATION CENSUS WAS UNDERTAKEN BY MOSES In 1017 B.C. BIBLICAL POPULATION CENSUS WAS UNDERTAKEN BY DAVID IN 13TH CENTURY TAX LISTS OF PARIS INCLUDED REGISTRATION OF THOSE WHO WERE SUBJECTED TO TAX.

4. Person Milestones / contribution Al- Kindi Wrote a book, "Manuscript on Deciphering Cryptographic Messages" that is believed to be the earliest writing on statistics. In this book, Al- Kindi gave a detailed description on how to decipher encrypted messages using statistics and frequency analysis. This text arguably gave rise to the birth of both statistics and cryptanalysis. John Graunt Pioneer of demography Produced the first  life table Blaise Pascal Pioneered probability theory Thomas Bayes Developed Bayesian probability Willian Playfair Pioneered statistical graphics

5. Person Milestones / contribution Florence Nightingale Applied statistical analysis to health problems, contributing to the establishment of epidemiology and public health practice. Developed statistical graphics Francis Galton Invented standard deviation, correlation and regression Karl Pearson Developed Pearson Chi-squared test, Pearson correlation William Sealy Gosset (Student) Discovered Student t distribution and Student’s t-test Ronald Fisher Developed analysis of variance (ANOVA) and theoretical concepts Developed practical methods for designing experiments

6. VARIABLES A variable is any quantity or characteristics whose value varies for different members of a population or sample. A characteristic that can be measured, categorized, quantified, or qualified. Height, Occupation, Age, Weight, Gender, Marital status, Annual Income are few examples of variables in our daily life.

7. Classification of VARIABLES Variable Qualitative Quantitative Discrete Continuous

8. Collection of data OR how we collect data Interview or enumerate (to mention separately or count or listing) Questionnaire Examine and/or experiment Observation Records.

9. Biostatistics : Statistics applied in biological sciences or biological experiments (for example: in medicine or pharmacy etc.) Data : Individual facts or items of information . A data is the raw material of statistics Types: A. Qualitative: 1. Nominal data 2. Ranked data B. Quantitative/Numerical: 1. Discrete data 2. Continuous data

10. A. Qualitative data 1. Nominal data Nominal data are data that one can name . They are not measured but simply counted . They often consist of unordered ‘ either-or ’ type observations, for example: Dead or Alive; Male or Female; Cured or Not Cured; pregnant or Not pregnant etc. 2. Ranked data If there are more than two categories of classification it may be possible to order them in some way. For example, after treatment a patient may be either improved , the same or worse . In some situations we have a group of observations that are first arranged from highest to lowest according to magnitude and then assigned numbers correspond to each observation’s place in the sequence. This type of data is known as ranked data .

11. B. Quantitative/numerical data 1. Discrete data Such data consist of counts which are only isolated points . Example may be the number of deaths in a hospital per year. 2. Continuous data Such data are measurements that can, in theory at least, take any value within a given range . Example : Diastolic blood pressure , which is continuous, is converted into hypertension.

12. Methods of data presentation in statistics or biostatistics Every study or experiment yields a set of data . Its size can range from a few measurements to many thousands of observations. Methods are: Tabulation of data Diagrammatic presentation The principal object of data presentation : whether tabular or graphical, is to convey the essential features of the study to any reader of the final publication.

13. 1. Tabulation of data A statistical table is a systematic organization of data in columns and rows in accordance with some characteristics. Tabulation is the process of presenting data in tables. Objectives of tabulation: To clarify the object of investigation To clarify complex data To clarify comparison .

14. Rules for Tabulation of data The table should be simple and compact . All title, subtitle, caption etc. should be arranged in a systemic manner . The unit of measurement should be clearly defined in the table. A table should be complete and self-explanatory . A table should be attractive to draw attention of readers. Accurate statistical analysis should be done. Abbreviation should be avoided. If units of measurements are involved, such as mg/100 ml for the serum cholesterol levels should be specified .

15. Graphical presentation of data A diagram is a visual form for presentation of data. Complicated data through a diagram or graph can easily be understood . It is convincing to the eye and mind. Importance of diagrams: They are attractive and impressive . They save time and labor to understand. They make data simple . They make comparison easy . They provide more information than table .

16. Types of diagram & graphs Graphs : Line diagram Scatter diagram Histogram Diagrams : Bar diagram (simple & multiple bars) Pie diagram . Frequency curve

17. Frequency distribution Frequency: Is the number of times a particular event occurs in data. Frequency distribution: Show either actual number or the percentage of observations in each category Can be used for both categorical and numeric variables Can be grouped (for example age groups) or ungrouped (exact age) variables Cumulative frequency distribution: Is the total frequency of all values up to the upper limit of a variable . (simply adding preceding frequency intervals and the frequency of the current interval) Examples: Chapter 2from book reference.

18. Line Graph/Diagram A   line graph  is a type of graph which displays information as a series of data points called 'markers' connected by straight line segments. A line diagram is used to show the trend of one variable over the other. It shows a trend over a period of time rising, falling or showing fluctuations.

19. Line Graph /Diagram EXAMPLE: South Africa held their nerve to win by three wickets on a pitch that got a lot slower as the game went on. Try to interpret the line graph.

20. Line Graph

21. HISTOGRAM Continuous or ordered categorical data on horizontal axis as class intervals and the frequencies on the vertical axis. It is a useful method for the presentation of frequency distribution of continuous data. Properly defined, a histogram is a bar graph in which each bar corresponds to a category created by grouping the values of the variable into intervals or classes , and where the height of each bar is proportional to the absolute (or relative) frequency of the corresponding class. Most important tool for exploring the shape of data distributions.

22. HISTOGRAM EXAMPLE: The following data shows marks obtained by 50 students in Statistics Exam.

23. Bar Graph A   bar graph  is a chart with rectangular bars with lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. Usually Nominal data or Ordered Categorical. If the data are mean, rate or ratio from a cross-sectional study then bar may be the only appropriate diagram. All bars should have the same width so as not to mislead the reader. Only the length should differ. The zero point or origin should be indicated.

24. Bar Graph EXAMPLE: The Table on the left gives the birth rate per thousand of different countries over a certain period of time.

25. MULTIPLE BAR GRAPH A multiple Bar Graph shows two or more characteristics corresponding to the values of a common variable in the form of grouped bars, whose length are proportional to the values of the characteristics. Each of the variable is shaded or colored differently to aid identification. This is a good device for the comparison of two or more kinds of information.

26. MULTIPLE BAR GRAPH EXAMPLE: The table beside shows Marks obtained by Boys and Girls in different disciplines in Federal Board Exam

27. PIE CHART A  pie chart  is a circular statistical diagram, which is divided into sectors to illustrate numerical proportion of a category. In a pie chart, the arc length of each sector (and consequently its central angle and area), is proportional to the quantity it represents. A Pie chart is popular way of presenting categorical data (nominal or ordinal data). The circle represents 100%, which means all of the result. The angle,  

28. PIE CHART A circle graph/pie chart is constructed by converting the share of each component into a percentage of 360 degrees. The circle graph/pie chart quickly tells you that half of students like rap best (50%), and the remaining students prefer alternative (25%), rock and roll (13%), country (10%) and classical (2%). If 50% of the students liked rap, then 50% of the whole circle graph/pie chart (360 degrees) would equal 180 degrees.

29. Problem Solving Find out the Winning Percentage of each country. Find out the angle for each country Construct a Pie chart

30. Solution