Bio stat

441 views
391 views

Published on

Published in: Education, Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
441
On SlideShare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
11
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide
  • page 4 of text
  • Emphasize that a population is determined by the researcher, and a sample is a subcollection of that pre-determined group. For example, if I collect the ages from a section of elementary statistics students, that data would be a sample if I am interested in studying ages of all elementary statistics students. However, if I am studying only the ages of the specific section of elementary statistics, the data would be a population.
  • page 5 of text
  • page 6 of text
  • Understanding the difference between discrete versus continuous data will be important in Chapters 4 and 5. When measuring data that is continuous, the result will be only as precise as the measuring device being used to measure.
  • page 7 of text
  • Understanding the differences between the levels of data will help students later in determining what type of statistical tests to use. Nominal and ordinal data should not be used for calculations (even when assigned ‘numbers’ for computerization) as differences and magnitudes of differences are meaningless.
  • Students usually have some difficulty understanding the difference between interval and ratio data. Fortunately, interval data occurs in very few instances.
  • review of four levels of measurement
  • Bio stat

    1. 1. E LEMENTARY Chapter 1 Introduction to Bio-Statistics Chapter 1 Introduction to Statistics
    2. 3. DATA:::DISCRETE OSERVATIONS OF ATTRIBUTES OR EVENTS THAT CARRY LITTLE MEANING WHEN CONSIDER ALONE REDUCE,SUMMERISE ADJUSTING FOR VARIATION  INFORMATION TRANSFORMATION OF INFORMATION THROUGH INTEGRATION AND PROCESSING WITH EXPERIENCE AND PERCEPTION BASED ON SOCIAL AND POLITICAL VALUE  INTELLIGENCE
    3. 4. STATISTICS <ul><li>S - Scientific method for </li></ul><ul><li>T - tabulation </li></ul><ul><li>A - analysis </li></ul><ul><li>T - testing of hypothesis and </li></ul><ul><li>I - inference </li></ul><ul><li>S - study of </li></ul><ul><li>T - time trend </li></ul><ul><li>I - in </li></ul><ul><li>C - community </li></ul><ul><li>S - set up </li></ul>
    4. 5. <ul><li>Statistics </li></ul><ul><li>Two Meanings </li></ul><ul><li>Specific numbers </li></ul><ul><li>Method of analysis </li></ul>Statistics 1-1 Overview <ul><li>Specific number </li></ul><ul><li>numerical measurement determined by a set of data </li></ul><ul><li>Example: Twenty-three percent of people </li></ul>
    5. 6. <ul><li>Specific number </li></ul><ul><li>numerical measurement determined by a set of data </li></ul><ul><li>Example: Twenty-three percent of people </li></ul>Statistics
    6. 7. <ul><li>Method of analysis </li></ul><ul><li>a collection of methods for planning experiments, obtaining data, and then then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data </li></ul>Statistics
    7. 8. Biostatistics is the application of statistical methods to the problems ofbiology, including human biology, medicine and public health Descriptive Biostatistics : It is the study of biostatistical procedures which deal with the collection, representation, calculation and processing. i.e., the summarization of data to make it more informative and comprehensible. It involves graphical and tabular to describe. Includes: Collecting Organizing Summerizing Presenting data Inferential Biostatistics: It constitutes the procedures which serve to make generalizations or drawing conclusions on the basis of the studies of a sample. This is also known as sampling biostatistics.   Includes: Making inferences Hypothesis testing Determining relationship Making predictions
    8. 9. <ul><li>Statistical significance - whether the effect is due to by chance or real </li></ul><ul><li>Experimental ->Intervention ->Outcome </li></ul><ul><li>Study Analysis -> Inference </li></ul><ul><li> Observational -> Exposure ->Observation </li></ul>Introduction <ul><li>Population </li></ul><ul><li>the complete collection of all elements (scores, people, measurements, and so on) to be studied. The collection is complete in the sense that it includes all subjects to be studied. </li></ul>
    9. 10. Definitions <ul><li>Population </li></ul><ul><li>the complete collection of all elements (scores, people, measurements, and so on) to be studied. The collection is complete in the sense that it includes all subjects to be studied. </li></ul>
    10. 11. Definitions <ul><li>Census </li></ul><ul><li>the collection of data from every element in a population </li></ul><ul><li>Sample </li></ul><ul><ul><li>a subcollection of elements drawn from a population </li></ul></ul>
    11. 12. <ul><li>Parameter </li></ul><ul><li>a numerical measurement describing some characteristic of a population </li></ul>Definitions
    12. 13. <ul><li>Parameter </li></ul><ul><li>a numerical measurement describing some characteristic of a population </li></ul>population parameter Definitions
    13. 14. Definitions <ul><li>Statistic </li></ul><ul><li>a numerical measurement describing some characteristic of a sample </li></ul>
    14. 15. Definitions <ul><li>Statistic </li></ul><ul><li>a numerical measurement describing some characteristic of a sample </li></ul>sample statistic
    15. 16. Definitions <ul><li>Quantitative data </li></ul><ul><li>numbers representing counts or measurements </li></ul>
    16. 17. Definitions <ul><li>Quantitative data </li></ul><ul><li>numbers representing counts or measurements </li></ul><ul><li>Qualitative (or categorical or attribute) data </li></ul><ul><li>can be separated into different categories that are distinguished by some nonnumeric characteristics </li></ul>
    17. 18. Definitions <ul><li>Quantitative data </li></ul><ul><li>the incomes of college graduates </li></ul>
    18. 19. Definitions <ul><li>Quantitative data </li></ul><ul><li>the incomes of college graduates </li></ul><ul><li>Qualitative (or categorical or attribute) data </li></ul><ul><li>the genders (male/female) of college graduates </li></ul>
    19. 20. <ul><li>Discrete </li></ul><ul><li>data result when the number of possible values is either a finite number or a ‘countable’ number of possible values </li></ul><ul><li>0, 1, 2, 3, . . . </li></ul>Definitions
    20. 21. <ul><li>Discrete </li></ul><ul><li>data result when the number of possible values is either a finite number or a ‘countable’ number of possible values </li></ul><ul><li>0, 1, 2, 3, . . . </li></ul><ul><li>Continuous </li></ul><ul><li>(numerical) data result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps. </li></ul>Definitions 2 3
    21. 22. <ul><li>Discrete </li></ul><ul><li>The number of eggs that hens lay; for example, 3 eggs a day. </li></ul>Definitions
    22. 23. <ul><li>Discrete </li></ul><ul><li>The number of eggs that hens lay; for example, 3 eggs a day. </li></ul><ul><li>Continuous </li></ul><ul><li>The amounts of milk that cows produce; for example, 2.343115 gallons a day. </li></ul>Definitions
    23. 24. <ul><li>nominal level of measurement </li></ul><ul><li>characterized by data that consist of names, labels, or categories only. The data cannot be arranged in an ordering scheme (such as low to high) </li></ul><ul><li>Example: survey responses yes, no, undecided </li></ul>Definitions
    24. 25. <ul><li>ordinal level of measurement </li></ul><ul><li>involves data that may be arranged in some order, but differences between data values either cannot be determined or are meaningless </li></ul><ul><li>Example: Course grades A, B, C, D, or F </li></ul>Definitions
    25. 26. <ul><li>interval level of measurement </li></ul><ul><li>like the ordinal level, with the additional property that the difference between any two data values is meaningful. The distance is defined. But ratio is not defined. Not include the natural zero starting point (where zero indicates that none of the quantity is present). </li></ul><ul><li>Example: temp in centigrade, intelligence score </li></ul><ul><li>20-25 0 c =30-35 0 c………0 0 c is not mean the absence of heat…20 0 c is not twice as hot as 10 0 c </li></ul>Definitions
    26. 27. <ul><li>ratio level of measurement </li></ul><ul><li>the interval level modified to include the natural zero starting point (where zero indicates that none of the quantity is present). For values at this level, differences and ratios are meaningful. The distance and ratio defined. </li></ul><ul><li>Example: length, weight…100 cm is 50cm more than 50 cm or twice as long as 50 cm </li></ul>Definitions
    27. 28. <ul><li>Nominal - categories only </li></ul><ul><li>Ordinal - categories with some order </li></ul><ul><li>Interval - differences but no absolute zero. </li></ul><ul><li>The distance is defined. But ratio is not defined. </li></ul><ul><li>Ratio – differences…….. absolute zero. </li></ul><ul><li>The distance and ratio are defined. </li></ul>Levels of Measurement qualitative quantitative
    28. 29. Bio - Statistics Descriptive (Summarize & Describe data) Inferential ( draw conclusion) Qualitative Quantitative Estimation Hypothesis Testing Confidence Interval P Value Proportion, Percentage Rate, Ratio Central tendency (mean median, mode) Dispersion Standard deviation standard error mean variance
    29. 30. Common Statistical Notations & Symbols <ul><li>Summery value Sample statistics Popln. parameter </li></ul><ul><li>Mean X µ </li></ul><ul><li>Standard Deviation S σ </li></ul><ul><li>Variance S 2 σ 2 </li></ul><ul><li>Proportion p P </li></ul><ul><li>Component of proportion q Q </li></ul><ul><li>Other Commonly Used Symbols: </li></ul><ul><li>Z : No of SD from Mean or standard normal deviate/ variate </li></ul><ul><li>d.f or f. : degree of freedom </li></ul><ul><li>P value : : Probability value </li></ul>
    30. 31. DATA  REDUCE,SUMMERISE ADJUSTING FOR VARIATION  INFORMATION <ul><li>COMPONENT OF HEALTH INFORMATION </li></ul><ul><li>Demography </li></ul><ul><li>Environmental Health Statistics </li></ul><ul><li>Health Status :Mortality, Morbidity, Disability, Qol </li></ul><ul><li>Health Resources </li></ul><ul><li>Utilization Non Utilization Health Services </li></ul><ul><li>USES OF HEALTH INFORMATION::: </li></ul><ul><li>Measure health status  local national and international comparison </li></ul><ul><li>Quantify health problems </li></ul><ul><li>Planning and effective management of health care need </li></ul><ul><li>Assessing health service accomplishing their objectives or not </li></ul>
    31. 32. <ul><li>::::SOURCES OF HEALTH INFORMATION::::: </li></ul><ul><li>Census </li></ul><ul><li>Registration Of Vital Events:: Birth 14 Days, Death 7 Days. </li></ul><ul><li>Srs </li></ul><ul><li>Notification Of Disease </li></ul><ul><li>Hospital Records </li></ul><ul><li>Disease Registers </li></ul><ul><li>Epidemiological Surveillance </li></ul><ul><li>Environmental Health Information </li></ul><ul><li>Health Manpower Statistics </li></ul><ul><li>Population Surveys </li></ul>
    32. 33. Health Management Information System (HMIS) <ul><li>A computerised information system (HMIS) has been installed in nearly all districts in India . </li></ul><ul><li>Data from SC, PHC, CHC and Hospitals is available for reporting, supervision, planning and analysis </li></ul>BEFORE Summary of data was calculated by hand and therefore prone to errors Long delay to produce reports
    33. 34. Computerized HMIS Data Collection at Health Facilities Form 6, 7 and 8) District Computer Unit Block Computer Unit Decision Support System State Directorate Health Managers / Program Officers Through Floppy Through FTP, using phone lines

    ×