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E LEMENTARY Chapter 1  Introduction to Bio-Statistics Chapter 1  Introduction to Statistics
 
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
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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
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Definitions ,[object Object],[object Object]
Definitions ,[object Object],[object Object],[object Object],[object Object]
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[object Object],[object Object],population parameter Definitions
Definitions ,[object Object],[object Object]
Definitions ,[object Object],[object Object],sample statistic
Definitions ,[object Object],[object Object]
Definitions ,[object Object],[object Object],[object Object],[object Object]
Definitions ,[object Object],[object Object]
Definitions ,[object Object],[object Object],[object Object],[object Object]
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[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Levels of  Measurement qualitative quantitative
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
Common Statistical Notations & Symbols  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
DATA  REDUCE,SUMMERISE ADJUSTING  FOR VARIATION  INFORMATION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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Health Management Information System (HMIS) ,[object Object],[object Object],BEFORE Summary of data was calculated by hand and therefore prone to errors Long delay to produce reports
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

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Bio stat

  • 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
  • 4.
  • 5.
  • 6.
  • 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
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 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
  • 30.
  • 31.
  • 32.
  • 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

Notes de l'éditeur

  1. page 4 of text
  2. 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.
  3. page 5 of text
  4. page 6 of text
  5. 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.
  6. page 7 of text
  7. 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.
  8. Students usually have some difficulty understanding the difference between interval and ratio data. Fortunately, interval data occurs in very few instances.
  9. review of four levels of measurement