FBS 719 and FBS 819 BIOSTATISTICS [Autosaved].pptx

This is an intro to biostat

1. FBS 719: FUNDAMENTALS OF BIOSTATISTICS & FBS 805: ADVANCED BIOSTATISTICS

2. INTRODUCTION AND BASICS OF BIOSTATISTICS

3. INTRODUCTION Definition of Biostatistics Branches of Biostatistics Importance / Applications of Biostatistics

4. Definition of Biostatistics Biostatistics is the application of statistical techniques to scientific research in health-related fields, including medicine, biology, and public health. It involves the collection, analysis, and interpretation of biological data, especially data relating to human health. Biostatistics is a branch of statistics that deals with data relating to living organisms, making sense of all the data to draw meaningful conclusions. Example, In public health, professionals use statistical techniques to monitor the prevalence of health disorders in a population, which falls under the field of biostatistics.

5. Branches of Biostatistics

6. Definition of Data Data refers to information that is collected, stored, and processed. It can include a wide range of information, including personal data. OR Data is information, facts, or statistics that you can analyze and interpret. You can use data across various fields and industries to guide decision-making, optimize processes, and uncover valuable insights.

7. C ategories/Types of Data

8. Qualitative or Categorical Data Qualitative data, also known as the categorical data, describes the data that fits into the categories. Qualitative data are not numerical. The categorical information involves categorical variables that describe the features such as a person’s gender, home town etc. Categorical measures are defined in terms of natural language specifications, but not in terms of numbers. Sometimes categorical data can hold numerical values (quantitative value), but those values do not have a mathematical sense. Examples of the categorical data are birthdate, favourite sport, school postcode. Here, the birthdate and school postcode hold the quantitative value, but it does not give numerical meaning.

9. Nominal Data Nominal data is one of the types of qualitative information which helps to label the variables without providing the numerical value. Nominal data is also called the nominal scale. It cannot be ordered and measured. But sometimes, the data can be qualitative and quantitative. Examples of nominal data are letters, symbols, words, gender etc. The nominal data are examined using the grouping method. In this method, the data are grouped into categories, and then the frequency or the percentage of the data can be calculated. These data are visually represented using the pie charts.

10. Ordinal Data Ordinal data/variable is a type of data that follows a natural order. The significant feature of the nominal data is that the difference between the data values is not determined. This variable is mostly found in surveys, finance, economics, questionnaires, and so on. The ordinal data is commonly represented using a bar chart. These data are investigated and interpreted through many visualisation tools. The information may be expressed using tables in which each row in the table shows the distinct category.

11. Quantitative or Numerical Data Quantitative data is also known as numerical data which represents the numerical value (i.e., how much, how often, how many). Numerical data gives information about the quantities of a specific thing. Some examples of numerical data are height, length, size, weight, and so on. The quantitative data can be classified into two different types based on the data sets. The two different classifications of numerical data are discrete data and continuous data.

12. Discrete Data Discrete data can take only discrete values. Discrete information contains only a finite number of possible values. Those values cannot be subdivided meaningfully. Here, things can be counted in whole numbers. Example: Number of students in the class

13. Continuous Data Continuous data embodies information that can assume any value within a defined range or interval. It's typically measured on a continuous scale, such as time, temperature, or distance. While analyzing continuous data

14. Data Collection Methods What is Data Collection? Data collection is a process of gathering information from all the relevant sources to find a solution to the research problem. It helps to evaluate the outcome of the problem. The data collection methods allow a person to conclude an answer to the relevant question. Once the data is collected, it is necessary to undergo the data organization process. So, the data collection process plays an important role in all the streams. Depending on the type of data, the data collection method is divided into two categories namely,

15. Data Collection Methods Data can be classified into two types, namely primary data and secondary data. So, the data collection process plays an important role in all the streams. Depending on the type of data, the data collection method is divided into two categories namely, Primary Data Collection methods Secondary Data Collection methods

16. Primary Data Collection Methods Primary data or raw data is a type of information that is obtained directly from the first-hand source through experiments, surveys or observations. The primary data collection method is further classified into two types. They are Quantitative Data Collection Methods Qualitative Data Collection Methods

17. Primary Data Collection Methods Quantitative Data Collection Methods It is based on mathematical calculations using various formats like close-ended questions, correlation and regression methods, mean, median or mode measures. This method is cheaper than qualitative data collection methods and it can be applied in a short duration of time. Qualitative Data Collection Methods It does not involve any mathematical calculations. This method is closely associated with elements that are not quantifiable. This qualitative data collection method includes interviews, questionnaires, observations, case studies, etc. There are several methods to collect this type of data. They are Observation Method Interview Method Questionnaire Method Schedules

18. Secondary Data Collection Methods Secondary data is data collected by someone other than the actual user. It means that the information is already available, and someone analyses it. The secondary data includes magazines, newspapers, books, journals, etc. It may be either published data or unpublished data. Published data are available in various resources including; Government publications, Public records, Historical and statistical documents, Business documents, Technical and trade journals, Unpublished data includes: Diaries, Letters Unpublished biographies, etc.

19. Methods of data presentation Presentation of data refers to an exhibition or putting up data in an attractive and useful manner such that it can be easily interpreted. The three main forms of presentation of data are: Textual presentation Data tables Diagrammatic presentation

20. Textual Presentation In this form of presentation, data is simply mentioned as mere text, that is generally in a paragraph. This is commonly used when the data is not very large. This kind of representation is useful when we are looking to supplement qualitative statements with some data. For this purpose, the data should not be voluminously represented in tables or diagrams. It just has to be a statement that serves as a fitting evidence to our qualitative evidence and helps the reader to get an idea of the scale of a phenomenon. For example, “the 2002 earthquake proved to be a mass murderer of humans. As many as 10,000 citizens have been reported dead”. The textual representation of data simply requires some intensive reading. This is because the quantitative statement just serves as an evidence of the qualitative statements and one has to go through the entire text before concluding anything.

21. Data Tables or Tabular Presentation A table facilitates representation of even large amounts of data in an attractive, easy to read and organized manner. The data is organized in rows and columns. Components of Data Tables Table Number and title: Each table should have a specific table number for ease of access and locating. A table must contain a title that clearly tells the readers about the data it contains, time period of study, place of study and the nature of classification of data. The table number and title is normally written on top of the table. Headnotes: A headnote further aids in the purpose of a title and displays more information about the table. Generally, headnotes present the units of data in brackets at the end of a table title.

22. Data Tables or Tabular Presentation Stubs: These are titles of the rows in a table. Thus a stub display information about the data contained in a particular row. Caption: A caption is the title of a column in the data table. In fact, it is a counterpart if a stub and indicates the information contained in a column. Body or field: The body of a table is the content of a table in its entirety. Each item in a body is known as a ‘cell’. Footnotes: Footnotes are rarely used. In effect, they supplement the title of a table if required. Source: When using data obtained from a secondary source, this source has to be mentioned below the footnote.

23. Construction of Data Tables There are some basic ideas for construction of a good table, they are: The title should be in accordance with the objective of study: Comparison: If there might arise a need to compare any two rows or columns then these might be kept close to each other. Headings: Headings should be written in a singular form. For example, ‘good’ must be used instead of ‘goods’. Footnote: A footnote should be given only if needed. Size of columns: Size of columns must be uniform and symmetrical. Use of abbreviations: Headings and sub-headings should be free of abbreviations. Units: There should be a clear specification of units above the columns.

24. The Advantages of Tabular Presentation Ease of representation: A large amount of data can be easily confined in a data table. Evidently, it is the simplest form of data presentation. Ease of analysis: Data tables are frequently used for statistical analysis like calculation of central tendency, dispersion etc. Helps in comparison: In a data table, the rows and columns which are required to be compared can be placed next to each other. To point out, this facilitates comparison as it becomes easy to compare each value. Economical: Construction of a data table is fairly easy and presents the data in a manner which is really easy on the eyes of a reader. Moreover, it saves time as well as space.

25. Diagrammatic Presentation

26. Methods of data presentation tabular and graphical (histogram, frequency polygon, frequency curve, line charts, scatter or dot plots, and Box and whiskers Data sets and Data tables Variables: Definition and types Hierarchical data order Binary data Clinometric data types

27. Methods of data presentation tabular and graphical (histogram, frequency polygon, frequency curve, line charts, scatter or dot plots, and Box and whiskers Data sets and Data tables Variables: Definition and types Hierarchical data order Binary data Clinometric data types

28. BASICS OF BIOSTATISTICS Data: Definition, sources, categories of data and types of data Methods of data presentation: tabular and graphical (histogram, frequency polygon, frequency curve, line charts, scatter or dot plots, and Box and whiskers Data sets and Data tables Variables: Definition and types Hierarchical data order Binary data Clinometric data types

29. Data: Definition, sources, categories of data and types of data

30. FBS 805: ADVANCED BIOSTATISTICS

31. Correlation Analysis: Pearson and Spearman correlation