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Course code: BFT2074 Course Title. BIOMETRY AND EXPERIMENTAL DESIGN Observed data & their Characteristics Prof Dr Md Ruhul Amin. Introduction and Data Collection. 1.1 Some definitions
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Course code: BFT2074 Course Title BIOMETRY AND EXPERIMENTAL DESIGN Observed data & their Characteristics Prof Dr MdRuhulAmin
Introduction and Data Collection 1.1 Some definitions • Statistics:Statistics is a study dealing with the process of collecting, organizing, summarizing, analyzing and presenting (COSAP) information. • Population:Population is the totality of items or things under consideration possessing certain characteristics of interest. • Parameter: Parameter (yardstick) is a summary measure that describes a characteristic of an entire population. • Sample: Sample is the representative portion of the population that is selected for analysis. • A statistic is a summary measure computed from sample data that is used to describe or estimate a characteristic of the entire population.
….Definitions Descriptive statistics Inferential statistics e.g. Mean height of SBS students: 5’ e.g. This one is better than that one Descriptive statistics is the method that focus on the collection, presentation and characterization of a set of data in order to properly describe the various features of that set. Inferential statistics is the method of estimating the characteristics of a population or the making of a decision concerning a population based only on sample results.
Definitions… Variable:a variable is any measured characteristic or attribute that differs for different subjects. For example, if the weight of 30 subjects were measured, then weight would be a variable. If no. of students in different classes were counted then no. of students counted would be a variable.
Biometry Statistics applied in the field of Life Science is called BIOMETRY or BIOSTATISTICS LifeScience includes Biological Science, Medical Science, Agricultural Science
Why data are needed? Provide the necessary input to a survey Provide the necessary input to a study Measure the performance of an ongoing service or production process Evaluate the conformance of standards Assist in formulating alternative courses of action in the decision making process Satisfy our curiosity
Observation of a particular event Generally an observation can be classified as either QUANTITATIVE or QUALITATIVE. Quantitative observations are based on some sort of measurement or counteg. Length, weight, temperature and pH, number of balls in the basket. Qualitative observations are based on categories reflecting a quality or characteristics of the observed event eg. Male vs female, diseased vs healthy, live vs dead, colouredvscolourless etc. Any observation when recorded is called DATA.
Variables or Data types There are several data types that arise in statistics. Each statistical test requires that the data analyzed be of a specific type. Most common types of variables- Quantitative variables – fall into two major categories a) Continuous variables- can assume any value in some (possibly unbounded) interval of real numbers. Common examples include length, weight, temperature, volume and height. They arise from MEASUREMENTS. b) Discrete variables- assume only isolated values. Examples include clutch size, trees per hectare, teats per sow, no. of days per month, no. of patient for a particular disease in hospitals. They arise from COUNTING.
Variables or Data types… 2.Ranked data (ordinal variables) are not measured but nonetheless have a natural ordering. For example, candidates for political affiliation can be ranked by individual voters. Or students can be arranged by height from shortest to tallest and correspondingly ranked without being measured. A candidate ranked 2 is not twice as preferable as the person ranked 1. 3.Categorical data or qualitative data: Some examples are species, gender (M/F), healthy vs diseased and marital status (married/ unmarried). Unlike ranked data, there is no ‘natural’ ordering that can be assigned to these categories.
Collecting data Primary data - the data that are gathered by researcher or data collector Secondary data (source data) arethe data obtained from data reservoir/data bank Once you have decided to use either secondary data or primary data or both, the next step is on how to collect the data. To collect secondary data is not a big problem. Just to approach the authority. Primary data collection needs specific design to have accurate and representative data at a minimum cost and time.
Table and graphs The data collected in a sample are often organized into a table or graph as a summary representation. The following table shows the no. of sedge plants found in 800 sample quadrats (1m2 ) in an ecological study of grasses. Example 1. A frequency distribution table Table 1.
Example 2. The following data were collected by randomly sampling a large population of rainbow trout. The variability of interest is weight (lb)
Example 2…. Rainbow trout have weights that can range from almost 0-20 lb or more. Moreover their wt.s can take any value in that interval. For example, a particular trout may weigh 4.3541 lb. From example 2 lb.
A sample of bar graph…. Category Categories may be: 4 different states in Malaysia Series Series may be people Bumiputra Chinese origin Indian origin
Exercises 1. For each of the following random variable determine whether the variable is categorical or numerical. If numerical, determine whether the variable of interest is discrete or continuous.
