VV - Data & Collection of data

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Scope, Collection and Classification of Data:

Scope, Collection and Classification of Data Dr. V.Vadivel


Introduction The world Statistics has been derived from the Latin word Status. Status means a political state. Gattfried Achenwall – Father of Statistics. For several decades, the word statistics was associated with the display of facts (details) and figures pertaining to the economic situations in a country, demographic (Study of population) situation in a country and political situations prevailing in a country The above information's are collected and brought out by local governments. Dr. V. Vadivel 2


Statistics helps the scientific workers to understand the nature of variability exist in their observations. Statistics refers to the statistical principles and methods formulated for handling numerical data . Dr. V. Vadivel 3


Definition Statistics is the science Statistic is the scientific method of collection of data, t he presentation of data, t he analysis of data and the interpretation of numerical data Statistical methods are the procedures employed in the design and planning of experiments and in the collection, summarizing, analysing, comparing and interpreting the complex and extensive numerical data Dr. V. Vadivel 4

Some Basic Terms:

Some B asic Terms Data – data in statistics are based on individual observation. Data may be a counted or measured observations. Variable a variable is any characteristics, number, quantity or quality. Variable can be measured or counted. A variable may also be called a data item. Examples of variables – plant height, number of leaves per plant. Dr. V. Vadivel 5


Types of variables Qualitative variables – individuals are distinguished by some quality. Petal colour of flower, smooth or wrinkled nature of surface in seeds – qualitative characters / variables. Quantitative variables – individuals are distinguished by a measurement. Height of plant, number of spikelets per spike, yield of a crop etc. Dr. V. Vadivel 6


Variables are further classified into Continuous variables – measurement can take any value within the certain range showed by the population. Many of the variables studied in biology are continuous variables. Examples - Weight, height, volume, time, percentage etc. Discontinuous or discrete variables – variables have certain fixed numerical values with no intermediate values possible in between. Number of organisms per unit area, number of leaves per plants, number of fruits per plant. Dr. V. Vadivel 7


Variate – An individual observation of any variable is known as variate . Sampling - selection of a part of a population to represent the whole population is known as sampling and the part selected is known as sample. Population – total number of individual observations at a particular time. Finite population – fixed number of variables Number plants in a plot. Infinite population – we can not observe all the values Phytoplankton's in a pond, number of RBC in the human body Dr. V. Vadivel 8

Uses of Biostatistics:

Uses of Biostatistics Agronomists conduct numerous experiments to know the effects of fertilizers on various crops, to know the best methods of cultivation etc. The Plant Breeders use statistics in the study of inheritance of characters in improving the crops. Statistics help the researchers in Agricultural Engineering in testing the weed control machines, transplanting implements, comparison of various machines or implements used in harvest, ploughing etc. Dr. V. Vadivel 9


Data The values recorded in an experiment or observation are called data . The person who collecting the data is called investigator . Example: Estimation of oxygen in different water samples. The amount of oxygen in the water sample is the data. Arranging values in columns is called tabulation Dr. V. Vadivel 10

1. Different Types of Data:

1. Different Types of Data Dr. V. Vadivel 11

1.1. Raw Data:

1.1. Raw Data The information collected through censuses, surveys, field observations (biology) etc., is called a raw data. The word data means information. The adjective raw attached to data indicates that the information thus collected and recorded cannot be put to any use immediately and directly. Raw data is like a raw rice. A raw rice has to be cooked properly and tastefully before it is eaten. Similarly, a raw data has to be converted into a proper form - tabulation, frequency distribution form etc., before any inference is drawn from it. Dr. V. Vadivel 12

1.2. Qualitative Data:

1.2. Qualitative Data If we classifying plants qualitatively as pigmented plants, non pigmented plants etc., we meet qualitative differentiation. Example: Men classified as single, married, widowed, divorced and separated. Nationality of persons. Flower colour Taste – good, better, bad etc Dr. V. Vadivel 13

1.3. Quantitative Data:

1.3. Quantitative Data When observation are in measurements, a quantitative data is obtained. Example Grain yield in different farms Grain number per hill Number of unfilled grains Dr. V. Vadivel 14

1.4. Continuous Data:

1.4. Continuous Data In continuous variation there is a complete range of measurements from one extreme to the other. Height is an example of continuous variation – individual can have a complete range of heights, for example 1.6., 1.61., 1.62., 1.625., etc., meters high. Continuous variation is the combined effect of many genes (known as polygenic inheritance) and is often significantly affected by environmental influences. Milk yield in cows, for example, is determined not only by environmental factors such as pasture quality and diet, weather, and the comfort of their surroundings. Dr. V. Vadivel 15

1.5. Discontinuous Data (discrete data):

1.5. Discontinuous Data (discrete data) Discontinuous data is based on features that can not be measured across a complete range. The variate takes values in integers i.e., in whole numbers. Example: Blood groups are a good example You are either one blood group or another – you can’t be in between. Number of grains in a plant. Number of insects caught Discontinuous variation is controlled by alleles of a single gene or a small number of genes. The environment has little effect on this type of variation. Dr. V. Vadivel 16

1.6. Chronological Data:

1.6. Chronological Data The chronological data show the trend of observations in different times. Example: Production of food grains in different times. Imports and exports of food grains in different periods Dr. V. Vadivel 17

1.7. Geographical Data:

1.7. Geographical Data When population is shown for each of the states in India, we have data which can be classified geographically. Example: Food grains produced by different countries arise due to differences in geographical positions of the countries. Rainfall in different regions. Dr. V. Vadivel 18

1.8. Primary Data:

1.8. Primary Data Data collected by the investigator himself / herself for a specific purpose. Example: Date collected by a student for his / her thesis or research project. Dr. V. Vadivel 19

1.9. Secondary Data:

1.9. Secondary Data Secondary data refers to data that was collected by someone other than the user. Example: Secondary data may be abstracted from existing records, published sources or unpublished sources. Dr. V. Vadivel 20

2. Population:

2. Population Dr. V. Vadivel 21

2.1. Population - Definition:

2.1. Population - Definition A population consists of all the individuals or objects in a well defined group about which information is desired to answer a question. Population as used in statistics does not necessarily refer to all the individuals in a particular community. Population may be the salary of each school. Dr. V. Vadivel 22

2.2. Finite Population:

2.2. Finite Population The population containing limited number of individuals is called a finite population. Example: Number of students in a class. Dr. V. Vadivel 23

2.3. Infinite Population:

2.3. Infinite Population The population, containing unlimited number of individuals, is called an infinite population. Example: Stars in the sky Fishes in the sea Dr. V. Vadivel 24

3. Sample:

3. Sample Dr. V. Vadivel 25

3.1. Sample - Definition:

3.1. Sample - Definition A sample is the part of a population about which information is gathered. Getting a sample from a population is called sampling. Example: Only a few rice is examined from a boiling pot to arrive at a conclusion. Only a few grapes are tasted before buying a bunch. Dr. V. Vadivel 26

Collection of Data:

Collection of Data Dr. V. Vadivel 27


Introduction The process of getting values and facts from an observation or experiment is called collection of data. Data is collected by the following methods …… Dr. V. Vadivel 28

1. Direct Personal Interview:

1. Direct Personal Interview In direct personal interview, the investigator directly interview the individual for collecting the data. Example: The blood group of a class of students can be obtained by personal interview. The data obtained by this method is original, reliable, authentic and accurate. It is more expensive and more time consuming. Dr. V. Vadivel 29

2. Indirect Oral Interview:

2. Indirect Oral Interview In indirect oral interview, the data is obtained from witnesses. Example: The number of smokers among college students can be obtained by interviewing the shop-keeper or friends. This method is economical. But the data may not be reliable. Dr. V. Vadivel 30

3. Through Correspondents:

3. Through Correspondents In this method, data is collected through appointed persons called correspondents. News agents of news papers are typical correspondents. The information for news papers are collected by this method. Dr. V. Vadivel 31

4. Questionnaire:

4. Questionnaire Questionnaire is a paper containing a set of questions to be answered by the individuals for collecting data. The questionnaire may be set through post or through correspondents. A questionnaire should possess the following characters. The purpose of the questionnaire should by given on a covering letter. The questionnaire should be small. The questionnaire should be simple. ‘Yes’ or ‘No’ type questions should be asked. Multiple choice questions can be given. Dr. V. Vadivel 32


The questions should be direct. Unambiguous questions should not be asked. The questions should not hurt the sentiments. Questions, related to age, polities, private life etc., should be carefully worded. Cross check questions may be included to bring out information about age, date of marriage, etc. Dr. V. Vadivel 33

5. Experiments:

5. Experiments Data can be collected by doing experiments. The amount of oxygen present in water samples can be collected by titration experiments. Dr. V. Vadivel 34

6. Census:

6. Census Census is a method of collection of data. Census is the counting of all members of a population one by one. Example: The tree in a coconut grove are counted by census method. Human population is assessed by census. The results obtained in census are reliable and accurate. It is very expensive. Dr. V. Vadivel 35

7. Sampling:

7. Sampling Sampling is a method of collection of data. Sample is the representative fraction of a population. In sampling method, a small group is taken from a large population. This small group is the sample. Analysis of the sample gives an idea of the population. When the population is very large (infinite), sampling is the suitable method of data collection. Example: One rice is tested from a pot of boiling rice to arrive at a conclusion. There are two types of sampling, namely Random sampling Non-random sampling Dr. V. Vadivel 36

1. Random Sampling:

1. Random Sampling In random sampling, a small group is selected from a large population without any aim. The small group selected is called a sample. In this method, each item of the population has an equal and independent chance of being included in the sample. Random sampling is of 3 types. Simple random sampling Stratified random sampling Cluster random sampling Dr. V. Vadivel 37

A. Simple Random Sampling:

A. Simple Random Sampling This is the most popular and simplest method of selecting a random sample from a finite population. This method is also called lottery method. In this method, all items of population are numbered on separate slips of paper of identical size, shape and colour. The slips are folded and mixed up in a box and a blindfold selection is made. It indicates that the selection of each item thus depends on chance. This lottery method Dr. V. Vadivel 38

Lottery Method:

Lottery Method Lottery method is common in agricultural and biological sciences. Example: Take samples of 10 individuals randomly from the population of 50. We must write the numbers of all the 50 individuals on a slips of the same size, shape and colour, mix them up and a blindfold selection of 10 slips is to be made (replacing each slip once it has been drawn out). The numbers corresponding to the slips drawn will constitute the random sample. If the population is infinite, this method is in applicable. Dr. V. Vadivel 39

Random Numbers:

Random Numbers In this method, random selection is done with the help of tables of random numbers. There are many random number tables are available. Some random number tables are familiar. They are Tippets Random Number Tables (1927) Fisher and Yates Tables (1938) C.R. Rao , Mitra and Mathai table of Random Numbers (1966). Snedecor and Cochran Random Number Tables. Dr. V. Vadivel 40


Example If we want select 10 pods from 200 pods, each pod should be given a number from 001 to 200. If we take Snedecor and Cochran (1988) random table, we considered the first three digits and later two are ignored. If the number we select 5 th line and 39 th column, i.e., 20002. The numbers coming after this are 05217, 03164, 19774, 12696, 05437, 178805 ….. By using these numbers we select the samples having number of 200, 052, 031, 197, 126….. Dr. V. Vadivel 41

B. Stratified Random Sampling:

B. Stratified Random Sampling In stratified random sampling, the population is divided into groups on the basis of certain characteristics. Example: We want to select a sample of 100 students from a college population of 1000 students consisting of 700 girls and 300 boys. The whole college population should be divided into two strata. One with 700 girls and other with 300 boys. Now by stratified random sampling method select 70 girls from total of 700 girls and 30 boys from the total of 300 boys/ Dr. V. Vadivel 42

C. Cluster Sampling:

C. Cluster Sampling The whole population is divided to a number of relatively small clusters or groups. Then some of the clusters are randomly selected. Example: If we want to survey the general health of the college student in a state consisting of 5000 colleges. Here we consider each college as a cluster. Now we can randomly select several college and conduct the survey. Dr. V. Vadivel 43

Non-random Sampling:

Non-random Sampling In this method, a sample is collected from a large population based on the convenience, judgment and consideration of the investigator. In this method, each individual does not get a chance of included in the sample. Example: If 20 students are selected from a college of 1000 students, the investigator selects 20 representatives. Dr. V. Vadivel 44

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