Lectures prepared by:Mr. K.O. Olorede

Statistical Methods for Biological Sciences ISTA 201

HARMATTAN 2016KWASU

Applied Statistical Methods in Agriculture, Health and Life

Sciences by:Bayo Lawal

Introductory Statistical Methods for Biological Data

1. Statistical data 1.1 I ntroduction

Statistical data are the basic raw materials f or statistical investigation. I nf ormation is essentially ref erred to as data in Statistics. I n everything we do, we seek inf ormation in order to guide us in all our activities. I n f act, activities we embark upon today will provide information to guide us better in executing similar activities in (subsequent days) f uture activities. However, gathering information may be f ormal or inf ormal.

1.1.1 Formal Gathering of I nformation This involves documented inf ormation in which every bit of what has been observed in the past or what is being observed currently is expected to be kept in its original (or raw) f orm.

1.1.2 I nformal Gathering of I nformation This involves inf ormation about experiences in the past which were not immediately captured. I t may not always provide desired level of inf ormation that is equivalent to complete retrieval as in the f ormal method of gathering inf ormation.

1.2 Techniques of Data Collection I n order to obtain statistical data, the investigator may either:

1. go and pursue into the records of some institutions, whether public or private that collect and publish data as a routine, or

2. make a special survey by fi eld inquiry. Thus, there are two sources of inf ormation available as listed above with the f ormer usually termed as Secondary source and the latter termed as Primary source. 1.2.1 Secondary Source Secondary source provide data readily available or previously used data f rom administrative sources such as journals, newspapers, databases, and offi cial compilations etc.

Advantages i. I t gives quicker information than the primary source ii. I t is more timely than the primary source iii. I t is not as expensive as the primary source Disadvantages i. I t gives lesser inf ormation than the primary source ii. I t may be wider or narrower than the objectives of the research iii. I t may not be as detailed inf ormation as the primary source

1.2.2 Primary Source Data f rom primary source are the datasets obtained f rom objects directly concerned. Primary sources of data provide data compiled as a result of counting of population count or results obtained f rom a sample of the population where the population is too large f or individual count. Primary sourced data can be collected either by i. Direct personal observations ii. Personal interview iii. Mailed questionnaire iv. Questionnaires administered by enumerators v. Direct interview by people

Advantages i. I t supplies exact inf ormation ii. I t gives more reliable data than the secondary source iii. I t gives detailed data than the secondary source Disadvantages i. I t is very expensive ii. I t takes time iii. I t may involve large non-responses

Methods of Describing Data

Variate: A variate is any quantity or attribute whose values varies from

one unit of investigation to another. Observation: An observation is the

value taken by a variate for a particular

unit of investigation.

1.3 Types of data Although the number of phenomena that can be measured is almost limit-less, data can generally be classifi ed as one of two types: Quantitative or qualitative.

i. Quantitative data: Quantitative data are data values that are numeric. They are observations that are measured on a numerical scale. The most common type of data is quantitative data, since many descriptive variables in nature are measured on numerical scales. For example, the heights of f emale basketball players, number of deaths in country, number of births in a community, count of air bubbles in a wind screen, body mass indices of students, Number of leaves per plant, yield of cowpea, the heights (or weights) of students in a class, the number of Lecturers in the f aculty of Science, University of I lorin, Nigeria. The measurements in these examples are all numerical.

ii. Qualitative data: Qualitative data are data values that can be placed into distinct categories, according to some characteristic or attribute. For example, the hair colour and skin colour of f emale basketball players, class of degrees of College students, gender of people, tribe of citizens etc are qualitative data All data that are not quantitative are qualitative. Quantitative variates can also be divided into two types. They may be con-tinuous, if they can take any value we care to specif y within some range or discrete if their values change by steps or jumps.

1.4 Variables Variables whose values are determined by chance are called random variables. There are two types of variables: qualitative variables and quantitative variables. Qualitative variables are nonnumeric in nature. Quantitative variables can assume numeric values and can be classified into two groups: discrete variables and continuous variables. Definition of terms

Explanation of the term- discrete variables: Discrete variables are variables that assume values that can be counted-f or example, the number of days it rained in your neighbourhood f or the month of March.

Explanation of the term- continuous variables: Continuous variables are variables that can assume all values between any two given values-f or example, the time it takes f or you to do your Christmas shopping.

Explanation of the term- population: A population consists of all elements that are being studied. For example, we may be interested in studying the distribution of STA 113 scores of 100 level at KWASU. I n this case, the population will be the STA 113 scores of all the 100 level students on KWASU campus.

Explanation of the term- sample: A sample is a subset of the population. For example, we may be interested in studying the distribution of STA 113 scores of f reshmen at a college campus. I n this case, we may select the STA 113 score of 100 level students at KWASU f rom an alphabetical list of the students' last names.

Explanation of the term- census: A census is a sample of the entire population. For example, we may be interested in studying the distribution of STA 113 scores of 100 level at KWASU. I n this case, we may list the STA 113 scores f or all 100 level students on KWASU campus.

Explanation of the term- parameter: A parameter is a characteristic of or a f act about a population. For example, we may be interested in studying the distribution of ACT math scores of f reshmen at a college campus. I n this case, the average STA 113 score f or all f reshmen on this particular campus may be 25. Parameters are characteristics of population

Explanation of the term- statistic: A statistic is a characteristic of or a fact about a sample. For example, we may be interested in studying the distribut ion of STA 113 scores of f reshmen at a KWASU campus. I n this case, the average STA 113 score f or every tenth 100 level students f rom an alphabetical list of their last names may be 22. A statistic is a characteristic of sample.

Explanation of the term- random sample: A random sample of a particular size is a sample selected in such a way that each group of the same size has an equal chance of being selected. For example, in a lottery game in which six numbers are selected, this will be a random Sample of size six, since each group of size six will have an equal chance of being selected.