Fundamentals of Biostatistics
Biostatistics can be defined as the statistical technique which can help in scientific researches in health-related fields such as medicine, biology, and public health. It can also be included in the field of applied statistics and it is widely included in different categories of activities such as psychology. The role of the bio statistician is much significant in the development of new methods. The concept of biostatistics is much useful for the monitoring patients suffering from high blood pressure. There are different categories of estimation models associated to biostatistics which can help in measurement of blood pressure. Descriptive statistics is one of the fundamental concepts which are associated to biostatistics and it helps in the treatment of several diseases such as the pulmonary heart diseases and cancer. Quantiles is defined as the approach which can be much helpful in quantifying the spread in a data set. Probability is another fundamental unit which is associated to biostatics, along with that there are other concepts associated to this subject as well such as the mutually exclusive sets and the not so mutually exclusive sets.
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Introduction to Elements of Biostatistics
There are numerous elements which are associated to biostatistics such as designing the methodology of an experiment, selection of the sampling procedure for the collection of raw data, collection, and representation of data, measures of central tendency are another major unit of biostatistics. The other elements of biostatistics are measurement of the dispersion, F tests, and analysis of variance, distribution patterns, statistical interference, tests of the significance of mean, statistical interferences, Chi square tests, probability, regression, vital statistics, standard formula, computer application in biostatistics.
The branch of Biostatistics that deals with data
The branch of biostatistics which deals with data is defined as descriptive statistics, this type of statistics is much helpful in describing the characteristic feature of a dataset. Descriptive statistics are much helpful in the measurement of the central tendency and measures of variability. Measures of variability makes the most out of range, standard deviation, interquartile range and standard error. It can also be said that there are four different categories of descriptive statistics such as measures of frequency, measures of the central tendency, measures of dispersion, and measures of position. On the other hand, there are other forms of descriptive statistics as well such as the mode, mean, median, skewness, kurtosis, standard deviation, third quartile, and first quartile.
Some Basic Concepts of Biostatistics
1: Variable
There are different categories of variables which can be considered while working with biostatistics such as independent variables, dependent variables, continuous variables and the categorical variables. The impact of the dependent variables can be understood in the first place using the independent variables. Whereas, dependent variable can also be defined as the criterion variable and this type of variable is considered mostly in the experimental setup. The dependent variables mostly rely on the action of the independent variables. The variables which are affected by other variables in econometrics can be defined as endogenous variables. On the other hand, a variable which have the capability in impacting the relationship between dependent and independent variables can be defined as the lurking variables. Categorical variables are a wide category of variables which is infinite and does not have any numerical data.
2: Quantitative Variable
It is defined as the type of variable which helps in evaluating numerical data and it can represent some kinds of measurement. There are two classification of quantitative variables such as the discrete variable and continuous variable. Discrete variables is defined as the type of variable which take countable number of values, whereas continuous variables can take any value in some range of variables.
3: Qualitative Variable
Qualitative variables are defined as the type of variable which are not measured. The result of the qualitative variables is not measured from counting or any type of measurement. The value of this type of variable are measured with the help of attributes or characteristics.
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Types of Data
There are different categories of data which are associated to biostatistics such as the interval data, ordinal data, and nominal data.
Nominal data: It is defined as the type of data which are measured on the basis of various groups such as absence of order, conclusive mode of the data, not quantifiable modes of the data, qualitative data property, and inconclusive mean value. This type of data can also be defined as the categorical data. In this data type, if the binary data represents two set of values the nominal data is set to be multi-valued.
Ordinal data: It is defined as the type of statistical data where the variables have natural ordered categories and the total distance between the two categories are unknown. This type of data exists only in ordinal scale. Avoiding loss of power is one of the primary objectives of the ordinal data. Ordinal data is used mostly in different types of statistics such as bivariate statistics, univariate statistics, and regression application. There are different categories of statistical models which are associated with ordinal data such as proportional odds model, baseline category logit model, ordered stereotype model, and adjacent categories legit model. This type of data is mostly considered in behavioural as well as social sciences. At the same time, these types of data are also used in government and business settings. Survey research, personality testing are the other areas of application of ordinal data.
Interval data: It is defined as the type of numerical measurement and it is used between two numbers in a meaningful measure. Interval data can be measured along a long scale where each point is placed at an equal distance from each other. It is one of the distinctive attributes when compared to the nominal data. The value of differences between two scales can be determined in the first place using interval data. Interval data makes the most out of the numerical values and are widely used in the arithmetic operations. Negative variables can also be measured with the help of the interval data.
