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A Brief Introduction To Biostatistics
Biostatistics covers the application of statistical analysis methods and techniques to the scientific research of living organisms. The term arises due to the combination of biology and statistics and the combination of these two fields has seen great developments in biological research which makes Biostatistics quite an important tool in the biological field.
Sub-Fields, Where Biostatistics Has Widely Been Applied, Include
2. Public health
Significance Of Biostatistics
I. Through Biostatistics, biological researchers can develop tools that will aid in making their experimental results more accurate and error-free.
II. With the use of statistical techniques, the results of various experiments can easily be visualized in a way that makes it easier to come up with meaningful conclusions. Biostatistics also makes it extremely easy for biological researchers to present the information in a non-technical way making it easy to understand.
III. In public health, medical practitioners use statistical methods to analyze clinical trials. These trials are important in understanding how different people react to different medical conditions and which are the suitable methods of dealing with such medical conditions on the wider public scale.
Topics Covered Under Our Biostatistics Homework Help Service
Ø Multivariable Analysis – Include multiple regression modeling and multivariate analysis. Covers multiple linear regression, classification, and regression trees.
Ø Statistical Computing – Mainly covers the use of statistical software and programming techniques in data analysis. Some of these techniques include R programming, SAS software, SPSS, Chronux, etc.
Ø Logistic Regression and Survival Analysis – includes regression modeling of time-to-event or categorical outcomes with continuous and categorical predictors.
Ø Design and Conduct Of Clinical Trials – Basically covers the development, conduct, and interpretation of clinical trials. It covers experimental design, methods of randomization, sample size determination, adverse event monitoring, etc.
Ø Quantitative Data Management
Ø Application of Statistical Methods in Clinical Research
Ø Genomics Data Mining and Statistics – Mainly covering the design and analysis of data from microarray and high-throughput sequencing experiments.
Ø Categorical Data Analysis – Covers logistic and Poisson regression, binomial and Poisson distribution, smoothed regression modeling, non-parametric methods for original data, cluster analysis, etc.
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