Statistics is a domain concerned with acquiring information, their interpretation, evaluation, and representation. It is the science that is involved in depicting quantifiable data structures and inferences for the obtained set of empirical information. Research in the field of statistics has a major significance in all scientific disciplines. These scientific domains further promote the generation of new statistical theory and methods.
Uncertainty and variation constitute the very essence of statistics. The outcome of several situations that an individual encounter in their daily life is uncertain or indecisive. This is might be a result of probable cases. First, the outcome of a situation is still undecided or second, uncertainty arises since the outcome has already been decided but we are unaware of it.
Understanding the concept of uncertainty and variations can be mediated with the use of various statistical methods like the null hypothesis of chi-square test, t-test, z test, probability distribution, correlation, regression, etc.
Statistics has several other descendants and each of them has vital applications in various fields of science and technology. R programming is one such statistical tool which is commonly used as a method for conducting biostatistical analysis. Students pursuing biostatistics are eventually introduced to this topic that majorly involves basic computer programming which initiates statistical calculations.
What is Biostatistics?
Biostatistics is an interdisciplinary subject that involves applications of statistical tool and analysis on research in the various fields of biological science. It helps to ascertain the viability of the likes of biochemical, genetic, proteomic analysis and to arrive at a statistically supported conclusion and solve problems.
Biostatistics not only contributes in the study and research of public health, nutrition, and medicine but also in other domains that include molecular biology, virology, genomics, validation of clinical trials, epidemiology, bioinformatics, microbial genetics, and so on. The key steps of bio statistical analysis involve-
- Data collection and correct interpretation
- Evaluation of hypothesis from the collected information
- Implementation of statistical methods to generate valid inferences
The interpretation of such huge amount of data is required to be handled by skilled professionals like biostatisticians. They are trusted with the responsibility of translating crucial data to comprehensive and significant inferences. This is why they are always on high demand in all major life science and healthcare sectors to carry out crucial data analysis that often forms the basis of path-breaking research and inventions.
Domains that have insurmountable applications of biostatistics includes-
- Major studies involving health issues like AIDS, diabetes, cancer, and other chronic disorders.
- Population evaluation and human development.
- Study of genetic mutations and diseases associated with them like Down’s syndrome, Alzheimer’s disease, Hemophilia, SCID, and so on.
Biostatisticians are required to work with variables and possibilities that require deep understanding to be able to generate logical explanations out of them. They often collaborate with life science and biomedical scientists to detect and disentangle challenges related to research and also identify problems that pose a threat to the quality of life and public health.
The primary purpose of biostatistics is to unravel the received data to understand their statistical significance so that they can be utilized in conducting further studies. It can be established that biostatistics has an undeniable importance in quantitative research and safeguarding public health that aims to improve lives.
This subject is often a part of several parent disciplines like biotechnology, biochemistry, microbiology, earth sciences, and genetics. Students can always turn to comprehensive sources for better understanding of the philosophy of biostatics as an independent discipline.
Biostatistics and its important topics like R programming are also pursued as a major in college and have varied prospects in terms of career paths and also further research. Being a subject that involves concepts of multiple other scientific domains, coping with biostatistics coursework can often be challenging. It is suggested that students turn towards professional guidance and avail the requisite support with the crucial subject matters of biostatistics.
The origin of R programming language
R programming is an open-source language that was developed with the vision of simplifying data analysis, statistical calculations, and their representation as graphical modules. R language is known for its user-friendly interface. Earlier it was primarily used for academic and research purposes but, owing to its intuitive features, R programming is now extensively used in enterprises as well.
The R programming is a derivative of S programming language that was developed by the Bell Laboratories. R was developed by Robert Gentleman and Ross Ihaka from the Auckland University, New Zealand. It was majorly used by biostatisticians and is now the standard programming language for all categories of statistical computing. The source code of R programming language is maintained by the R Development Core Team.
Breaking down the features of R programming
Understanding R programming will provide an entirely new concept that can be used to analyze, compare, manipulate, and report data. R language was initially designed for use in statistics only, but it has proven to be useful irrespective of which scientific field an individual is pursuing. It is extremely flexible and powerful when it comes to conducting classical statistical evaluations.
R is a widely accepted language for core statistical computing since it can be downloaded for free. Multiple communities operating online include professional R users. They are highly resourceful when it comes to answering queries about the language. They have immense contributions towards making the process of learning an entirely different genre of technology ten folds simpler. They can clarify doubts and expand a student’s ability to implement R programming.
Some of the vital techniques that R programming offers include-
- Time-series analysis
- Modern and conventional statistical models like GLM, ANOVA, and Tree
- Linear modeling
- Nonlinear modeling
- Clustering
- Loop statements
- Classification technique
Students who have just begun their pursuit of understanding R programming language can count on resourceful blogs, for a thorough characterization of the topic.
Importance and applications of R programming
With the rapid expansion of numerical data and other information in all scientific disciplines, dealing with them manually to arrive at viable, accurate conclusion has proven to be challenging. This is a major reason why learning these statistical tools has become more critical for students and scientists.
All modern career streams are highly demanding in terms of a candidate’s aptitude for analysis and problem-solving abilities. This is why it is advised that students work towards developing skills in important topics like R programming whether or not it is a part of their academic curriculum. The knowledge of a high-level programming language like R will act as an added advantage in both academic and professional fields.
Programming languages might come across as an impenetrable and intimidating concept especially for students with no solid background in computer science. But, on the contrary, learning a programming language involves some very simple steps with minimal theoretical aspects.
Students can turn to numerous sources available both online and offline that can offer ample assistance with learning programming languages. This will accelerate the learning process and also help in gaining the concepts through a professional approach.
Simple tricks to stay ahead in your R programming class
For students who belong to the domains of biological science and the likes often find it daunting to cope with the unique features and characteristics of programming languages. Since the understanding of programming languages is imperative to excel in a professional career, it is important to fetch adequate help at the earliest.
Students must acknowledge the immediate sources for solving their preliminary doubts on programming. They always have their peers, college professors, and seniors around who can be approached to gain clarity on matters related to the subject. This will also allow everyone to relate to each other’s unique skills and perspective of the subject matter that will ultimately lead to a well-constituted understanding of the topic.
Apart from gathering assistance from external sources, students can work towards making subtle changes in their regular pattern of preparing for a subject. Certain little changes surely go a long way in ensuring better academic performance in complex topics like R programming. Here are some of the key tips that students can follow to get the best results out of their R programming assessments-
- The terminologies–
The major challenge that students face while learning a coding language is to adapt to their unique terminologies and instructions. They must primarily begin the process of familiarizing with the terms, texts, and symbols before diving into writing elaborate codes. Students must strictly follow the key instructions that are exclusive for R programming to avoid making mistakes later while developing complex programs.
- Work on the mathematical skills–
Unlike other high-level programming languages, R programming requires extensive applications of mathematics. Starting from basic algebraic functions to complex vector data types, R programming requires the users to bear prior knowledge each of these contexts. Students should brush up their skills of mathematical calculations that will give them a head start while writing computational codes in R language.
- A brief understanding of Matrices–
Several numerical data are collected in matrices form that can be easily evaluated using R languages. It suggested that students develop their knowledge of matrices to optimize the statistical calculations that need to be conducted in matrix form in R programs. Students who do not have any basic knowledge of matrix operations like multiplication, they are recommended to resort to informative manuals, lectures, and textbooks for detailed understanding.
R language comprises all the functions of a conventional calculator. It constitutes the basic techniques and unique terminologies of computer programming and other mathematical concepts like matrices, data types, the rudimentary knowledge of algebra, and so on. The topic might appear to be quite demanding but with the right kind of assistance, it can be dealt with efficiently.
Why assistance with statistics coursework is necessary?
In all modern academic fields and professional sectors, the knowledge of mathematical calculation, data analysis, and especially with the use of statistical methods is the most sought-for attribute in a prospective candidate.
It is recommended, irrespective of whether a student is an aspiring data scientist, engineer, researcher, or an entrepreneur, he or she must have a solid insight on statistical analysis. The right kind of professional help can make it happen without any grueling efforts or sleepless night from the students’ end.
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Author bio-
Emma McCoy is a well-known author who invests huge efforts in assisting university students with their coursework. She is highly qualified with years of experience that makes her one of the most ideal sources to clarify issues regarding assignments. She uses current methods of pedagogy to teach the students. Emma McCoy is a professor of statistics, faculty of natural sciences and Department of Mathematics. Her research interests are Time Series, Computational statistics and Wavelets. She is known to be highly comprehensive with her teaching approach and has helped hundreds of students improve their academic performance. She has done M.S. in Statistics from Hult International Business School. Through her unique teaching technique students gain clarity on the subject. For more information about her, you can find it here http://wwwf.imperial.ac.uk/~ejm/