Statistics is the science and art of extracting valuable information from data. As a discipline it is concerned with the collection, analysis, and interpretation of data, as well as the effective communication and presentation of results relying on data. Statistics lies at the heart of the type of quantitative reasoning necessary for making important advances in the sciences, such as medicine and genetics, and for making important decisions in business and public policy.

From medical studies to research experiments, from satellites continuously orbiting the globe to ubiquitous social network sites like Facebook or MySpace, from polling organizations to United Nations observers, data are being collected everywhere and all the time. Knowledge in statistics provides you with the necessary tools and conceptual foundations in quantitative reasoning to extract information intelligently from this sea of data.While lower level introductory college statistics based courses are generally based on non-calculus formats, the advanced level statistics taught in graduate and research programs draw heavily on Mathematical calculus and linear algebra based approach.

Most non-calculus Statistics courses are based upon four ideas: exploratory data analysis, sampling studies with experiment designing, figuring out distributional patterns, and drawing conclusions and results based on statistical inference.

In the beginner’s level exploratory data analysis students are often taught to pictorially represent information in the form of graphs such as pie charts, bar graphs, histograms etc for simple univariate data as well as retrieve basic statistical and distributional information from given graphs. This facilitates easy data comparison methods. Students are then progressively  taught to describe distributional patterns, interpret basic statistical and comparative information from a given graphical distributions and conduct comparisons of different data sets based bivariate distributions as well as categorical data. Exploratory data analysis thus acquaints the learners with sound knowledge of basic data representation and conducting analysis based on preliminary data tools and distributional patterns.  Students are also required to learn about various abnormal data patterns and must be able to detect patterns and data points that do not conform to standard distributions.

In Research planning and designs sessions, students are taught to plan their research methodology based on techniques of data collection. Primary data are collected from surveys and experiments which students are taught to design based on their analysis requirements.

Research methods dealing with inferential statistics, formulating results and conclusions from statistical experiments, research studies and observational studies are taught as a final step. Inferential Analysis deals with estimation of population parameters and drawing conclusions based on hypothesis testing. Tests of significance are also used for drawing conclusions and comparing various distributions.

Various statistical distributions and underlying theories of Random variables, probability theory and simulation techniques are undertaken at intermediate levels. With random variables, students are taught the laws of addition and multiplication in probability, Permutation and combinatorial techniques as well as Baye’s theorem. Based on the ideas of independent random variables and their combination properties students are taught the theories of various statistical distributions such as Bernoulli’s , Binomial distribution, Uniform distribution, Poisson’s distribution as well as Normal distribution and standard normal distribution as well as sampling distributions.

Statistics courses generally require students to understand principles of linear algebra and calculus, as well as being able to use graphing calculators and computer programs such as MS Excel. Teaching of statistics is regularly supplemented with statistical software packages such as MS Excel, Minitab, STATA, R, Eviews, SPSS for data analysis. Most statistics teachers include conceptual and descriptive statistical problems in tests which can test the basic understanding of students. Various short answer questions as well as investigative problems cutting across topics point towards the finer understanding and application abilities of students.

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