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Statistics: - Though for a layman the term ‘Statistics’ may mean data of some sort, it has a much broader meaning. Statistics is a kind of research methodology, which deals with collection, analysis, interpretation and presentation of data. It is applicable to a wide variety of academic disciplines, from natural and social sciences to humanities, government and business. Statistics plays a very vital role in the formulation of economic plans, estimation of industrial demand and sales figures, maintenance of the quality of products and minimization of inspection costs, forecasting agricultural production and helping studies in the field of genetics. Thus a Statistician is well versed in statistical methods and its applications. The numerous applications of Statistics make its knowledge indispensable.
There are many statistical tools and techniques, one being Descriptive statistics. Descriptive Statistics deals with the methods of organization, reduction, summarization and presentation of data in order to make them amenable to rational analysis and interpretations. The level of complexity of statistical problems varies. It may range from computation of simple measures like mean, median and mode to computation of standard regression results and complicated forecasts using time series data. Across all colleges students are often tested on these topics.
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Some topics relating to statistics assignment help and statistics homework help provided bTabular and Diagrammatic Presentation of frequency and non-frequency data
Measures of Central Tendency
Dispersion
Skewness and Kurtosis
Probability of an event
Conditional Probability
Independence of events
Discrete and Continuous Probability Distributions and their properties
Regression Analysis
Correlation Analysis (Including Rank correlations)
Random sample generation
Computation of expectation and standard error
Point and Interval Estimation of Parameters
Maximum Likelihood Estimation
Testing of Hypothesis
Power and Confidence Intervals
Non-parametric Tests
Contingency tables and Chi-square Test
Analysis of Variance (ANOVA)
Design of Experiments
Sample Surveys
Time Series Analysis and Forecasting
Scaling of Scores and Ratings
Z-Scores
T-scores
Principal Components
Factor Analysis
Cluster Analysis
Discrimination and Classification
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