R Programming For Statistics by DR. SOURAV DAS
R Programming For Statistics by DR. SOURAV DAS
R Programming For Statistics by DR. SOURAV DAS
Internet is filled up with a lot of data, and then analysis of data and getting to a
higher level. So students of economics, background, mathematics, statistics are all
doing their hardest to reach. to the required outcome.
Hence, when I saw that not much proper analysis is available I came up with my
personalised course in it .
Let's discuss about it detail.
· Probability in R
· Discrete distributions
· Benford Distribution
· Bernoulli
· Binomial
· Hypergeometric distribution
· Geometric distribution
· Multinomial
· Poisson distribution
· Zipf’s law
· Continuous distributions
· Beta distributions
· Dirichlet distributions
· Cauchy
· Chi-Square distribution
· Exponential
· Fisher-Snedecor
· Gamma
· Levy
· Log-normal distribution
· Pareto Distributions
· Student’s t distribution
· Uniform distribution
· Weibull
· Binomial Distribution
· Normal Distribution in R
· Beta Distribution in R
· Hypothesis in R
· Types of Hypothesis
· Null Hypothesis
· Alternative Hypothesis
· Decision Errors in R
· Type I Error
· Type II Error
· Confidence Intervals
· Covariance Matrix
· Pearson Correlation
· Root-Mean-Square Error
1. Statistics: R is widely used in introductory and advanced statistics courses for tasks such as
data visualization, hypothesis testing, regression analysis, and probability distributions.
2. Data Science: R is a popular choice for courses in data science due to its extensive libraries
for data manipulation, exploratory data analysis, machine learning, and predictive modeling.
3. Econometrics: R is frequently used in econometrics courses for analyzing economic data,
estimating econometric models, and conducting statistical tests relevant to economic theory.
4. Biostatistics: R is extensively used in biostatistics courses for analyzing biological and health-
related data, conducting survival analysis, and fitting models to epidemiological data.
5. Psychology and Social Sciences: R is utilized in psychology and social science courses for
statistical analysis of surveys, experiments, and observational studies.
6. Business Analytics: R is valuable for courses in business analytics for tasks such as
forecasting, customer segmentation, and market basket analysis.
7. Environmental Science: R is used in environmental science courses for analyzing
environmental data, spatial analysis, and modeling ecological systems.
8. Quantitative Finance: R is employed in courses related to quantitative finance for tasks such
as financial modeling, risk analysis, and time series analysis of financial data.
9. Operations Research: R is used in operations research courses for optimization, simulation,
and decision analysis.
10. Machine Learning: R is used in courses covering machine learning algorithms, including
implementations of algorithms such as decision trees, random forests, and support vector
machines.
Overall, R programming is an essential tool for anyone working with data analysis,
statistics, and data science across various academic disciplines and professional
fields.
Any more questions directly ping me on whats app +918583070415 or email
souravsirclasses@gmail.com