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**R ProgrammingÂ **Assignment Help

R is programming language which is used for statistical computing and graphics. R programming was developed and researched at Bell laboratories. R programming is used for a variety of statistical assignments. R programming is used for traditional statistical tests, techniques used for representing the data in forms of graphs and charts, time series analysis, clustering and classification of data. A wide variety of tools and utilities are available to assist R programming.

R Programming assignments can be very complex and time consuming for students. We has a pool of programmers who are having huge command over the R programming as they are working on this language for a very long time. Thus our experts are higly capable in solving all your assignments, project reports, research work and analysis.

Sub topics in R programming assignments are:

- ANOVA for regression: analysis of variance calculations for simple and multiple regression, f statistics
- Binomial distributions: counts, proportions, normal approximation
- Chi-square goodness of fit test: chi-square test statistics, tests for discrete and continuous distributions
- Censored and truncated regression
- Canonical correlation analysis
- Conditional probability: probabilities of intersections of events, bayes's formula
- Categorical data: two-way tables, bar graphs, segmented bar graphs
- Confidence intervals: inference about population mean, z and t critical values
- Correlation: correlation coefficient, rÂ˛
- Comparison of two means: confidence intervals and significance tests, z and t statistics, pooled t procedure
- Experiments and sampling
- Experimental design: experimentation, control, randomization, replication
- Graphical displays: stem plots, histograms, box plots, scatter plots
- Hypothesis tests and confidence interval
- Inference for categorical data: confidence intervals and significance tests for a single proportion, comparison of two proportions
- Interval regression
- Inference in linear regression: confidence intervals for intercept and slope, significance tests, mean response and prediction intervals.
- Multivariate analysis
- Multiple linear regression: confidence intervals, tests of significance, squared multiple correlation
- Multinomial logistic regression
- Mean and variance of random variables: definitions, properties
- Mixed effect models
- Mixed effects logistic regression models
- Negative binomial regression
- Numerical summaries: mean, median, quartiles, variance, standard deviation
- Normal distributions: assessing normality, normal probability plots
- Linear regression and correlation
- Linear regression: least-squares, residuals, outliers and influential observations, extrapolation
- Logistic regression
- Probability
- Probability models: components of probability models, basic rules of probability
- Probit regression
- Poisson regression
- Robust regression
- Random variables: discrete, continuous, density functions
- Sampling: simple, stratified, and multistage random sampling
- Sampling in statistical inference: sampling distributions, bias, variability
- Tests of significance: null and alternative hypotheses for population mean, one-sided and two-sided z and t tests, levels of significance, matched pair analysis
- To bit regression
- Truncated regression
- Two-way tables and the chi-square test: categorical data analysis for two variables, tests of association
- Zero-inflated Poisson regression
- Zero-inflated negative binomial regression
- Zero-truncated Poisson
- Zero-truncated negative binomial