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R Programming 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, r²
  • 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