R Code homework help

R Code homework help

We at StatisticsAssignmentExperts have established ourselves prominently in the space by providing high quality Help with R Code Assignments .You can upload your R Code Assignment/R Code Homework or R Code Project by clicking on ‘Submit Your Assignment’ tab .For any Help with R Code Assignment/ R Code Homework or R Code Project ,you can also e-mail it to  support@statisticsassignmentexperts.com .

Following is the list of comprehensive topics in which we offer Homework Help, Assignment Help, Exam Preparation Help and Online Tutoring:

  • Robust regression
  • Logistic regression
  • Exact logistic regression
  • Multinomial logistic regression
  • Ordinal logistic regression
  • Probit regression
  • Poisson regression
  • Negative binomial regression
  • Zero-inflated Poisson regression
  • Zero-inflated negative binomial regression
  • Zero-truncated Poisson
  • Zero-truncated negative binomial
  • Censored and truncated regression
  • To bit regression
  • Truncated regression
  • Interval regression
  • Multivariate analysis
  • Canonical correlation analysis
  • Mixed effect models
  • Mixed effects logistic regression models
  • Graphical displays: stem plots, histograms, box plots, scatter plots
  • Numerical summaries: mean, median, quartiles, variance, standard deviation
  • Normal distributions: assessing normality, normal probability plots
  • Categorical data: two-way tables, bar graphs, segmented bar graphs
  • Linear regression and correlation
  • Linear regression: least-squares, residuals, outliers and influential observations, extrapolation
  • Correlation: correlation coefficient, r²
  • Inference in linear regression: confidence intervals for intercept and slope, significance tests, mean response and prediction intervals.
  • Multiple linear regression: confidence intervals, tests of significance, squared multiple correlation
  • ANOVA for regression: analysis of variance calculations for simple and multiple regression,
    f statistics
  • Experiments and sampling
  • Experimental design: experimentation, control, randomization, replication
  • Sampling: simple, stratified, and multistage random sampling
  • Sampling in statistical inference: sampling distributions, bias, variability
  • Probability
  • Probability models: components of probability models, basic rules of probability
  • Conditional probability: probabilities of intersections of events, bayes’s formula
  • Random variables: discrete, continuous, density functions
  • Mean and variance of random variables: definitions, properties
  • Binomial distributions: counts, proportions, normal approximation
  • Sample means: mean, variance, distribution, central limit theorem
  • Hypothesis tests and confidence intervals
  • Confidence intervals: inference about population mean, z and t critical values
  • 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
  • Comparison of two means: confidence intervals and significance tests, z and t statistics, pooled t procedures
  • Inference for categorical data: confidence intervals and significance tests for a single proportion, comparison of two proportions
  • Chi-square goodness of fit test: chi-square test statistics, tests for discrete and continuous distributions
  • Two-way tables and the chi-square test: categorical data analysis for two variables, tests of association

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NOTE : In order to demonstrate the quality and comprehensiveness of our R Code Homework Solutions,following reference R Code Sample Assignment has been provided. This R Code Sample Assignment has been prepared by our R Code Experts just for your reference and they do not constitute to any of our previous Assignment/Homework solution deliveries.