# R Code homework help

## R Code homework help

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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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