## Statistics using R programming Assignment Help

In recent times, the application of R Programming in statistics has become widespread especially in the area of Probability, Regression Analysis, Testing of Hypothesis, Sampling etc. Our Statistics tutors being proficient in these multiple areas can provide you the quality and timely solutions in the form of Statistics using R homework help, assignment help, term paper help and exam preparation help. Our assignment/homework help tutors hold PhD degrees or Masters and are well versed with any referencing style, be it Harvard or APA or any other. Our experts are available 24×7 to help high school/ college/ university students with their assignments. Along with College Statistics Homework Help and University Statistics Homework Help we also provide Online Statistics using R Programming tutoring for high school, undergraduate, graduate and PhD level students

Following is the list of comprehensive topics in which we offer the quality solutions:

- 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 correlations
- 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