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R Programming Assignment Help

We provide 24/7 support for R Programming Assignment help & R Programming homework help. Our R Programming Online tutors are available online to provide online support for complex R Programming assignments & homework to deliver with in the deadline. R Programming guidance is available by experienced tutors round the clock.

Topics related to R Programming Assignment help

  • produce illustrative data plots, out statistical tests, linear or generalized linear models
  • flow control and functions, Finance Assignment help: data import/export and data frames
  • Graphical procedures, Hypothesis tests, Linear models like ANOVA, linear regression and mixed models.
  • Statistics for Biologists, software R, elementary programs, statistical models implemented, applied R
  • import, manage and structure data files, write simple program scripts for data analysis
  • Simple computer programming: expressions, variables, data types, logical conditions,
  • Introduction to R, survey data sets with R , An R and S-Plus Companion to Applied Regression
  • Data Analysis and Graphics Using R, Linear Models with R, Using R for Data Analysis and Graphics
  • Practical Regression and ANOVA using R
  • Manual computation, Data vector, functions: mean(), sd(), (pqrd)qnorm()
  • Finding confidence intervals, Finding p-values, Issues with data
  • Using data stored in data frames (attach()/detach(), with())
  • Missing values, Cleaning up data, EDA graphs
  • Histogram()
  • Boxplot()
  • Densityplot() and qqnorm()
  • The t.test() function
  • P-values, Confidence intervals, The power of a t test
  • GUI's, Rcmdr, PMG
  • Tests with two data vectors x, and y, Two independed samples no equal variance assumption
  • Two independed samples assuming equal variance, Matched samples
  • Data stored using a factor to label one of two groups; x ~ f;
  • Boxplots for displaying more than two samples, The chisq.tests, Test of homogeneity or independence
  • Wilkinson-Rogers notation: y ~ x, y ~ x linear regression
  • Scatterplots with regression lines, Reading the output of lm()
  • Confidence intervals for beta_0, beta_1, Tests on beta_0, beta_1
  • Identifying points in a plot, Diagnostic plots
  • boostrapping, sample() function
  • bootstrap sample, Forming several bootstrap samples
  • Aside for loops vs. matrices and speed, Using the bootstrap
  • permuation tests, permutation test simulation

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