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Topics like R data analysis , R statistical assignments ,R models generation & the assignment help on these topics is really helpful if you are struggling with the complex statistical programming problems.
- 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
- 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