Rguroo’s Analytics toolbox makes available major statistical analysis methods including detailed diagnostics and graphs, using R. Methods include construction of confidence intervals and performing tests of hypotheses for means and proportions, linear regression modeling, data tabulation and analysis of contingency tables. For each analysis, elementary methods are made available within their corresponding “Basics” menu and advanced methodologies are available on the “Details” menu. Outputs of the analyses are customizable and professionally presentable.

##### Mean Inference

Perform mean inference using either raw data or summary statistics.

Construct confidence intervals for one population mean or difference of two population means, using t, z, or bootstrap methods.

Conduct tests of hypothesis for one population mean or difference of two population means, using t, z, bootstrap, and permutation methods.

Construct confidence intervals and perform tests of hypothesis for paired data.

Compute power at specified values of an alternative.

Use the Report Layout Generator to customize the output, including presentable tables and graphs that illustrate p-values, critical region, and power.

Output your report in Word, HTML, and pdf formats.

##### Proportion Inference

Construct confidence intervals for a single population proportions using methods of large sample z, exact binomial, Agresti-Coull, and Wilson score.

Conduct tests of hypotheses for single proportion using exact binomial, large sample z, and score tests.

Construct confidence intervals for difference of proportions, using large sample z and Wilson score methods.

Conduct test of hypotheses about the difference between two proportions using large sample z, chi-squared, and Fisher exact test.

Use the Report Layout Generator to customize the output, including presentable tables and graphs that illustrate p-values and critical region.

Output your report in Word, HTML, and pdf formats.

##### Regression Analysis

Easy to use graphical interface to specify regression models, including main effects and interactions.

Obtain tables including data summary, parameter estimates, confidence interval for parameter estimates, ANOVA, and sequential ANOVA.

Obtain graphs including response versus predictor, residual versus fit (ordinary, standardized and Studentized), Q-Q plot of residuals, added variable plots, and influence index and regression influence plots.

Request many diagnostic and inferential measures including Cook’s distance, Leverage, and prediction and confidence intervals.

Customize your output including placing tables and graphs in any order you desire.

##### Tabulation and Analysis of Contingency Tables

Tabulate data for up-to three factors, obtaining joint, marginal, and all conditional aggregates and distributions.

Perform tests of independence.

Perform goodness-of-fit tests.