Business Analytics with R: A Comprehensive Guide

Explore major analytics software, including SAS, R, SPSS, and Excel Miner, and show how they support data import, modeling, and results interpretation in business analytics.

Discover basic R functions and how to use R as an interpreter and calculator, perform square roots, logs, trigonometry with pi, and assign variables to vectors.

Discover how R recycles the shorter vector to match the longer one in arithmetic, with modulo and integer division, and learn subsetting with indices and value updates.

Import data into R from csv, semicolon, excel, and SPSS or SAS outputs using packages. Export to csv, tab-delimited, or Excel, and inspect data with head, names, and nrow.

Explore data manipulation in R including slicing, sorting, reshaping, and melting, and learn basics of statistics, data exploration, and visualization with bar, pie, box, and scatter plots.

Merge data frames in r by joining on ids to add columns from another dataset, exemplifying inner and outer joins with all is equal to false and true. Reshape2 converts between long and wide formats using id variables and gps to produce pivoted score and t2 columns.

Apply statistics as a method to collect, summarize, and infer data insights for business analytics. Use measures, visualizations, and regression to predict relations in large data with R and Python.

Explore how quantiles partition data into equal parts, defining percentile, decile, and quartile, and relate them to the mean, median, variance, and standard deviation.

Learn to compute cumulative frequency and frequency tables, and visualize data with bar diagrams, pie charts, histograms, and box plots in R for effective business analytics.

Explore random variables, probability distributions, and both continuous and discrete distributions, including normal and binomial, and learn how expected value guides real-world decisions with hands-on R demonstrations.

Explore binomial, Poisson, and normal distributions through practical ship arrivals and retail footfall examples; learn how to compute counts and probabilities in R for business analytics.

Apply the exponential distribution to estimate survival beyond one year for cancer patients, use the CDF to compute probability, and relate it to expected value and variance in normal distributions.

Compute the expected value as the weighted average of a random variable for discrete and continuous cases. Empirical mean is a case; a lottery example shows how it guides decisions.

Explore binomial distribution with a twelve-question test and five choices, calculating exactly four correct and at least four using probability mass and cumulative distributions; also introduce Poisson for bridge traffic.

explain kurtosis and skewness in small samples, and how the central limit theorem yields normality by averaging; compute mean, standard error of the mean, and related z-score probabilities.

Build the confidence interval for the mean from a roughly normal distribution, using mu ± 1.96 sigma for 95% confidence. Apply step-by-step calculations with alpha, z scores, and standard error.

Frame null and alternative hypotheses, distinguish type I and II errors, and use z or t statistics and p-values to assess drug efficacy in one or two-sided tests.

Demonstrate hypothesis testing with R through problem-based examples, calculating z statistics, critical values, and p-values at alpha 0.05. Learn to reject or not reject null hypotheses in practical scenarios.

Explore hypothesis testing for population proportions in R, using z tests and prop.test to interpret p-values with real examples like female proportion, rotten apples, and coin toss.

Identify level, trend, and seasonality as the systematic components of a time series, separating them from the random part. Use R to decompose, visualize, and apply ARIMA for forecasting.

Explore univariate ARIMA basics, including lag, differencing for stationarity, and ACF and PACF to determine p, d, and q, with notes on unit roots and GARCH for volatility.

Explore R visualization techniques for data exploration, from bar charts and heat maps to correlation maps and radar charts, using ggplot2, 3D plots, and maps.

Visualization reveals patterns missed by summaries, enabling fast data comparison. Learn to use bar plots, pie charts, and line graphs in R to compare distributions and track time series.

Overlay Dell and Intel stock data on a single plot using lines, differentiate with line types and colors, and add a legend.

Learn how Tukey's method supplements ANOVA for multiple comparisons, evaluating group means (A, B, C) with p-values and confidence intervals to decide which differences are significant.

Learn to perform factorial anova in R by modeling product as a function of variety and pesticide, interpret the AOV table, and use two k hsd for multiple comparisons.

Explore simple and multiple regression using least squares to predict house prices from size, with parameter estimation, residuals, and hypothesis testing. Estimate beta naught, beta one, and evaluate with p-values.

Explore how regression models assess relationships between variables using ANOVA, F statistics, and p values, interpret R-squared and adjusted R-squared, and build predictive equations with confidence intervals.