Data Analysis and Statistical Modeling in R

Explore built-in R math functions, including exponential, logarithms, sqrt, trigonometry, abs, min/max, and which.min/which.max, plus cumulative sum and product for vector and matrix analysis.

Learn basic statistical concepts in this course, including mean, median, mode, range, standard deviation, variance, skewness, and kurtosis, with examples and outlier intuition.

Explore fundamental data distributions, from discrete and continuous data to density, PMF, and CDF, with normal, binomial, and chi-square examples, plus the central limit theorem and sample size implications.

Explore density, cdf, quantile, and random number functions for normal, binomial, and chi-square distributions, including continuous vs discrete density, quintiles, median, and practical applications.

Explore how to plot normal and binomial distributions in R, adjust mean and standard deviation, compare continuous and discrete densities, and interpret density and cumulative functions.

Use bar plots to visualize categorical data in R, illustrated with weight loss and chickweed datasets, including colors and orientation; convert data to frequencies when plotting categories.

Learn to create bar plots for grouped data by computing group means, reshaping with transpose, assigning group names, and visualizing color-coded bars to compare feeds.

discover how to create pie charts for categorical data in r, customize visuals with graphical parameters like margins and layout, and practice with the orange dataset.

Demonstrates finishing and enhancing pie charts in R, including labels, colors, start angle, and arranging multiple pies, then contrasts with bar charts as clearer alternatives.

Explore histograms in R using real data, customizing breaks, applying five-number summaries, and overlaying normal curves and kernel density lines for orange circumference and Lake Huron levels.

Generate a basic histogram of urban population in the US, adjust breaks and colors, and overlay a normal curve with mean and standard deviation for visual analysis.

Learn to create and customize box plots in R to compare distributions in the iris data using the five-number summary and detect outliers across species.

Explore box plots for comparing feed groups in the Checkmates dataset, showing how boxes reveal growth rate differences across feeds and why box plots outperform bar plots.

Explore scatter plots in R with base plotting and the car package, visualizing before-after data, solving encoding issues, and adding regression lines with locally weighted smoothing and boxplots.

Master matplot in r to plot corresponding columns from two matrices, customize with type, pch, and colors, add a legend, and arrange multiple plots with mfrow using iris data.

Explore parametric and nonparametric tests, assess significance with p-values and alpha levels, and apply hypothesis testing with null/alternative hypotheses, z-scores, and confidence intervals.

Complete the data distribution and simulation section by applying standard normal theory to compute 90–99% confidence intervals using z-scores, and explore cumulative and inverse distribution functions with real-world examples.

Apply the single proportional test in R to compare proportions with a null of 0.5. Interpret p-values and confidence intervals, and choose one-tailed or two-tailed tests.

Compare two proportions to test whether two students have different university acceptance rates using a two-sided proportional test, interpreting p-values and degrees of freedom.

T-tests, including one-sample, independent two-sample, and paired tests, compare means and assess whether observed differences arise by chance via p-values, under the null hypothesis and normal distribution.

Apply a one-sample t-test to compare a sample mean to a null value, interpret p-values and t-values, and determine whether the true mean differs from the hypothesized population mean.

Perform two-sample independent t-tests to compare means across groups, such as sex (male vs female) and office location, using p-values and confidence intervals to assess null versus alternative hypotheses.

uses the paired t-test to compare before and after measurements, test null vs alternative hypotheses, interpret p-values and one- and two-tailed tests, and assess practical significance of a teaching intervention.

Compare group means using the F-test and ANOVA to assess variance between and within groups, and identify which means differ with Tukey HSD.

Perform one-way anova on sepal length across iris species, interpret the f-statistic and p-values, and apply tukey hsd to reveal which species means differ.

Apply the chi square one sample goodness of fit test to compare observed counts with expected distributions. Interpret chi square values and p values to determine fit using real examples.

Apply the chi-square test for independence in R to contingency tables, comparing drug alpha and drug beta, and men versus women opinions, using observed and expected counts and p-values.

Explore how correlations measure the strength of the relationship between two variables, using Pearson for normally distributed data and Spearman for ranked data.

Practice with the course source files and exercises to master data analysis and statistical modeling in r. Share knowledge to help others grow.