Practical Biostatistics with R

Install the core R software and the R Studio Desktop IDE to enable using R, with platform-specific download links for Linux, Mac OS, and Windows.

Import data in R using a bookstore.csv example with read.csv, inspect structure with str, and export data using write.csv, including Excel import via the Open Excel library.

Explore interval scale concepts by examining measurements expressed in numbers, with arbitrary starting points and no absolute zero, using temperature, IQ scores, and pH as examples.

Explain how pie charts, a circular graph with slices, depict the frequency of categorical data and 100 percent total, using hypertension 37.5 percent and depression 6.2 percent as an example.

Use scatter plots to compare two continuous variables and explore their relationship, such as height and weight.

Explore how a frequency polygon visualizes the distribution of a continuous variable, like a histogram, and how it can be derived from a histogram by joining plotted points.

Identify the mode as the most frequently occurring value in a data set. Recognize bimodal, trimodal, and multimodal patterns when multiple values share the highest frequency.

Deciles partition a data set into ten parts. Identify q1 as the second decile, the median as the fifth, and q3 as the seventh decile, highlighting high and low observations.

Explore measures of dispersion and how observations deviate from the center. Understand the range as the difference between the highest and lowest values and its sensitivity to extreme values.

The variance measures how far observations spread from the mean, with the standard deviation as its square root. It is sensitive to outliers, units, and normality assumptions.

Explore probability as a tool for inference in biostatistics, quantify uncertainty, and assess statistical significance while understanding how trial factors influence vaccine safety and effectiveness.

Define probability as the measure of the likelihood that an event occurs, ranging from 0 to 1. Differentiate classical, empirical, and subjective probability with examples like dice, coins, and weather.

Explore classical and empirical probability through vaccine trials in R, using simulations, sample spaces, and probability rules (multiplication, addition, conditional) with ggplot visualizations.

Explore random variables, their discrete and continuous forms, and how probability distributions describe outcomes with the probability mass function, probability density function, cumulative distribution function, and the expected value (mean).

explore binomial distribution for discrete outcomes, modeling the number of successes in n independent trials with two outcomes and constant probability p, and understand its mean and variability.

learn how the Poisson distribution models counts in a fixed interval using lambda as the average number of occurrences, especially for rare events, with mean equal to variance.