This document is a period 2 reviewer for the course MAT 152 Mathematics in the Modern World. It covers data classification, data presentation and graphs, ethical use of data, measures of central tendency and variability, fundamental counting principle, probability, z-scores, normal distribution, Pearson correlation, and linear regression.
This document is a period 2 reviewer for the college course MAT 152 Mathematics in the Modern World. It is intended for students preparing for an exam on topics including data classification, data presentation, probability, and statistics.
The first section covers data classification, distinguishing categorical data (groups or labels), discrete data (obtained by counting), and continuous data (obtained by measurement). The next section describes data presentation methods: tabular presentation, line graphs, pie graphs, histograms, and scatter plots, with guidance on when to use each.
The ethical use and interpretation of data section discusses data integrity, transparency, privacy and consent, evaluating data sources, and handling outliers. Measures of central tendency covered are mean, weighted mean, median, and mode, with formulas and memory tips.
Measures of variability include range, sample variance, and standard deviation, with step-by-step calculation instructions. The fundamental counting principle and tree diagrams are explained for counting possible outcomes, followed by basic probability calculation.
Z-scores and the normal distribution are covered, including how to interpret positive, negative, and zero z-scores. Pearson correlation coefficient r is explained in terms of direction and strength, with examples of positive and negative correlations and a note that correlation does not imply causation.
The final section covers linear regression, including the equation y = a + bx, interpretation of intercept and slope, and using the equation for prediction. A master review table and study checklist summarize key concepts and formulas.