24 lessons across 5 weeks, 57 verified past-year questions. Free preview below — sign in for the full interactive lessons, AI tutor, and mock exams.
Week 0
- Introduction to Statistics — An overview of statistics as the art of collecting, analyzing, and drawing conclusions from data.
- Descriptive Statistics — Techniques for organizing and summarizing categorical and numerical data using graphical and numerical measures.
- Probability Theory — Using counting principles and set theory to quantify uncertainty in random experiments.
Week 1
- Foundations of Statistics — Introduction to the branches of statistics and the fundamental roles of population, samples, and probability in drawing conclusions from data.
- Understanding and Organizing Data — Defining data, explaining the necessity of structured datasets, and identifying key components like variables and observations.
- Data Classification — Classifying variables into categorical vs. numerical types and distinguishing between cross-sectional and time-series data.
- Scales of Measurement — Understanding the four measurement scales—nominal, ordinal, interval, and ratio—and their implications for statistical analysis.
Week 2
- Frequency Distribution — Understanding frequency and relative frequency as methods to summarize categorical data sets.
- Graphical Displays of Categorical Data — Using bar charts and pie charts to visualize categorical data distributions.
- Best Practices for Graphing — Guidelines for effective visualization, including labelling, handling multiple categories, and avoiding misleading graph designs.
- Descriptive Measures: Mode and Median — Using central tendency measures like mode for nominal/ordinal data and median for ordinal data to summarize categorical variables.
- End of Week Lab — Comprehensive Google Sheets lab covering all week 2 concepts.
Week 3
- Organizing Numerical Data — Methods for summarizing discrete and continuous numerical data using frequency tables, including grouping into class intervals.
- Mean as a Measure of Central Tendency — Definition, computation for ungrouped and grouped data, sensitivity to outliers, and effects of linear transformations.
- Median and Mode — Defining median and mode as central tendency measures, their computation, and their behavior under linear transformations.
- Measures of Dispersion — Quantitative measures of variability including range, variance, and standard deviation, and how they respond to data scaling.
- Percentiles and Quartiles — Using percentiles, quartiles, and the five-number summary to describe data spread and identify interquartile range (IQR).
- End of Week Lab — Comprehensive spreadsheet lab covering all week 3 concepts.
Week 4
- Bivariate Categorical Association — Using contingency tables and stacked bar charts to determine if two categorical variables are associated.
- Scatter Plots for Numerical Association — Visualizing the relationship between two numerical variables using scatter plots to identify trends, variation, and outliers.
- Covariance and Correlation — Quantifying the strength and direction of linear associations using covariance and the unitless Pearson correlation coefficient.
- Linear Regression and Goodness of Fit — Summarizing linear relationships with a best-fit line and using R-squared to evaluate model fit.
- Point-Biserial Correlation — Measuring association between a numerical variable and a dichotomous categorical variable using group means and standard deviations.
- End of Week Lab — Comprehensive spreadsheet lab covering all week 4 concepts.
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