AP Statistics: 8-Week Exam Review - Saturday Group equips students with essential statistical tools and techniques, focusing on data analysis, probability, and inference. This intensive review course prepares students for the AP Statistics exam by building a strong foundation and progressing to complex problem-solving, enabling them to confidently tackle exam questions and real-world statistical challenges. By the end of this course, students will master key concepts and methodologies required for success on the AP Statistics exam.

Descriptive Statistics and Data Visualization

• Understanding data distribution — skewness (left, right, symmetrical), modality (unimodal, bimodal, multimodal)
• Visualizing data — histograms, scatter plots to identify patterns and outliers
• Key metrics — shape, center, spread

Two-Variable Data Analysis

• Identifying response and explanatory variables to understand relationships
• Creating and interpreting scatter plots to assess the direction and strength of relationships
• Understanding correlation — calculation using calculators, properties, and interpretations

Data Collection: Sampling and Surveys

• Key vocabulary — population, parameter, sample, census
• Sampling methods — simple random, stratified, cluster sampling techniques
• Potential biases in sampling and survey design and how to mitigate them

Experimental Design

• Principles of experimental design — control, randomization, replication
• Types of experimental designs — completely randomized, randomized block designs
• Understanding the role of control groups and blinding in experiments

Probability and Random Variables

• Interpreting probability as long-run relative frequency
• Random processes — generating outcomes determined purely by chance
• Basic probability rules — addition, multiplication, and complement rules
• Conditional probability and independence — understanding and calculating conditional probabilities
• Discrete and continuous random variables — probability distributions, expected value, and variance

Inference for Categorical Data: Proportions

• Confidence intervals — basics, calculation, and interpretation in context
• Point estimators — identifying and calculating point estimates
• Estimating population proportions — sample size determination
• Estimating differences in proportions — hypothesis testing