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AP® Statistics
Master Review Sessions with Mr. Sofy
GROUP SESSIONS
Get exam-ready with our comprehensive AP Statistics free-response prep course! In just six focused sessions, you'll learn essential topics, including Describing Data, Regression, Sampling & Experimentation, Probability, and Statistical Inference (covered in two parts). Through real AP-style questions, expert guidance, and hands-on problem-solving, you'll gain the confidence and skills needed to tackle even the toughest free-response problems. Don't leave your AP score to chance! Join us and take your stats game to the next level! Helpful mnemonics will also be provided!
In Session 1, Students learn how to summarize and interpret numerical information using various representations. They explore measures of center (mean, median, mode), measures of spread (range, interquartile range, standard deviation), and data distribution shapes (skewed, symmetric, uniform). They analyze tables and graphs such as histograms, boxplots, dot plots, and scatterplots to identify trends, outliers, and patterns. Additionally, students learn how to describe data using contextual language, compare distributions, and recognize misleading visual representations. This lesson builds foundational skills for making data-driven decisions and prepares students for deeper statistical analysis.
75 US dollars
In Session 2, Students learn how to model relationships between two quantitative variables using a least-squares regression line (LSRL). They explore how to interpret the slope and y-intercept in context, assess the strength and direction of a relationship using the correlation coefficient (r), and evaluate how well the model fits the data using the coefficient of determination (r²). Students also analyze residuals to determine if a linear model is appropriate and learn how to make predictions while considering the risks of extrapolation. Through real-world examples, such as predicting exam scores from study hours or analyzing trends in economics, students develop critical skills in data analysis and decision-making.
75 US dollars
In Session 3, Students learn how to design studies that collect reliable and unbiased data. They explore different sampling methods, including simple random sampling, stratified sampling, cluster sampling, and systematic sampling, while discussing the importance of avoiding bias. Students also learn how to distinguish between observational studies and experiments, identify experimental design principles (such as control groups, randomization, replication, and blinding), and understand how to establish cause-and-effect relationships. Additionally, they analyze potential sources of bias, such as undercoverage, nonresponse, and response bias, and discuss how well a sample represents a larger population. This lesson builds a foundation for conducting valid statistical studies and interpreting real-world data.
75 US dollars
In Session 4, Students learn how to quantify uncertainty and determine the likelihood of events occurring. They explore fundamental probability rules, including the addition rule for mutually exclusive events and the multiplication rule for independent events. Students also learn about conditional probability and how to use two-way tables, Venn diagrams, and tree diagrams to visualize probability scenarios. Concepts such as complementary events, expected value, and the Law of Large Numbers help students understand real-world applications, from predicting outcomes in games to assessing risk in decision-making. By applying these principles, students develop a strong foundation for making informed statistical inferences.
75 US dollars
In Session 5, Students learn how to estimate an unknown population parameter based on sample data. They explore how to construct and interpret confidence intervals for population means and proportions, understanding the role of margin of error and critical values (such as 𝑧∗ or 𝑡∗). Students also learn how sample size, variability, and confidence level affect the width of a confidence interval. The lesson emphasizes the correct interpretation of confidence intervals and confidence levels while avoiding common misconceptions. Real-world applications, such as polling and scientific research, help students see the importance of confidence intervals in making data-driven decisions.
75 US dollars
In Session 6, Students learn how to determine whether sample data provides enough evidence to support or reject a claim about a population. They explore the null hypothesis (𝐻0), which represents no effect or no difference, and the alternative hypothesis (𝐻𝑎), which reflects the claim being tested. Students calculate test statistics (such as z-scores or t-scores), determine p-values, and compare them to a given significance level (𝛼) to make decisions. They also learn about Type I and Type II errors, as well as the importance of statistical power. Real-world examples, such as drug effectiveness studies or quality control tests, help students understand how hypothesis testing is used to make informed decisions based on data.
75 US dollars