2021 Live Review 6 | AP Statistics | Rubrics for Free-Response Success on the AP Statistics Exam

In this introductory paragraph, Darren Starns welcomes AP Statistics students to the sixth live daily review session. He outlines the session's focus on rubrics for free response success and references tools discussed in the previous session by Mr. Wilcox. The presenter shares logistical reminders about available materials, including a handout, formula sheet, and tables, all accessible via a provided URL. Additionally, he mentions the alignment of AP Statistics live review questions with AP daily videos in a Google Sheet. The importance of a graphing calculator for the AP exam is emphasized, and the session's end will include a feedback solicitation via Google form. The response to a previous session's feedback about using a calculator for free response questions is also addressed, explaining the approach for probability calculations and inference calculations.

This paragraph delves into the specifics of free response questions, using rubrics or scoring guidelines to understand how to achieve full credit. The session plan includes working through three free response questions from previous AP exams, each highlighting different topics: exploring data, collecting data, and inference. The aim is to review scoring guidelines and common errors. The paragraph also discusses the importance of simplifying challenging content, sharing strategies for exam success, and building confidence. A detailed example of a free response question about the Great Plains Railroad's fuel consumption is presented, including a student response and how it aligns with the scoring guideline for model appropriateness.

The paragraph continues the discussion on scoring guidelines, examining a student's response to a free response question about the relationship between fuel consumption and the number of rail cars. It covers how to score the response based on the clarity of reasoning, the correct use of the slope for calculating changes in fuel cost, and the interpretation of r-squared in the context of the problem. The scoring criteria are explained, emphasizing the need for clarity and accuracy in student responses. The paragraph also touches on the importance of aiming for optimal responses and the consequences of not meeting those standards.

This paragraph focuses on the analysis of a regression equation's output and the concept of extrapolation. It discusses the interpretation of r-squared, the standard deviation of residuals, and the standard error of the slope. The paragraph presents a scenario where a student is asked whether it would be reasonable to use the fitted regression equation to predict fuel consumption for a train with 65 rail cars, far beyond the observed data range. The student's response, which advises against extrapolation, is evaluated against the scoring guideline, highlighting the importance of understanding the domain of the model.

The paragraph presents a study design for evaluating the effectiveness of dietary supplements in promoting joint and hip health in dogs. It discusses the advantages of adding a control group to the study and the importance of randomization in assigning dogs to treatment groups. The paragraph also addresses the concept of blocking in experimental design, comparing the use of veterinary clinics versus dog breeds as blocking variables. The scoring guidelines for each part of the question are explained, emphasizing the need for context in responses and the importance of understanding experimental design principles.

This paragraph continues the discussion on experimental design, focusing on the correct method of randomization and the decision-making process for choosing a blocking variable. It presents a student's response to assigning dogs to treatment groups using a die roll, which is evaluated as essentially correct. The paragraph also discusses the choice between blocking on clinics and breed of dog, highlighting the need to consider the variable with a stronger relationship to the response variable. The scoring for each part of the question is detailed, showing the consequences of not fully addressing the context or the variability associated with each blocking option.

The paragraph describes a medical study investigating the effects of low-dose aspirin on colon cancer risk. It outlines the study's design, where volunteers are randomly assigned to take either aspirin or a placebo. The student's response to the study's analysis includes stating hypotheses, identifying the correct test procedure, and checking conditions for a two-sample z-test for proportions. The paragraph explains the scoring guidelines for each step, emphasizing the importance of correct parameter definition, understanding random assignment, and checking for approximate normality through expected counts.

This paragraph examines the mechanics of performing a z-test for a difference in proportions, including the calculation of the test statistic and p-value. It presents a student's attempt to calculate these values and the importance of linking the p-value to the given alpha level to make a statistical conclusion. The paragraph discusses the scoring guidelines for the mechanics and conclusion steps, noting that minor errors may still result in an essentially correct score if the overall response is accurate and contextually appropriate.

The paragraph concludes the discussion on free response questions, summarizing the importance of using content learned throughout the year to answer questions about linear model appropriateness, interpreting regression output, avoiding extrapolation, and understanding experimental design vocabulary. It emphasizes the need to answer in context, address both options when choosing between blocking variables, and follow the inference process for significance tests or confidence intervals. The paragraph invites feedback from students and previews upcoming sessions, highlighting the importance of the investigative task and offering top tips for exam success.