Exam 01 review

Prof. Maria Tackett

Feb 12, 2026

Announcements

  • Lab 03 due TODAY at 11:59pm

  • Monday’s lab: Exam 01 review (will turn in during class)

  • Exam 01: Tuesday, February 17 (in-class) + take-home due February 19

Exam 01

  • In-class: 75 minutes during February 17 lecture

  • Take-home: due February 19 at 5pm (no lecture on February 19)

  • If you miss any part of the exam for an excused absence (with academic dean’s note), your Exam 02 score will be counted twice

Outline of in-class portion

  • Closed-book, closed-note.

  • Potential question types:

    • Multiple choice
    • Short answer (no more than 3 sentences)
    • Interpretation
    • Evaluate a response.

  • Analysis output included in the exam

  • Just need a pen or pencil. No calculator permitted on exam.

Outline of take-home portion

  • Released: Tuesday, February 17 right after class

  • Due: Thursday, February 19 at 5pm

  • Similar in format to a lab/ HW

    • Will receive exam questions in README of GitHub repo

  • Push work to GitHub and submit a PDF of responses to Gradescope

Tips for studying

  • Review exercises in AEs and assignments, asking “why” as you review your process and reasoning

    • e.g., Why do we include “holding all else constant” in interpretations?

  • Focus on understanding not memorization

  • Explain concepts / process to others

  • Ask questions in office hours, lab, Ed Discussion

  • Review lecture recordings as needed

Content: Weeks 1 - 6

  • Exploratory data analysis

  • Fitting and interpreting linear regression models

  • Statistical models and regression equations

  • Simulation-based inference

  • Mathematical models for inference

  • Prediction

  • Different types of predictors

  • Model conditions and diagnostics

Data: Rail trail

The Pioneer Valley Planning Commission (PVPC) collected data for ninety days from April 5, 2005 to November 15, 2005. Data collectors set up a laser sensor, with breaks in the laser beam recording when a rail-trail user passed the data collection station.

We will use regression analysis to predict the number of trail users based on weather and other features describing the day.

We’ll use the following variables in this analysis:

  • volume estimated number of trail users that day (number of breaks recorded)

  • hightemp daily high temperature (in degrees Fahrenheit)

  • daytype one of “weekday” or “weekend”

Main effects vs. interactions

  • Consider the main effects model using hightemp and daytype to explain variability in volume. Which of the following is true for this model? Select all that apply.

  • Consider the interaction effects model using hightemp, daytype, and the interaction between the two variables to explain variability in volume. Which of the following is true for this model? Select all that apply.

🔗 https://forms.office.com/r/NqDLZ92x4d

Application exercise

📋 sta210-26.github.io/ae/ae-07-exam-01-review.html


Important

One person from each group: Put your group’s response on your slide: https://docs.google.com/presentation/d/15nF9ADDlQwiDiRG55TCMOkqQgCy6_JB5hjPKE_kZE90/edit?usp=sharing