Free Printable WorksheetMathAlgebra IIMath CurriculumS-ID.6a

ALG2.213 Linear Regression and Residuals Worksheet

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Worksheet Overview & Skills Covered

Core academic competencies and curriculum alignment

This Algebra II Math worksheet provides structured practice on ALG2.213 Linear Regression and Residuals. Students are guided through targeted exercises that build foundational understanding, computational accuracy, and critical thinking skills.

ALG2.213 Linear Regression and Residuals - Primary standard selected for prompt fields in this prompt cell: CCSS S-ID.6a. Skill statement in this prompt cell: Select the best-fitting a linear select a model to, select the correct interpretation of slope and intercept, and analyze residuals to evaluate select a model to fit and appropriateness. Associated standards listed with this skill in this prompt cell: CCSS S-ID.6a,b; S-ID.7–8; CA CCSSM S-ID.6a,b; S-ID.7–8; MA 2017 S-ID.6a,b; S-ID.7–8; NY NGLS S-ID.6a,b; S-ID.7–8; TX TEKS Algebra II: §111.40. Use this prompt-provided wording as the supporting standard description for this workflow.

Whether you are an educator preparing classroom handouts, a tutor reinforcing weekly topics, or a parent supporting homeschool learning, this printable resource gives students the deliberate practice required to achieve subject fluency and master state standards.

Grade

Algebra II

Subject

Math

Domain

Math Curriculum

Formats

PDF & DOCX

Sample Practice Problems

Preview the types of problems and questions covered in this worksheet

Below are sample questions representative of the exercises included on this worksheet and related ALG2.213 Linear Regression and Residuals skill assessments:

Problem 1Difficulty: Mastery
The table shows x: 2, 4, 6, 8, 10 and y: 20, 33, 45, 52, 55. A linear fit gives residuals -3, 1, 4, 1, -3 in x-order. Which conclusion is best?
AThe residuals show a curved pattern, so a nonlinear model may fit better.
BThe residuals are all zero, so the line fits perfectly.
CThe residuals prove the variables have no association.
DThe residuals show that every prediction is too low.
Solution & Explanation:The residuals are negative at both ends and positive in the middle, which is a pattern. That suggests the straight line may be missing curvature.
Problem 2Difficulty: Mastery
Use the accompanying residual plot. What issue should be noted before relying on the linear model?
AThe residual spread increases as x increases, so prediction error is not equally consistent across the range.
BThe residuals are all exactly 0, so the model is perfect.
CThe residuals prove that x causes y.
DThe plot shows that the slope must be negative.
Solution & Explanation:A fan shape means residual variability changes with x. That can make predictions less reliable in some parts of the range.
Problem 3Difficulty: Mastery
A linear model has correlation r = 0.78. What percent of the variation in y is not explained by the model? Round to two decimal places.
Solution & Explanation:R2 = 0.782 = 0.6084, so 60.84% is explained. The unexplained percent is 100 - 60.84 = 39.16.

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Frequently Asked Questions

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QWhat grade level and standards does this worksheet align with?

This worksheet is designed specifically for Algebra II Math students. It aligns with educational standard S-ID.6a (Primary standard selected for prompt fields in this prompt cell: CCSS S-ID.6a. Skill statement in this prompt cell: Select the best-fitting a linear select a model to, select the correct interpretation of slope and intercept, and analyze residuals to evaluate select a model to fit and appropriateness. Associated standards listed with this skill in this prompt cell: CCSS S-ID.6a,b; S-ID.7–8; CA CCSSM S-ID.6a,b; S-ID.7–8; MA 2017 S-ID.6a,b; S-ID.7–8; NY NGLS S-ID.6a,b; S-ID.7–8; TX TEKS Algebra II: §111.40. Use this prompt-provided wording as the supporting standard description for this workflow.).

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