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Item 310 of 426 · Year 12: Software Automation, 17 of 35

Linear regression

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Definition

Fits a straight line through data to predict a numeric value from one or more inputs.

Example: Predict house price from floor area.

Found in: syllabus, course specifications, sample exam and familiarisation guidelines, school notes.

Short version to memorise (key terms bank)

Predicts a continuous numeric value on a straight line.

Where you'll use it in the test

Syllabus area: Y12 Software Automation.

Question types it can appear in

Short answer: Compare linear regression and KNN (sample Q23b)
Multiple choice: Pick the option that matches the definition or example.
Python: Write or complete it in the online Python tool.

Where it has appeared in the paper

Software Automation (Part B). Part letters follow each paper’s own layout.

How to use it

A model sentence

General advice, built from the definition and example above

“Fits a straight line through data to predict a numeric value from one or more inputs.” Then apply it: Predict house price from floor area.

In a short answer, give the definition in your own words first, then tie it to the situation in the question. Do not copy the example above unless it fits the scenario.

Command words used with it

From the linked past questions

Compare: Show how things are similar or different. (Sample exam Q23b (3 marks): Linear regression v K-nearest neighbour)

Official worked answer

Sample exam Q23b (3 marks): Linear regression v K-nearest neighbour (3 marks): official criteria and sample answer

Criteria

3: Compares linear regression and KNN. 2: Outlines features of one or both. 1: Some relevant information.

Sample answer

Linear regression and K-nearest neighbours (KNN) are both supervised learning algorithms that make predictions from labelled data. The key difference is how they handle data: linear regression assumes a linear relationship between input features and the target variable, while KNN makes predictions based on the closest 'K' training examples, allowing it to handle more complex, non-linear patterns. However, KNN can be more computationally expensive than linear regression and is usually slower.

Open the question page

Go deeper

Flashcard: Linear regression in Software Automation (Year 12) · study that set
Memorise: Decision trees
Key terms bank entry: Linear regression
Master answer: sample-q23b (3 marks, short answer (compare, max 75 words)) (Master model answers: Software Automation)
Notes: Describe types of algorithms associated with ML, including linear regression, logistic… (Software Automation (Year 12))
Notes: Design, develop and apply ML regression models using an OOP to predict numeric values,… (Software Automation (Year 12))
Full term page: every question, note and marking comment for Linear regression

Check yourself

Explain “Linear regression” in one sentence, give an example, and name a question type that could test it.

Reveal the answer

Fits a straight line through data to predict a numeric value from one or more inputs.

Example: Predict house price from floor area.

Could be tested as: Short answer, Multiple choice, Python.

Quick multiple choice

Which problem is BEST solved with linear regression?

Predicting the winner of a two-party election
Deciding whether an email is spam
Predicting the price of a house from its floor area
Deciding whether a student is admitted to a course

Show the answer

Linear regression predicts a continuous numeric value. The other three are yes/no classifications (logistic regression). 2025 Q12 tested this.

Source: original (written for this study pack; not a NESA question). Open it in the multiple-choice bank.