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Day 16: Functions, slopes, and limits

Functions and change become calculable through definitions, worked derivations, retrieval, and honest numerical evidence.

Mathematics & Calculus 480 active minutes (8 hours focused study)
01 120 min

Functions as mappings

Agent-Assisted Engineering Check

Prerequisites

Complete the previous activity and use the listed recovery source if blocked.

Learning Objective

Treat a function as a rule that maps each allowed input in its domain to exactly one output. Build a table and state the domain, range, units, and assumptions before using a formula.

Instructions

1. Read the definition first: a function f from a domain D assigns to every input x in D exactly one output f(x). Write D, the output set and the units (here plain numbers) before touching the formula.
2. Predict before calculating: which input gives the smallest output, and which pairs of inputs share an output?
3. Build the table column by column: x, then x^2, then x^2+1. Squaring before adding 1 is where most slips happen; (-2)^2 is 4, not -4.
4. Read the range from the algebra, not only from the table: x^2 ≥ 0 for every real x, so x^2+1 ≥ 1, and every y ≥ 1 is reached by x = sqrt(y-1).
5. Test the definition on the practice items: a relation stops being a function as soon as one input is paired with two different outputs.
6. Compare your prediction with the table and write one sentence about any mismatch.

Time plan (120 min): reading 30 + exercise 60 + check 30. Reading: OpenStax Calculus Vol. 1 Section 2.1 (definition, domain and range, vertical line test). Exercise: the table and practice items (b) and (c). Check: close the notes, redo the mastery item, and compare line by line with the reference answer.

Practice & Prediction Log

(a) For f(x)=x^2+1, make a table for x=-2,-1,0,1,2. State the domain and range over the real numbers. Then explain why one input cannot have two outputs. (b) For h(x)=sqrt(x-2), state the real domain and range, then compute h(2), h(3) and h(6). (c) Decide whether each is a function of x: the relation {(1,2), (2,3), (1,4)}, and the rule x = y^2.

Independent Mastery Criterion

Without assistance, define domain and range, produce the five correct outputs, and name the one-output-per-input assumption.

Authoritative Source Resources & Provenance

linking CC BY-NC-SA 4.0 attribution noncommercial share-alike

OpenStax Calculus Volume 1

Author: OpenStax, Rice University (Gilbert Strang, Edwin Herman, et al.) | Original Source Link ↗

Relevant Section / Range: Sections 2.1-2.3 (Functions, Limits, and the Derivative); Sections 3.1-3.2 (Derivative Rules)

Verified Version & Date: 3rd edition, verified 2026-10-30

Selection Rationale:

Open, systematic treatment of the limit definition and derivative rules with stable section locations.

Source Prerequisites:

Algebra, coordinate geometry, and function notation

Safe Fallback (Offline Resilience):

The authored derivations, tables, and exercises in these three Study Days are complete offline recovery material; use the linked section only for optional comparison.

Compared Candidate Sources & Selection Analysis (3)
MIT OpenCourseWare 18.01SC Single Variable Calculus ↗ compared_selected_for_visual_practice [CC BY-NC-SA 4.0]

Strong exercises and transcripts, but the noncommercial share-alike terms and course navigation make OpenStax the primary stable text.

Paul's Online Math Notes: Calculus I ↗ compared_rejected_for_reuse [All Rights Reserved]

Useful reference, but rights and redistribution terms are less suitable for embedded recovery material.

SciPy Numerical Differentiation Documentation ↗ compared_selected_for_numerics [BSD-3-Clause]

A primary API reference for numerical derivative step-size behavior; it complements rather than replaces the hand derivation.

linking BSD-3-Clause

SciPy numerical derivative reference

Author: SciPy Developers | Original Source Link ↗

Relevant Section / Range: derivative API: finite differences, step-size iteration, tolerances

Verified Version & Date: SciPy documentation, verified 2026-10-30

Selection Rationale:

Documents numerical differentiation as an approximation with finite step sizes and stopping criteria.

Source Prerequisites:

Python arithmetic and the definition of a derivative

Safe Fallback (Offline Resilience):

The local Day 18 tables and pure-Python snippets reproduce the relevant finite-difference behavior without SciPy.

Compared Candidate Sources & Selection Analysis (2)
NumPy: numpy.gradient ↗ compared_alternative [BSD 3-Clause License]

Array-oriented gradient estimation on sampled grids; the SciPy derivative reference matches the pointwise finite-difference helper the exercises implement.

SymPy Calculus tutorial ↗ compared_alternative [BSD License]

Symbolic rather than numerical differentiation; useful for exact cross-checks but not the finite-difference practice target.

02 120 min

Average change and secant slope

Independent Mastery Check (no AI assistance).

Prerequisites

Complete the previous activity and use the listed recovery source if blocked.

Learning Objective

Calculate average rate of change as (f(b)-f(a))/(b-a), interpret its units, and distinguish a secant line through two points from a tangent line at one point.

Instructions

Study source: within this activity's existing reading/practice time, use MIT OCW 18.01SC Single Variable Calculus, Unit 1: Differentiation; Part A: Definition and Basic Rules; derivative introduction and worked practice, then explain one connection to the local task in your own words. Keep the local exercise and acceptance checks; use its original explanation if the external source is unavailable. 1. Name the endpoints a and b, compute f(a) and f(b) separately, and write both points (a, f(a)) and (b, f(b)).
2. Predict the sign of the slope from the shape: x^2+1 increases for x ≥ 0, so on [2,5] the slope should be positive.
3. Form the quotient (f(b)-f(a))/(b-a), keeping numerator and denominator in the same order, and attach units: output units per input unit.
4. Turn the slope into the secant line with point-slope form y - f(a) = m(x - a), and check that it passes through (b, f(b)).
5. Shrink the interval toward a = 2 and watch the average slope; this table is the bridge to Day 17's derivative.
6. Compare prediction and result; if they disagree, recompute the endpoint values first.

Time plan (120 min): reading 25 + exercise 65 + check 30. Reading: OpenStax Section 2.2 (secant slope, average rate of change). Exercise: items (a)-(d). Check: the mastery item on f(x)=3x-4, unaided, then compare.

Practice & Prediction Log

(a) For the function f(x)=x^2+1 on [2,5], calculate the endpoint values, the average rate of change, and the secant line slope. Predict whether the slope is positive before calculating. (b) Write the equation of that secant line and verify it at x=5. (c) Compute the average rate of change on [2,2.5], [2,2.1] and [2,2.01], and describe the pattern. (d) If x is time in seconds and f(x) is distance in metres, state the units and the meaning of the slope from (a).

Independent Mastery Criterion

Unaided, compute the average slope for f(x)=3x-4 on [1,6] as 3 and explain why a secant uses two distinct inputs.

Authoritative Source Resources & Provenance

linking CC BY-NC-SA 4.0 noncommercial share-alike

MIT OCW 18.01SC Single Variable Calculus

Author: MIT OpenCourseWare (David Jerison) | Original Source Link ↗

Relevant Section / Range: Unit 1: Differentiation; Part A: Definition and Basic Rules; derivative introduction and worked practice

Verified Version & Date: Public edition checked 2026-11-01; moving pages may change

Selection Rationale:

Independent-study teaching sequence with lectures, worked recitations, problem sets and solutions for Days 16-19; OpenStax remains the numbered written reference.

Source Prerequisites:

Functions, algebra and high-school trigonometry.

Safe Fallback (Offline Resilience):

Use the original localized explanations, exercises and checks already present in this activity; no external account or purchase is required for that fallback.

Compared Candidate Sources & Selection Analysis (2)
OpenStax Calculus Volume 1 ↗ compared_alternative [CC BY-NC-SA 4.0]

Stable numbered text and exercises; retained alongside the lecture and recitation sequence.

Essence of Calculus ↗ compared_alternative [Copyright 3Blue1Brown / Grant Sanderson (no general open license)]

Visual motivation without an answer-keyed exercise sequence; retained for intuition.

linking CC BY-NC-SA 4.0 attribution noncommercial share-alike

OpenStax Calculus Volume 1

Author: OpenStax, Rice University (Gilbert Strang, Edwin Herman, et al.) | Original Source Link ↗

Relevant Section / Range: Sections 2.1-2.3 (Functions, Limits, and the Derivative); Sections 3.1-3.2 (Derivative Rules)

Verified Version & Date: 3rd edition, verified 2026-10-30

Selection Rationale:

Open, systematic treatment of the limit definition and derivative rules with stable section locations.

Source Prerequisites:

Algebra, coordinate geometry, and function notation

Safe Fallback (Offline Resilience):

The authored derivations, tables, and exercises in these three Study Days are complete offline recovery material; use the linked section only for optional comparison.

Compared Candidate Sources & Selection Analysis (3)
MIT OpenCourseWare 18.01SC Single Variable Calculus ↗ compared_selected_for_visual_practice [CC BY-NC-SA 4.0]

Strong exercises and transcripts, but the noncommercial share-alike terms and course navigation make OpenStax the primary stable text.

Paul's Online Math Notes: Calculus I ↗ compared_rejected_for_reuse [All Rights Reserved]

Useful reference, but rights and redistribution terms are less suitable for embedded recovery material.

SciPy Numerical Differentiation Documentation ↗ compared_selected_for_numerics [BSD-3-Clause]

A primary API reference for numerical derivative step-size behavior; it complements rather than replaces the hand derivation.

linking BSD-3-Clause

SciPy numerical derivative reference

Author: SciPy Developers | Original Source Link ↗

Relevant Section / Range: derivative API: finite differences, step-size iteration, tolerances

Verified Version & Date: SciPy documentation, verified 2026-10-30

Selection Rationale:

Documents numerical differentiation as an approximation with finite step sizes and stopping criteria.

Source Prerequisites:

Python arithmetic and the definition of a derivative

Safe Fallback (Offline Resilience):

The local Day 18 tables and pure-Python snippets reproduce the relevant finite-difference behavior without SciPy.

Compared Candidate Sources & Selection Analysis (2)
NumPy: numpy.gradient ↗ compared_alternative [BSD 3-Clause License]

Array-oriented gradient estimation on sampled grids; the SciPy derivative reference matches the pointwise finite-difference helper the exercises implement.

SymPy Calculus tutorial ↗ compared_alternative [BSD License]

Symbolic rather than numerical differentiation; useful for exact cross-checks but not the finite-difference practice target.

03 120 min

Limits from nearby values

Agent-Assisted Engineering Check

Prerequisites

Complete the previous activity and use the listed recovery source if blocked.

Learning Objective

Use a two-sided limit as a prediction of nearby output values, state the approach assumptions, and simplify a removable hole without silently redefining the original function.

Instructions

1. Check the domain before substituting: g(x)=(x^2-1)/(x-1) is defined only for x != 1. Substituting x = 1 gives 0/0, which is not a value.
2. Predict the limit from the shape of the expression before factoring.
3. Factor x^2 - 1 = (x - 1)(x + 1) and cancel (x - 1), stating that the cancellation is valid because x != 1.
4. Build the table from both sides (x = 0.9, 0.99 and x = 1.01, 1.1) with the original formula, then confirm that the simplified formula gives the same values.
5. State a two-sided limit only if the left and right values approach the same number; otherwise write "does not exist" and give both one-sided limits.
6. Keep g(1) and the limit separate in your write-up: one is a value of the function, the other describes nearby values.

Time plan (120 min): reading 30 + exercise 60 + check 30. Reading: OpenStax Section 2.3 (limit laws, removable discontinuities, one-sided limits). Exercise: items (a)-(c). Check: explain g(1) versus the limit in writing, unaided, then compare.

Practice & Prediction Log

(a) Let g(x)=(x^2-1)/(x-1) for x != 1. Factor the numerator, make a near-1 table using x=.9,.99,1.01,1.1, and state lim(x->1)g(x). Is g(1) defined by the given rule? (b) Repeat for k(x)=(x^2-4)/(x-2) near x=2 using x=1.9 and 2.1. (c) For s(x)=|x|/x, compute s(-0.1), s(-0.01), s(0.01) and s(0.1), and decide whether lim(x->0)s(x) exists.

Independent Mastery Criterion

Without AI, explain the difference between g(1) and the limit, and correctly predict the four table values and two-sided limit.

Authoritative Source Resources & Provenance

linking CC BY-NC-SA 4.0 attribution noncommercial share-alike

OpenStax Calculus Volume 1

Author: OpenStax, Rice University (Gilbert Strang, Edwin Herman, et al.) | Original Source Link ↗

Relevant Section / Range: Sections 2.1-2.3 (Functions, Limits, and the Derivative); Sections 3.1-3.2 (Derivative Rules)

Verified Version & Date: 3rd edition, verified 2026-10-30

Selection Rationale:

Open, systematic treatment of the limit definition and derivative rules with stable section locations.

Source Prerequisites:

Algebra, coordinate geometry, and function notation

Safe Fallback (Offline Resilience):

The authored derivations, tables, and exercises in these three Study Days are complete offline recovery material; use the linked section only for optional comparison.

Compared Candidate Sources & Selection Analysis (3)
MIT OpenCourseWare 18.01SC Single Variable Calculus ↗ compared_selected_for_visual_practice [CC BY-NC-SA 4.0]

Strong exercises and transcripts, but the noncommercial share-alike terms and course navigation make OpenStax the primary stable text.

Paul's Online Math Notes: Calculus I ↗ compared_rejected_for_reuse [All Rights Reserved]

Useful reference, but rights and redistribution terms are less suitable for embedded recovery material.

SciPy Numerical Differentiation Documentation ↗ compared_selected_for_numerics [BSD-3-Clause]

A primary API reference for numerical derivative step-size behavior; it complements rather than replaces the hand derivation.

linking BSD-3-Clause

SciPy numerical derivative reference

Author: SciPy Developers | Original Source Link ↗

Relevant Section / Range: derivative API: finite differences, step-size iteration, tolerances

Verified Version & Date: SciPy documentation, verified 2026-10-30

Selection Rationale:

Documents numerical differentiation as an approximation with finite step sizes and stopping criteria.

Source Prerequisites:

Python arithmetic and the definition of a derivative

Safe Fallback (Offline Resilience):

The local Day 18 tables and pure-Python snippets reproduce the relevant finite-difference behavior without SciPy.

Compared Candidate Sources & Selection Analysis (2)
NumPy: numpy.gradient ↗ compared_alternative [BSD 3-Clause License]

Array-oriented gradient estimation on sampled grids; the SciPy derivative reference matches the pointwise finite-difference helper the exercises implement.

SymPy Calculus tutorial ↗ compared_alternative [BSD License]

Symbolic rather than numerical differentiation; useful for exact cross-checks but not the finite-difference practice target.

04 120 min

Retrieval and independent bridge

Independent Mastery Check (no AI assistance)

Prerequisites

Complete the previous activity and use the listed recovery source if blocked.

Learning Objective

Retrieve functions, secant slope, and limits, then connect them to the idea that an instantaneous derivative is a limit of secant slopes. Record prediction, work, observation, and a recovery action.

Instructions

1. Work closed book: notes, the earlier answers on this page and AI tools stay closed until you have written an answer for every item.
2. Write the general quotient with the endpoint 3 + h before inserting any number.
3. Expand (3 + h)^2 fully, subtract p(3), and cancel h only after stating h != 0.
4. Evaluate the simplified quotient for h = 0.1, 0.01, 0.001 and for h = -0.1, -0.01, so both sides of 3 are covered.
5. Name, in your own words, the assumptions a derivative at 3 needs.
6. Then open the reference answer, mark every line as matched or missed, and for each miss record which activity (1, 2 or 3) you will revisit. Recovery is a fresh attempt at an equivalent item, not a copy of the answer.

Time plan (120 min): reading 15 + exercise 60 + check 45. Reading: only your own notes from Activities 1-3. Exercise: items (1)-(4), closed book. Check: mark the attempt, revisit the activity behind each miss, then solve item (4) again at a=1.

Practice & Prediction Log

Closed book: (1) find the average slope of p(x)=x^2 on [3,3.1]; (2) predict what happens as the second endpoint approaches 3; (3) state the assumptions needed for a derivative at 3. Show every substitution. (4) Repeat (1)-(2) at a=-1: write the quotient for p on [-1,-1+h] and find its limit.

Independent Mastery Criterion

Independent Mastery Check: with no AI or answer lookup, derive the h-expression, evaluate h=.1, and justify the limiting value 6 in complete steps.

Authoritative Source Resources & Provenance

linking CC BY-NC-SA 4.0 attribution noncommercial share-alike

OpenStax Calculus Volume 1

Author: OpenStax, Rice University (Gilbert Strang, Edwin Herman, et al.) | Original Source Link ↗

Relevant Section / Range: Sections 2.1-2.3 (Functions, Limits, and the Derivative); Sections 3.1-3.2 (Derivative Rules)

Verified Version & Date: 3rd edition, verified 2026-10-30

Selection Rationale:

Open, systematic treatment of the limit definition and derivative rules with stable section locations.

Source Prerequisites:

Algebra, coordinate geometry, and function notation

Safe Fallback (Offline Resilience):

The authored derivations, tables, and exercises in these three Study Days are complete offline recovery material; use the linked section only for optional comparison.

Compared Candidate Sources & Selection Analysis (3)
MIT OpenCourseWare 18.01SC Single Variable Calculus ↗ compared_selected_for_visual_practice [CC BY-NC-SA 4.0]

Strong exercises and transcripts, but the noncommercial share-alike terms and course navigation make OpenStax the primary stable text.

Paul's Online Math Notes: Calculus I ↗ compared_rejected_for_reuse [All Rights Reserved]

Useful reference, but rights and redistribution terms are less suitable for embedded recovery material.

SciPy Numerical Differentiation Documentation ↗ compared_selected_for_numerics [BSD-3-Clause]

A primary API reference for numerical derivative step-size behavior; it complements rather than replaces the hand derivation.

linking BSD-3-Clause

SciPy numerical derivative reference

Author: SciPy Developers | Original Source Link ↗

Relevant Section / Range: derivative API: finite differences, step-size iteration, tolerances

Verified Version & Date: SciPy documentation, verified 2026-10-30

Selection Rationale:

Documents numerical differentiation as an approximation with finite step sizes and stopping criteria.

Source Prerequisites:

Python arithmetic and the definition of a derivative

Safe Fallback (Offline Resilience):

The local Day 18 tables and pure-Python snippets reproduce the relevant finite-difference behavior without SciPy.

Compared Candidate Sources & Selection Analysis (2)
NumPy: numpy.gradient ↗ compared_alternative [BSD 3-Clause License]

Array-oriented gradient estimation on sampled grids; the SciPy derivative reference matches the pointwise finite-difference helper the exercises implement.

SymPy Calculus tutorial ↗ compared_alternative [BSD License]

Symbolic rather than numerical differentiation; useful for exact cross-checks but not the finite-difference practice target.