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MathematicsMathematics7 閲覧数·更新日 Jun 6, 2026·7 ページ

Mastering Differentiation: Tangents, Normals, and Curve Sketching

Differentiation isn't just abstract maths - it's your toolkit for...

1
of 7
# Applications of Differentiation

An overview of applications

Differentiation isn't just about finding the derivative of a function. It's

Applications Overview and Key Concepts

Understanding differentiation gives you the power to solve problems that matter in the real world. The derivative tells you how steep a curve is at any point, which translates to finding maximum profits, minimum costs, or optimal designs.

When you see dydx\frac{dy}{dx} or f(x)f'(x), you're looking at the instantaneous rate of change - basically the gradient of the tangent line at any point. This is your foundation for everything else.

Stationary points occur where f(x)=0f'(x) = 0, meaning the gradient is zero and you've got a horizontal tangent. These points are crucial because they're often where maximum and minimum values occur - exactly what you need for optimisation problems.

Remember: A tangent touches the curve at one point with the same gradient, while a normal is perpendicular to the tangent at that same point.

2
of 7
# Applications of Differentiation

An overview of applications

Differentiation isn't just about finding the derivative of a function. It's

Finding Tangent and Normal Lines Plus Rates of Change

Getting the equation of a tangent follows a straightforward process: find f(x)f'(x), substitute your x-coordinate to get the gradient, then use yy1=m(xx1)y - y_1 = m(x - x_1). For the normal line, use mN=1mTm_N = -\frac{1}{m_T} since perpendicular lines have gradients that multiply to give -1.

Rates of change connect maths to physics beautifully. If you've got displacement s(t)s(t), then velocity is v=dsdtv = \frac{ds}{dt} and acceleration is a=d2sdt2a = \frac{d^2s}{dt^2}. It's all about how quickly things change over time.

The real power comes when you realise that any rate of change problem follows the same pattern. Whether it's water flowing from a tank or profit changing with production levels, the derivative gives you the rate.

Top Tip: Always check your perpendicular gradients multiply to give -1 - it's an easy way to catch mistakes!

3
of 7
# Applications of Differentiation

An overview of applications

Differentiation isn't just about finding the derivative of a function. It's

Classifying Stationary Points

The second derivative test is your best friend for determining whether stationary points are maximums, minimums, or points of inflection. Once you've found where f(x)=0f'(x) = 0, substitute those x-values into f(x)f''(x).

If f(x)>0f''(x) > 0, you've got a local minimum - think of a smile shape. If f(x)<0f''(x) < 0, it's a local maximum - like a frown. When f(x)=0f''(x) = 0, the test is inconclusive and you'll need to check the behaviour on either side.

Points of inflection occur where the curve changes from concave up to concave down (or vice versa). These might also be stationary points, but not always.

Memory Trick: Positive second derivative = minimum (like a positive, happy smile ☺). Negative second derivative = maximum (like a negative, sad frown ☹).

4
of 7
# Applications of Differentiation

An overview of applications

Differentiation isn't just about finding the derivative of a function. It's

Curve Sketching Techniques

Curve sketching brings together everything you know about a function into one clear picture. Start with the y-intercept letx=0let x = 0, find any obvious x-intercepts, then locate and classify all stationary points.

Consider what happens as x approaches positive and negative infinity - for polynomials, the highest power term dominates the behaviour. This tells you how the curve behaves at the extremes.

Plot your key points (intercepts and stationary points) and connect them with smooth curves that respect the nature of each point. Maximums create peaks, minimums create troughs.

Pro Tip: Always sketch a rough version first to check your curve makes sense before drawing the final version!

5
of 7
# Applications of Differentiation

An overview of applications

Differentiation isn't just about finding the derivative of a function. It's

Worked Example: Tangent and Normal Lines

Let's work through finding tangent and normal equations for y=x24x+1y = x^2 - 4x + 1 at point (1, -2). First, differentiate to get dydx=2x4\frac{dy}{dx} = 2x - 4.

At x = 1, the gradient of the tangent is mT=2(1)4=2m_T = 2(1) - 4 = -2. Using the point-slope form: y(2)=2(x1)y - (-2) = -2(x - 1), which simplifies to $2x + y = 0$.

For the normal, the gradient is mN=12=12m_N = -\frac{1}{-2} = \frac{1}{2}. Using the same point: y+2=12(x1)y + 2 = \frac{1}{2}(x - 1), which gives us x2y5=0x - 2y - 5 = 0.

Check Your Work: Verify that mT×mN=(2)×12=1m_T \times m_N = (-2) \times \frac{1}{2} = -1

6
of 7
# Applications of Differentiation

An overview of applications

Differentiation isn't just about finding the derivative of a function. It's

Optimisation Example: Maximum Area Problem

Optimisation problems are where differentiation really shines. Consider a rectangular garden against a wall, using 80m of fencing for three sides. Let the parallel side be l and the other sides be w.

Since fencing covers l+2w=80l + 2w = 80, we get l=802wl = 80 - 2w. The area function becomes A=lw=(802w)w=80w2w2A = lw = (80 - 2w)w = 80w - 2w^2.

To maximise area, find dAdw=804w\frac{dA}{dw} = 80 - 4w and set it to zero: $80 - 4w = 0gives gives w = 20m.Therefore. Therefore l = 80 - 2(20) = 40m.Since. Since \frac{d^2A}{dw^2} = -4 < 0$, this confirms a maximum.

Real-World Check: Always verify your answer makes physical sense - negative dimensions would be impossible!

7
of 7
# Applications of Differentiation

An overview of applications

Differentiation isn't just about finding the derivative of a function. It's

Essential Tips and Quick Reference

Common mistakes to avoid: Always substitute x-values back into the original function f(x)f(x) for coordinates, not into the derivative. When the second derivative test gives zero, check the sign of f(x)f'(x) on either side of the stationary point.

Read optimisation questions carefully - are you finding the maximum value itself or the conditions that create it? Context matters enormously.

Quick reference for revision: Stationary points occur when f(x)=0f'(x) = 0. Use f(x)>0f''(x) > 0 for minimums, f(x)<0f''(x) < 0 for maximums. For motion problems: velocity is dsdt\frac{ds}{dt} and acceleration is d2sdt2\frac{d^2s}{dt^2}.

Success Strategy: Practice identifying what type of problem you're dealing with first - this determines which technique to use!

そんなこと聞いてくれるのを待ってたよ...

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このアプリはとても使いやすくて、デザインも良いです。今のところ探していたものは全て見つかったし、プレゼン資料からもたくさん学べました!絶対に課題でも使いたいと思います!もちろん、アイデアを得るのにもすごく役立ちます。

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このアプリは本当に素晴らしいです。学習ノートやサポート資料がとても豊富で[...]。例えば、私の苦手科目はフランス語なんですが、このアプリにはサポートオプションがたくさんあります。このアプリのおかげでフランス語が上達しました。誰にでもおすすめしたいです。

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すごい、本当に驚いた。広告で何度も見かけたからアプリを試してみたら、めちゃくちゃ感動した。このアプリは学校で欲しかった「まさにこれ!」って感じのサポートで、特に練習問題や要点まとめみたいな機能がたくさんあって、個人的にすごく助かってる。

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MathematicsMathematics7 閲覧数·更新日 Jun 6, 2026·7 ページ

Mastering Differentiation: Tangents, Normals, and Curve Sketching

Differentiation isn't just abstract maths - it's your toolkit for solving real-world problems like finding the steepest point on a road or calculating maximum profit. You'll use derivatives to analyse how functions behave and find optimal solutions to practical situations.

1
of 7
# Applications of Differentiation

An overview of applications

Differentiation isn't just about finding the derivative of a function. It's

サインアップしてコンテンツを見よう。無料だよ!

  • 全ドキュメントへのアクセス
  • 成績アップ
  • 数百万人の学生と一緒に学習

Applications Overview and Key Concepts

Understanding differentiation gives you the power to solve problems that matter in the real world. The derivative tells you how steep a curve is at any point, which translates to finding maximum profits, minimum costs, or optimal designs.

When you see dydx\frac{dy}{dx} or f(x)f'(x), you're looking at the instantaneous rate of change - basically the gradient of the tangent line at any point. This is your foundation for everything else.

Stationary points occur where f(x)=0f'(x) = 0, meaning the gradient is zero and you've got a horizontal tangent. These points are crucial because they're often where maximum and minimum values occur - exactly what you need for optimisation problems.

Remember: A tangent touches the curve at one point with the same gradient, while a normal is perpendicular to the tangent at that same point.

2
of 7
# Applications of Differentiation

An overview of applications

Differentiation isn't just about finding the derivative of a function. It's

サインアップしてコンテンツを見よう。無料だよ!

  • 全ドキュメントへのアクセス
  • 成績アップ
  • 数百万人の学生と一緒に学習

Finding Tangent and Normal Lines Plus Rates of Change

Getting the equation of a tangent follows a straightforward process: find f(x)f'(x), substitute your x-coordinate to get the gradient, then use yy1=m(xx1)y - y_1 = m(x - x_1). For the normal line, use mN=1mTm_N = -\frac{1}{m_T} since perpendicular lines have gradients that multiply to give -1.

Rates of change connect maths to physics beautifully. If you've got displacement s(t)s(t), then velocity is v=dsdtv = \frac{ds}{dt} and acceleration is a=d2sdt2a = \frac{d^2s}{dt^2}. It's all about how quickly things change over time.

The real power comes when you realise that any rate of change problem follows the same pattern. Whether it's water flowing from a tank or profit changing with production levels, the derivative gives you the rate.

Top Tip: Always check your perpendicular gradients multiply to give -1 - it's an easy way to catch mistakes!

3
of 7
# Applications of Differentiation

An overview of applications

Differentiation isn't just about finding the derivative of a function. It's

サインアップしてコンテンツを見よう。無料だよ!

  • 全ドキュメントへのアクセス
  • 成績アップ
  • 数百万人の学生と一緒に学習

Classifying Stationary Points

The second derivative test is your best friend for determining whether stationary points are maximums, minimums, or points of inflection. Once you've found where f(x)=0f'(x) = 0, substitute those x-values into f(x)f''(x).

If f(x)>0f''(x) > 0, you've got a local minimum - think of a smile shape. If f(x)<0f''(x) < 0, it's a local maximum - like a frown. When f(x)=0f''(x) = 0, the test is inconclusive and you'll need to check the behaviour on either side.

Points of inflection occur where the curve changes from concave up to concave down (or vice versa). These might also be stationary points, but not always.

Memory Trick: Positive second derivative = minimum (like a positive, happy smile ☺). Negative second derivative = maximum (like a negative, sad frown ☹).

4
of 7
# Applications of Differentiation

An overview of applications

Differentiation isn't just about finding the derivative of a function. It's

サインアップしてコンテンツを見よう。無料だよ!

  • 全ドキュメントへのアクセス
  • 成績アップ
  • 数百万人の学生と一緒に学習

Curve Sketching Techniques

Curve sketching brings together everything you know about a function into one clear picture. Start with the y-intercept letx=0let x = 0, find any obvious x-intercepts, then locate and classify all stationary points.

Consider what happens as x approaches positive and negative infinity - for polynomials, the highest power term dominates the behaviour. This tells you how the curve behaves at the extremes.

Plot your key points (intercepts and stationary points) and connect them with smooth curves that respect the nature of each point. Maximums create peaks, minimums create troughs.

Pro Tip: Always sketch a rough version first to check your curve makes sense before drawing the final version!

5
of 7
# Applications of Differentiation

An overview of applications

Differentiation isn't just about finding the derivative of a function. It's

サインアップしてコンテンツを見よう。無料だよ!

  • 全ドキュメントへのアクセス
  • 成績アップ
  • 数百万人の学生と一緒に学習

Worked Example: Tangent and Normal Lines

Let's work through finding tangent and normal equations for y=x24x+1y = x^2 - 4x + 1 at point (1, -2). First, differentiate to get dydx=2x4\frac{dy}{dx} = 2x - 4.

At x = 1, the gradient of the tangent is mT=2(1)4=2m_T = 2(1) - 4 = -2. Using the point-slope form: y(2)=2(x1)y - (-2) = -2(x - 1), which simplifies to $2x + y = 0$.

For the normal, the gradient is mN=12=12m_N = -\frac{1}{-2} = \frac{1}{2}. Using the same point: y+2=12(x1)y + 2 = \frac{1}{2}(x - 1), which gives us x2y5=0x - 2y - 5 = 0.

Check Your Work: Verify that mT×mN=(2)×12=1m_T \times m_N = (-2) \times \frac{1}{2} = -1

6
of 7
# Applications of Differentiation

An overview of applications

Differentiation isn't just about finding the derivative of a function. It's

サインアップしてコンテンツを見よう。無料だよ!

  • 全ドキュメントへのアクセス
  • 成績アップ
  • 数百万人の学生と一緒に学習

Optimisation Example: Maximum Area Problem

Optimisation problems are where differentiation really shines. Consider a rectangular garden against a wall, using 80m of fencing for three sides. Let the parallel side be l and the other sides be w.

Since fencing covers l+2w=80l + 2w = 80, we get l=802wl = 80 - 2w. The area function becomes A=lw=(802w)w=80w2w2A = lw = (80 - 2w)w = 80w - 2w^2.

To maximise area, find dAdw=804w\frac{dA}{dw} = 80 - 4w and set it to zero: $80 - 4w = 0gives gives w = 20m.Therefore. Therefore l = 80 - 2(20) = 40m.Since. Since \frac{d^2A}{dw^2} = -4 < 0$, this confirms a maximum.

Real-World Check: Always verify your answer makes physical sense - negative dimensions would be impossible!

7
of 7
# Applications of Differentiation

An overview of applications

Differentiation isn't just about finding the derivative of a function. It's

サインアップしてコンテンツを見よう。無料だよ!

  • 全ドキュメントへのアクセス
  • 成績アップ
  • 数百万人の学生と一緒に学習

Essential Tips and Quick Reference

Common mistakes to avoid: Always substitute x-values back into the original function f(x)f(x) for coordinates, not into the derivative. When the second derivative test gives zero, check the sign of f(x)f'(x) on either side of the stationary point.

Read optimisation questions carefully - are you finding the maximum value itself or the conditions that create it? Context matters enormously.

Quick reference for revision: Stationary points occur when f(x)=0f'(x) = 0. Use f(x)>0f''(x) > 0 for minimums, f(x)<0f''(x) < 0 for maximums. For motion problems: velocity is dsdt\frac{ds}{dt} and acceleration is d2sdt2\frac{d^2s}{dt^2}.

Success Strategy: Practice identifying what type of problem you're dealing with first - this determines which technique to use!

そんなこと聞いてくれるのを待ってたよ...

KnowunityのAIコンパニオンとは?

KnowunityのAIコンパニオンは学生向けに設計されたAIツールで、単なる答えを提供するだけではありません。数百万のKnowunityリソースを基に構築され、関連する情報、個別の学習プラン、クイズ、コンテンツをチャット内で直接提供し、あなたの個別の学習過程に適応します。

Knowunityアプリはどこでダウンロードできますか?

Google Play StoreとApple App Storeからアプリをダウンロードできます。

Knowunityは本当に無料ですか?

その通り!学習コンテンツへの無料アクセス、仲間の学生とのつながり、そして即座のサポートを手のひらで楽しもう。

Mathematicsの人気コンテンツ

8

人気コンテンツ

9

探しているものが見つからない?他の教科も見てみよう。

生徒たちが愛用中 — あなたもきっと気に入るはず

4.6/5App Store
4.7/5Google Play

このアプリはとても使いやすくて、デザインも良いです。今のところ探していたものは全て見つかったし、プレゼン資料からもたくさん学べました!絶対に課題でも使いたいと思います!もちろん、アイデアを得るのにもすごく役立ちます。

Stefan SiOSユーザー

このアプリは本当に素晴らしいです。学習ノートやサポート資料がとても豊富で[...]。例えば、私の苦手科目はフランス語なんですが、このアプリにはサポートオプションがたくさんあります。このアプリのおかげでフランス語が上達しました。誰にでもおすすめしたいです。

Samantha KlichAndroidユーザー

すごい、本当に驚いた。広告で何度も見かけたからアプリを試してみたら、めちゃくちゃ感動した。このアプリは学校で欲しかった「まさにこれ!」って感じのサポートで、特に練習問題や要点まとめみたいな機能がたくさんあって、個人的にすごく助かってる。

AnnaiOSユーザー