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Mathematics (B1) formulas for CUET Maths

Every formula and rule in CUET Maths Section B1, Mathematics, on one page: relations and functions, inverse trigonometry, continuity, the chain rule and logarithmic differentiation, areas of circles, parabolas and ellipses, homogeneous and linear differential equations, vectors, lines in space and Bayes' theorem. Read it the night before; the chapter lessons have the worked examples.

This is the short version of the nine Section B1 lessons. If a line here is not clear, the chapter it comes from is named in its heading.

Relations and functions

IdeaFormula or fact
Reflexive(a,a)∈R(a, a) \in R for every aa
Symmetric(a,b)∈R⇒(b,a)∈R(a, b) \in R \Rightarrow (b, a) \in R
Transitive(a,b),(b,c)∈R⇒(a,c)∈R(a, b), (b, c) \in R \Rightarrow (a, c) \in R
Equivalenceall three; its classes split the set
Counts on nn elements2n22^{n^{2}} relations, 2n2−n2^{n^{2} - n} reflexive
One-onef(a1)=f(a2)⇒a1=a2f(a_1) = f(a_2) \Rightarrow a_1 = a_2
Ontorange = codomain
Counts from mm to nn elementsnmn^{m} functions, nPm{}^{n}P_{m} one-one

Inverse trigonometric functions

FunctionDomainPrincipal branch
sin⁡−1x\sin^{-1} x[−1,1][-1, 1][−π2,π2]\left[-\frac{\pi}{2}, \frac{\pi}{2}\right]
cos⁡−1x\cos^{-1} x[−1,1][-1, 1][0,π][0, \pi]
tan⁡−1x\tan^{-1} xR\mathbb{R}(−π2,π2)\left(-\frac{\pi}{2}, \frac{\pi}{2}\right)
cot⁡−1x\cot^{-1} xR\mathbb{R}(0,π)(0, \pi)
sec⁡−1x\sec^{-1} x∣x∣≥1\lvert x \rvert \ge 1[0,π]−{π2}[0, \pi] - \left\{\frac{\pi}{2}\right\}
cosec⁡−1x\operatorname{cosec}^{-1} x∣x∣≥1\lvert x \rvert \ge 1[−π2,π2]−{0}\left[-\frac{\pi}{2}, \frac{\pi}{2}\right] - \{0\}
Negative inputssin⁡−1(−x)=−sin⁡−1x\sin^{-1}(-x) = -\sin^{-1} x; cos⁡−1(−x)=π−cos⁡−1x\cos^{-1}(-x) = \pi - \cos^{-1} x

Continuity and differentiability

IdeaFormula or fact
Continuity at aaleft limit = right limit = f(a)f(a)
Standard limitssin⁡axx→a\frac{\sin ax}{x} \to a, eax−1x→a\frac{e^{ax} - 1}{x} \to a, 1−cos⁡axx2→a22\frac{1 - \cos ax}{x^{2}} \to \frac{a^{2}}{2}
Differentiability at a joinvalues match and slopes match
Linkdifferentiable ⇒ continuous, not the converse

Chain rule, log differentiation and rates

IdeaFormula or fact
Chain ruledydx=dydu⋅dudx\frac{dy}{dx} = \frac{dy}{du} \cdot \frac{du}{dx}
sin⁡−1x\sin^{-1} x, cos⁡−1x\cos^{-1} x±11−x2\pm\frac{1}{\sqrt{1 - x^{2}}}
tan⁡−1x\tan^{-1} x, cot⁡−1x\cot^{-1} x±11+x2\pm\frac{1}{1 + x^{2}}
eue^{u}, log⁡u\log ueuu′e^{u}u', u′u\frac{u'}{u}
xxx^{x}xx(1+log⁡x)x^{x}(1 + \log x)
Related ratesdVdt=dVdr⋅drdt\frac{dV}{dt} = \frac{dV}{dr} \cdot \frac{dr}{dt}

Area of bounded regions

RegionArea
Circle x2+y2=a2x^{2} + y^{2} = a^{2}πa2\pi a^{2}
Ellipse x2a2+y2b2=1\frac{x^{2}}{a^{2}} + \frac{y^{2}}{b^{2}} = 1πab\pi ab
y2=4axy^{2} = 4ax up to x=ax = a83a2\frac{8}{3}a^{2}
Key integral∫0aa2−x2 dx=πa24\int_0^{a} \sqrt{a^{2} - x^{2}}\,dx = \frac{\pi a^{2}}{4}

Homogeneous and linear differential equations

IdeaFormula or fact
Homogeneous of degree nnF(λx,λy)=λnF(x,y)F(\lambda x, \lambda y) = \lambda^{n} F(x, y)
Homogeneous equationput y=vxy = vx, dydx=v+xdvdx\frac{dy}{dx} = v + x\frac{dv}{dx}
Lineardydx+Py=Q\frac{dy}{dx} + Py = Q; I.F. =e∫P dx= e^{\int P\,dx}
Solutiony⋅I.F.=∫Q⋅I.F. dx+Cy \cdot \text{I.F.} = \int Q \cdot \text{I.F.}\,dx + C

Vectors

IdeaFormula or fact
Magnitudea12+a22+a32\sqrt{a_1^{2} + a_2^{2} + a_3^{2}}
Section formulamb⃗+na⃗m+n\frac{m\vec{b} + n\vec{a}}{m + n} inside, mb⃗−na⃗m−n\frac{m\vec{b} - n\vec{a}}{m - n} outside
Dot producta1b1+a2b2+a3b3=∣a⃗∣∣b⃗∣cos⁡θa_1b_1 + a_2b_2 + a_3b_3 = \lvert \vec{a} \rvert \lvert \vec{b} \rvert \cos \theta
Projection on b⃗\vec{b}a⃗⋅b⃗∣b⃗∣\frac{\vec{a} \cdot \vec{b}}{\lvert \vec{b} \rvert}
Cross productmagnitude ∣a⃗∣∣b⃗∣sin⁡θ\lvert \vec{a} \rvert \lvert \vec{b} \rvert \sin \theta, perpendicular to both
Areasparallelogram ∣a⃗×b⃗∣\lvert \vec{a} \times \vec{b} \rvert; triangle half

Three-dimensional geometry

IdeaFormula or fact
Liner⃗=a⃗+λb⃗\vec{r} = \vec{a} + \lambda\vec{b}; x−x1a=y−y1b=z−z1c\frac{x - x_1}{a} = \frac{y - y_1}{b} = \frac{z - z_1}{c}
Anglecos⁡θ=∣a1a2+b1b2+c1c2∣a12+b12+c12a22+b22+c22\cos \theta = \frac{\lvert a_1a_2 + b_1b_2 + c_1c_2 \rvert}{\sqrt{a_1^{2} + b_1^{2} + c_1^{2}}\sqrt{a_2^{2} + b_2^{2} + c_2^{2}}}
Skew linesd=∣(a⃗2−a⃗1)⋅(b⃗1×b⃗2)∣∣b⃗1×b⃗2∣d = \frac{\lvert (\vec{a}_2 - \vec{a}_1) \cdot (\vec{b}_1 \times \vec{b}_2) \rvert}{\lvert \vec{b}_1 \times \vec{b}_2 \rvert}
Parallel linesd=∣b⃗×(a⃗2−a⃗1)∣∣b⃗∣d = \frac{\lvert \vec{b} \times (\vec{a}_2 - \vec{a}_1) \rvert}{\lvert \vec{b} \rvert}

Conditional probability and Bayes' theorem

IdeaFormula or fact
ConditionalP(A∣B)=P(A∩B)P(B)P(A \mid B) = \frac{P(A \cap B)}{P(B)}
MultiplicationP(A∩B)=P(A)P(B∣A)P(A \cap B) = P(A)P(B \mid A)
IndependentP(A∩B)=P(A)P(B)P(A \cap B) = P(A)P(B)
Total probabilityP(A)=∑P(Ei)P(A∣Ei)P(A) = \sum P(E_i)P(A \mid E_i)
BayesP(Ei∣A)=P(Ei)P(A∣Ei)P(A)P(E_i \mid A) = \frac{P(E_i)P(A \mid E_i)}{P(A)}

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