MHT-CET Maths · Vectors
Linear Combinations, Collinearity, and Coplanarity
Building one vector out of others as m·a + n·b, and the structural conditions hiding inside that idea — collinear vectors need ONE scalar, coplanar vectors need TWO, and a dependent set is exactly a coplanar one.
Why this matters
25 PYQs sit here, and they lean hard: 10 HARD, 12 MODERATE, 3 EASY. Two recurring shapes dominate: the collinearity / coplanarity conditions (one-scalar vs two-scalar, and the 'no two collinear' chain systems), and the linear-system shapes — express a vector as m·b + n·c, find components against a transformed basis, or test linear dependence by a determinant. Get the counting right (collinear ⇒ 1 scalar, coplanar / dependent ⇒ 2 scalars) and almost every question here reduces to a small system you can solve by equating components.
Concept 1 of 10: Linear combination of vectors
Definition
A vector is a linear combination of vectors if there exist scalars such that . In the standard basis you build a combination componentwise: has -component , -component , and so on. The scalars are called the coefficients of the combination.
Linear combination
- real scalar coefficients (any sign, including zero)
- the combined vector — built componentwise
Diagram · component form (drag to rotate)
Step along x (2.4 î), then y ( 1.6 ĵ), then z ( 2.0 k̂) to reach the tip: v = 2.4 î + 1.6 ĵ + 2.0 k̂. Any vector is the sum of its axis components, and |v| = √(x² + y² + z²).
Worked example
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A linear combination is built componentwise — three sums, not one
Concept 2 of 10: Collinear vectors and collinear points (one scalar)
Definition
- Collinear vectors: and () are collinear iff for some scalar . In components, this means the components are proportional: .
- Collinear points: (position vectors ) are collinear iff , i.e. for some scalar .
- \"is collinear with\": the phrase is collinear with means — introduce ONE unknown scalar and solve.
Collinearity (one scalar)
- the single scalar; same direction, opposite
Worked example
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Collinear needs ONE scalar; coplanar needs TWO — keep the count straight
Parallel VECTORS vs collinear POINTS
Concept 3 of 10: Coplanar vectors (two scalars)
Definition
Three vectors are coplanar iff one is a linear combination of the other two: for some scalars (assuming non-collinear). Equivalently, their scalar triple product vanishes: , i.e. the determinant of their components is zero. \"Lies in the plane of and \" is the phrase that signals this: write the unknown vector as and solve for .
Coplanarity (two scalars)
- the two scalars — one per spanning vector
- scalar triple product; zero ⟺ coplanar
Diagram · orthonormal triple î, ĵ, k̂ (drag to rotate)
î, ĵ, k̂ are mutually perpendicular unit vectors: each pair has dot product 0 (î·ĵ = ĵ·k̂ = k̂·î = 0) and each has length 1. They form the standard basis — any vector is a unique combination x î + y ĵ + z k̂.
Worked example
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Coplanar / dependent ⇒ TWO scalars and a vanishing determinant
Concept 4 of 10: Forming a combination, then normalising / measuring it
Definition
Once a linear combination is built componentwise, the follow-up is routine:
- Unit vector along : .
- Vector of magnitude parallel to : .
- Angle between two combinations : .
Scale a combination to a target magnitude
- the linear combination, built first
- the required magnitude of the parallel vector
Worked example
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The same idea in a real exam question:
Example 4 · Vectors · Linear Combinations, Collinearity, and Coplanarity
Build the combination BEFORE you normalise — don't normalise the parts
\"Parallel of magnitude \" has TWO answers —
Concept 5 of 10: Collinearity of three points (and who lies between)
Definition
Points with position vectors are collinear iff (or any two displacements are proportional), where , . Reading the multiplier: if with , the line goes so lies between and ; a negative means doubles back, putting a different point in the middle.
Three-point collinearity
- the scalar; its sign/size fixes the order of the points
Worked example
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The same idea in a real exam question:
Example 5 · Vectors · Linear Combinations, Collinearity, and Coplanarity
Use displacements that SHARE a point
Concept 6 of 10: Express a vector as a combination of two others
Definition
To write : equate each component to get the system , , . Pick the two simplest equations, solve for and , then verify with the remaining one. If the third equation also holds, the representation is valid (the three vectors are coplanar); if it fails, no such exist.
Two-equation linear system
- coefficients to solve for; the 3rd component is the check
Worked example
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The same idea in a real exam question:
Example 6 · Vectors · Linear Combinations, Collinearity, and Coplanarity
Solve from two equations, but ALWAYS verify with the third
Concept 7 of 10: Linear dependence vs independence (the determinant test)
Definition
Vectors are linearly dependent if there exist scalars , not all zero, with . They are independent if forces . Test in 3-D: dependent ⟺ coplanar ⟺ the determinant of their components is zero:
- ⇒ dependent / coplanar.
- ⇒ independent / non-coplanar (they form a basis).
Dependence ⟺ vanishing determinant
- scalars; a non-trivial solution means dependent
Worked example
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The same idea in a real exam question:
Example 7 · Vectors · Linear Combinations, Collinearity, and Coplanarity
Linearly dependent = coplanar = zero determinant — three names, one idea
A second condition (like ) is part of the same problem
Concept 8 of 10: Chained-collinearity systems (\"no two collinear\")
Definition
Turn each \"collinear with\" into a scalar multiple, then use independence to match coefficients:
- (one scalar ).
- (one scalar ).
Substitute one into the other and compare coefficients of the linearly-independent (each side's coefficient of a given basis vector must match). This pins down — typically — and yields the target, e.g. , so .
Chained collinearity
- one scalar per collinearity fact; matched via coefficient comparison
Worked example
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The same idea in a real exam question:
Example 8 · Vectors · Linear Combinations, Collinearity, and Coplanarity
Match coefficients only because the vectors are independent
Read the target carefully: vs
Concept 9 of 10: A vector lying in the plane of two others
Definition
A vector coplanar with non-collinear has the form . Common second conditions and how to use them:
- Bisects the angle between : is parallel to (sum of the UNIT vectors).
- Perpendicular to : impose .
- Projection on equals : impose .
- Magnitude : scale the resulting direction to length .
Coplanar form + a second condition
- two scalars from coplanarity
- second conditionbisector / perpendicular / projection / magnitude — fixes the scalars
Diagram · orthonormal triple î, ĵ, k̂ (drag to rotate)
î, ĵ, k̂ are mutually perpendicular unit vectors: each pair has dot product 0 (î·ĵ = ĵ·k̂ = k̂·î = 0) and each has length 1. They form the standard basis — any vector is a unique combination x î + y ĵ + z k̂.
Worked example
Practise this conceptself-check · 4 quick reps
The same idea in a real exam question:
Example 9 · Vectors · Linear Combinations, Collinearity, and Coplanarity
Angle bisector uses UNIT vectors, not the raw vectors
Coplanar form first, condition second
Concept 10 of 10: Components of a vector against a transformed basis
Definition
If are non-coplanar and (the given components), then writing and collecting like terms gives a system: the coefficient of must equal , of equal , of equal . Solve the system for ; independence of is what makes the coefficient-matching valid.
Match coefficients of a basis
- rowscoefficients of equated to the given components
Worked example
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The same idea in a real exam question:
Example 10 · Vectors · Linear Combinations, Collinearity, and Coplanarity
Equating components needs an INDEPENDENT basis
Answer the asked combination, not the raw
Summary — formulas & gotchas at a glance
A revision cheat-sheet for the formulas and gotchas above. Click any concept name to jump back to its full explanation.
Formulas (10)
- Linear combination of vectors
Linear combination
- Collinear vectors and collinear points (one scalar)
Collinearity (one scalar)
- Coplanar vectors (two scalars)
Coplanarity (two scalars)
- Forming a combination, then normalising / measuring it
Scale a combination to a target magnitude
- Collinearity of three points (and who lies between)
Three-point collinearity
- Express a vector as a combination of two others
Two-equation linear system
- Linear dependence vs independence (the determinant test)
Dependence ⟺ vanishing determinant
- Chained-collinearity systems (\"no two collinear\")
Chained collinearity
- A vector lying in the plane of two others
Coplanar form + a second condition
- Components of a vector against a transformed basis
Match coefficients of a basis
Watch out for (16)
- A linear combination is built componentwise — three sums, not one→ Linear combination of vectors
- Collinear needs ONE scalar; coplanar needs TWO — keep the count straight→ Collinear vectors and collinear points (one scalar)
- Parallel VECTORS vs collinear POINTS→ Collinear vectors and collinear points (one scalar)
- Coplanar / dependent ⇒ TWO scalars and a vanishing determinant→ Coplanar vectors (two scalars)
- Build the combination BEFORE you normalise — don't normalise the parts→ Forming a combination, then normalising / measuring it
- \"Parallel of magnitude \" has TWO answers —→ Forming a combination, then normalising / measuring it
- Use displacements that SHARE a point→ Collinearity of three points (and who lies between)
- Solve from two equations, but ALWAYS verify with the third→ Express a vector as a combination of two others
- Linearly dependent = coplanar = zero determinant — three names, one idea→ Linear dependence vs independence (the determinant test)
- A second condition (like ) is part of the same problem→ Linear dependence vs independence (the determinant test)
- Match coefficients only because the vectors are independent→ Chained-collinearity systems (\"no two collinear\")
- Read the target carefully: vs→ Chained-collinearity systems (\"no two collinear\")
- Angle bisector uses UNIT vectors, not the raw vectors→ A vector lying in the plane of two others
- Coplanar form first, condition second→ A vector lying in the plane of two others
- Equating components needs an INDEPENDENT basis→ Components of a vector against a transformed basis
- Answer the asked combination, not the raw→ Components of a vector against a transformed basis
Test yourself on Vectors
20 past MHT-CET questions from this chapter, timed at 36 minutes and marked the way the exam marks it. You see your score and every answer the moment you finish. Free to start.