Topics
Mathematical Logic
- Statements and Truth Values in Mathematical Logic
- Logical Connectives
- Tautology, Contradiction, and Contingency
- Quantifier, Quantified and Duality Statements in Logic
- Negations of Compound Statements
- Converse, Inverse, and Contrapositive
- Algebra of Statements
- Application of Logic to Switching Circuits
- Overview of Mathematical Logic
Matrices
Trigonometric Functions
Pair of Straight Lines
Vectors
Line and Plane
Linear Programming
Differentiation
- Introduction & Derivatives of Some Standard Functions
- Derivatives of Composite Functions
- Geometrical Meaning of Derivative
- Derivative of Inverse Function
- Logarithmic Differentiation
- Derivative of Implicit Functions
- Derivatives of Functions in Parametric Forms
- Higher Order Derivatives
- Overview of Differentiation
Applications of Derivatives
- Applications of Derivatives in Geometry
- Derivatives as a Rate Measure
- Approximations
- Rolle's Theorem
- Lagrange's Mean Value Theorem (LMVT)
- Increasing and Decreasing Functions
- Maxima and Minima
- Overview of Applications of Derivatives
Indefinite Integration
Definite Integration
- Definite Integral as Limit of Sum
- Integral Calculus
- Methods of Evaluation and Properties of Definite Integral
- Overview of Definite Integration
Application of Definite Integration
- Application of Definite Integration
- Area Bounded by Two Curves
- Overview of Application of Definite Integration
Differential Equations
- Basic Concepts of Differential Equations
- Order and Degree of a Differential Equation
- Formation of Differential Equations
- Methods of Solving Differential Equations> Homogeneous Differential Equations
- Methods of Solving Differential Equations>Linear Differential Equations
- Applications of Differential Equation
- Solution of a Differential Equation
- Overview of Differential Equations
Probability Distributions
- Random Variables
- Probability Distribution of Discrete Random Variables
- Probability Distribution of a Continuous Random Variable
- Variance of a Random Variable
- Expected Value and Variance of a Random Variable
- Overview of Probability Distributions
Binomial Distribution
Formula: Distance between Skew Lines
Vector Form:
\[\mathbf{d}=\left|\frac{(\overline{\mathbf{b}}_{1}\times\overline{\mathbf{b}}_{2}).(\overline{\mathbf{a}}_{2}-\overline{\mathbf{a}}_{1})}{\left|\overline{\mathbf{b}}_{1}\times\overline{\mathbf{b}}_{2}\right|}\right|\]
Cartesian Form:
\[\mathbf{d}=\left|\frac{ \begin{vmatrix} x_2-x_1 & y_2-y_1 & z_2-z_1 \\ \mathbf{a}_1 & \mathbf{b}_1 & \mathbf{c}_1 \\ \mathbf{a}_2 & \mathbf{b}_2 & \mathbf{c}_2 \end{vmatrix}}{\sqrt{\left(\mathbf{a}_1\mathbf{b}_2-\mathbf{a}_2\mathbf{b}_1\right)^2+\left(\mathbf{a}_1\mathbf{c}_2-\mathbf{a}_2\mathbf{c}_1\right)^2+\left(\mathbf{b}_1\mathbf{c}_2-\mathbf{b}_2\mathbf{c}_1\right)^2}}\right|\]
Notes
If two lines in space intersect at a point, then the shortest distance between them is zero. Also, if two lines in space are parallel, then the shortest distance between them will be the perpendicular distance, i.e. the length of the perpendicular drawn from a point on one line onto the other line. Further, in a space, there are lines which are neither intersecting nor parallel. In fact, such pair of lines are non coplanar and are called skew lines.Fig.
The line GE that goes diagonally across the ceiling and the line DB passes through one corner of the ceiling directly above A and goes diagonally down the wall. These lines are skew because they are not parallel and also never meet.
By the shortest distance between two lines we mean the join of a point in one line with one point on the other line so that the length of the segment so obtained is the smallest.
For skew lines, the line of the shortest distance will be perpendicular to both the lines.
Distance between two skew lines:
We now determine the shortest distance between two skew lines in the following way: Let `l_1` and `l_2` be two skew lines with equations in fig.
`vec r = vec a _1 + lambda vec b_1` ...(1)
and `vec r = vec a _2 + mu vec b _2` ...(2)
Take any point S on `l_1`with position vector `vec a_1` and T on `l_2`, with position vector `vec a_2`. Then the magnitude of the shortest distance vector will be equal to that of the projection of ST along the direction of the line of shortest distance.
If `vec (PQ)` is the shortest distance vector between `l_1` and `l_2` , then it being perpendicular to both `vec b_1` and `vec b_2` , the unit vector `hat n` along `vec (PQ)` would therefore be
`hat n = (vec b_1 xx vec b_2)/ |vec b_1 xx vec b_2|` ...(3)
Then `vec (PQ) = d . hat n `
where, d is the magnitude of the shortest distance vector. Let θ be the angle between `vec (ST)` and `vec (PQ).` Then
`PQ = ST |cos θ| `
But cos θ `= |(vec (PQ) . vec (ST))/(|vec (PQ)| |vec (ST)|)| `
`= |(d . hat n (vec a_2 - vec a_1)) / (d ST)|` (since `vec (ST) = vec a_2 - vec a_1`)
`= |((vec b _1 xx vec b_2) . (vec a_2 - vec a_1)) /( ST |vec b_1 xx vec b_2|)|` [From (3)]
Hence, the required shortest distance is
d = PQ =ST |cos θ|
or d=` |((vec b_1 xx vec b_2) . (vec a_2 xx vec a_1))/(|vec b_1 xx vec b_2|)|`
The shortest distance between the lines
`l_1 : (x - x_1)/a_1 = (y - y_1)/b_1 = (z - z_1)/c_1`
and `l_2 : (x - x_2)/a_2 = (y - y_2)/b_2 = (z - z_2)/c_2`
is `||(x_2-x_1 , y_2 - y_1 , z_2 - z_1),(a_1 , b_1 , c_1), (a_2 , b_2 , c_2)|/ sqrt ((b_1c_2 - b_2c_1)^2 + (c_1a_2 - c_2a_1)^2 + (a_1b_2 - a_2b_1)^2) |`
Video link : https://youtu.be/BXzj9mJvTKQ
Distance between parallel lines:
If two lines `l_1` and `l_2` are parallel, then they are coplanar. Let the lines be given by
`vec r = vec a_1 + lambda vec b` ...(1)
and `vec r = vec a_2 + mu vec b` ...(2)
where , `vec a_1` is the position vector of a point S on `l_1` and `vec a_2` is the position vector of a point T on `l_2` in following fig.

As `l_1, l_2` are coplanar, if the foot of the perpendicular from T on the line `l_1` is P, then the distance between the lines `l_1` and `l_2` = |TP|.
Let θ be the angle between the vectors `vec (ST)` and `vec b`. Then
`vec b xx vec (ST) = ( |vec b| |vec (ST)| sin θ) hat n ` ...(3)
where `hat n` is the unit vector perpendicular to the plane of the lines `l_1` and `l_2` But `vec (ST) = vec a_2 -vec a_1`
Therefore, from (3), we get
`vec b xx (vec a_2 - vec a_1) = |vec b| PT hat n` (since PT =ST sin θ )
i.e. `|vec b xx (vec a_2 - vec a_1)| = |vec b| PT . 1` (as `|hat n|` = 1)
Hence, the distance between the given parallel lines is
`d = |vec (PT)| = |(vec b xx (vec a_2 - vec a _1))/|vec b||`
Formula: Distance between Parallel Lines
\[SD=\left|\frac{\left(a_{2}-a_{1}\right)\times b}{\left|b\right|}\right|\]
