TANCET MCA Important Quantitative Aptitude Topics

Will you please provide me the Important topics of Quantitative Aptitude for TANCET MCA Exam??

[Kiran Chandar]

Tamil Nadu Common Entrance Test - TANCET Exam is conducted for admission in MBA, MCA, ME and M.Tech courses

TANCET – MCA Important Topics of Quantitative Aptitude:
Algebra
Arithmetic
Geometry
Trigonometry
Permutation & Combination
Statistics & Probability

Syllabus for Quantitative Aptitude:

Algebra:

Fundamental operations in Algebra, Expansion, factorization, simultaneous linear / quadratic equations, indices, logarithms, arithmetic, geometric and harmonic progressions, binomial theorem, permutations and combinations, surds, determinants, matrices and application to solution of simultaneous linear equations.

Co-ordinate Geometry:

Rectangular Cartesian co-ordinates, equations of a line, mid point,intersections etc., equations of a circle, distance formulae, pair of straight lines, parabola,ellipse and hyperbola, simple geometric transformations such as translation, rotation, scaling.

Calculus:

Limit of functions, continuous functions, differentiation of functions(s),Tangents and normal, simple examples of maxima and minima, Integration of function by parts, by substitution and by partial fraction, definite integral application to volumes and surfaces of frustums of a sphere, cone, cylinder, Taylor Series.

Differential Equations:

Differential equations of first order and their solutions, linear differential equations with constant coefficients, homogenous linear differential equations.

Vector:

Position vector, additions and subtraction of vectors, scalar and vector products and their applications to simple geometrical problems and mechanics.

Trigonometry:

Simple identities, trigonometric equations, properties of triangles, solution of triangles, height and distance, inverse function

Probability and Statistics:

Basic concepts of probability theory, Averages, Dependent and independent events, frequency distributions, and measures of dispersions, skewness and kurtosis, random variable and distribution functions, mathematical expectations, Binomial, Poisson, normal distributions, curve fitting, and principle of least squares, correlation and regression.

Linear Programming:

Formulation of simple linear programming problems, basic concepts of graphical and simplex methods