GRE General Test- Quantitative Reasoning Syllabus

GRE has two main components, Verbal and Quantitative Reasoning. The Quantitative Reasoning section contains questions related to some basic high-school math. The questions are posed in real-life settings and are easily comprehensible. It does not include any high-level mathematics nor does it give questions which take a lot of time to solve. This section contains 40 questions and awards 170 marks for your efforts. For each right answer, you receive 1 point.

It is quite evident that just like the verbal section, the quant section is also equally important. But first, let’s get a good idea of what the ETS GRE tests through the quantitative reasoning section.

  • Skills in basic mathematics,
  • Understanding of elementary math,
  • Ability to reason quantitatively,
  • And ways of modelling and solving problems using various quantitative methods.

Now, let’s face another question that has been bugging you, the problem of preparing well for the quantitative section. For a start, you need to know the syllabus of GRE Quantitative Reasoning section. The syllabus for GRE is quite vast and with incomplete information, one can end up wasting a lot of precious time.

Download GRE Quantitative Reasoning Syllabus.

GRE Quantitative Reasoning Syllabus

The questions asked in GRE quantitative reasoning section can be broadly divided into four main categories.

Arithmetic

  1. Integers
  2. Fractions
  3. Exponents and Roots
  4. Decimals
  5. Real Numbers
  6. Ratio
  7. Percent

Algebra

  1. Operations with Algebraic Expression
  2. Rules of Exponents
  3. Solving Linear Equations
  4. Solving Quadratic Equations
  5. Solving Linear Inequalities
  6. Functions
  7. Applications
  8. Coordinate Geometry
  9. Graphs of Functions

Geometry

  1. Lines and Angles
  2. Polygons
  3. Triangles
  4. Quadrilaterals
  5. Circles
  6. Three-Dimensional Figures

Data Analysis

  1. Graphical Methods for Describing Data
  2. Numerical Methods for Describing Data
  3. Counting Methods
  4. Probability
  5. Distributions of Data, Random Variables, and Probability Distributions
  6. Data Interpretation Examples

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