StudySmarter - The all-in-one study app.

4.8 • +11k Ratings

More than 3 Million Downloads

Free

Suggested languages for you:

Americas

Europe

Quantitative Variables

- Calculus
- Absolute Maxima and Minima
- Absolute and Conditional Convergence
- Accumulation Function
- Accumulation Problems
- Algebraic Functions
- Alternating Series
- Antiderivatives
- Application of Derivatives
- Approximating Areas
- Arc Length of a Curve
- Area Between Two Curves
- Arithmetic Series
- Average Value of a Function
- Calculus of Parametric Curves
- Candidate Test
- Combining Differentiation Rules
- Combining Functions
- Continuity
- Continuity Over an Interval
- Convergence Tests
- Cost and Revenue
- Density and Center of Mass
- Derivative Functions
- Derivative of Exponential Function
- Derivative of Inverse Function
- Derivative of Logarithmic Functions
- Derivative of Trigonometric Functions
- Derivatives
- Derivatives and Continuity
- Derivatives and the Shape of a Graph
- Derivatives of Inverse Trigonometric Functions
- Derivatives of Polar Functions
- Derivatives of Sec, Csc and Cot
- Derivatives of Sin, Cos and Tan
- Determining Volumes by Slicing
- Direction Fields
- Disk Method
- Divergence Test
- Eliminating the Parameter
- Euler's Method
- Evaluating a Definite Integral
- Evaluation Theorem
- Exponential Functions
- Finding Limits
- Finding Limits of Specific Functions
- First Derivative Test
- Function Transformations
- General Solution of Differential Equation
- Geometric Series
- Growth Rate of Functions
- Higher-Order Derivatives
- Hydrostatic Pressure
- Hyperbolic Functions
- Implicit Differentiation Tangent Line
- Implicit Relations
- Improper Integrals
- Indefinite Integral
- Indeterminate Forms
- Initial Value Problem Differential Equations
- Integral Test
- Integrals of Exponential Functions
- Integrals of Motion
- Integrating Even and Odd Functions
- Integration Formula
- Integration Tables
- Integration Using Long Division
- Integration of Logarithmic Functions
- Integration using Inverse Trigonometric Functions
- Intermediate Value Theorem
- Inverse Trigonometric Functions
- Jump Discontinuity
- Lagrange Error Bound
- Limit Laws
- Limit of Vector Valued Function
- Limit of a Sequence
- Limits
- Limits at Infinity
- Limits at Infinity and Asymptotes
- Limits of a Function
- Linear Approximations and Differentials
- Linear Differential Equation
- Linear Functions
- Logarithmic Differentiation
- Logarithmic Functions
- Logistic Differential Equation
- Maclaurin Series
- Manipulating Functions
- Maxima and Minima
- Maxima and Minima Problems
- Mean Value Theorem for Integrals
- Models for Population Growth
- Motion Along a Line
- Motion in Space
- Natural Logarithmic Function
- Net Change Theorem
- Newton's Method
- Nonhomogeneous Differential Equation
- One-Sided Limits
- Optimization Problems
- P Series
- Particle Model Motion
- Particular Solutions to Differential Equations
- Polar Coordinates
- Polar Coordinates Functions
- Polar Curves
- Population Change
- Power Series
- Radius of Convergence
- Ratio Test
- Removable Discontinuity
- Riemann Sum
- Rolle's Theorem
- Root Test
- Second Derivative Test
- Separable Equations
- Separation of Variables
- Simpson's Rule
- Solid of Revolution
- Solutions to Differential Equations
- Surface Area of Revolution
- Symmetry of Functions
- Tangent Lines
- Taylor Polynomials
- Taylor Series
- Techniques of Integration
- The Fundamental Theorem of Calculus
- The Mean Value Theorem
- The Power Rule
- The Squeeze Theorem
- The Trapezoidal Rule
- Theorems of Continuity
- Trigonometric Substitution
- Vector Valued Function
- Vectors in Calculus
- Vectors in Space
- Washer Method
- Decision Maths
- Geometry
- 2 Dimensional Figures
- 3 Dimensional Vectors
- 3-Dimensional Figures
- Altitude
- Angles in Circles
- Arc Measures
- Area and Volume
- Area of Circles
- Area of Circular Sector
- Area of Parallelograms
- Area of Plane Figures
- Area of Rectangles
- Area of Regular Polygons
- Area of Rhombus
- Area of Trapezoid
- Area of a Kite
- Composition
- Congruence Transformations
- Congruent Triangles
- Convexity in Polygons
- Coordinate Systems
- Dilations
- Distance and Midpoints
- Equation of Circles
- Equilateral Triangles
- Figures
- Fundamentals of Geometry
- Geometric Inequalities
- Geometric Mean
- Geometric Probability
- Glide Reflections
- HL ASA and AAS
- Identity Map
- Inscribed Angles
- Isometry
- Isosceles Triangles
- Law of Cosines
- Law of Sines
- Linear Measure and Precision
- Median
- Parallel Lines Theorem
- Parallelograms
- Perpendicular Bisector
- Plane Geometry
- Polygons
- Projections
- Properties of Chords
- Proportionality Theorems
- Pythagoras Theorem
- Rectangle
- Reflection in Geometry
- Regular Polygon
- Rhombuses
- Right Triangles
- Rotations
- SSS and SAS
- Segment Length
- Similarity
- Similarity Transformations
- Special quadrilaterals
- Squares
- Surface Area of Cone
- Surface Area of Cylinder
- Surface Area of Prism
- Surface Area of Sphere
- Surface Area of a Solid
- Surface of Pyramids
- Symmetry
- Translations
- Trapezoids
- Triangle Inequalities
- Triangles
- Using Similar Polygons
- Vector Addition
- Vector Product
- Volume of Cone
- Volume of Cylinder
- Volume of Pyramid
- Volume of Solid
- Volume of Sphere
- Volume of prisms
- Mechanics Maths
- Acceleration and Time
- Acceleration and Velocity
- Angular Speed
- Assumptions
- Calculus Kinematics
- Coefficient of Friction
- Connected Particles
- Conservation of Mechanical Energy
- Constant Acceleration
- Constant Acceleration Equations
- Converting Units
- Elastic Strings and Springs
- Force as a Vector
- Kinematics
- Newton's First Law
- Newton's Law of Gravitation
- Newton's Second Law
- Newton's Third Law
- Power
- Projectiles
- Pulleys
- Resolving Forces
- Statics and Dynamics
- Tension in Strings
- Variable Acceleration
- Work Done by a Constant Force
- Probability and Statistics
- Bar Graphs
- Basic Probability
- Charts and Diagrams
- Conditional Probabilities
- Continuous and Discrete Data
- Frequency, Frequency Tables and Levels of Measurement
- Independent Events Probability
- Line Graphs
- Mean Median and Mode
- Mutually Exclusive Probabilities
- Probability Rules
- Probability of Combined Events
- Quartiles and Interquartile Range
- Systematic Listing
- Pure Maths
- ASA Theorem
- Absolute Value Equations and Inequalities
- Addition and Subtraction of Rational Expressions
- Addition, Subtraction, Multiplication and Division
- Algebra
- Algebraic Fractions
- Algebraic Notation
- Algebraic Representation
- Analyzing Graphs of Polynomials
- Angle Measure
- Angles
- Angles in Polygons
- Approximation and Estimation
- Area and Circumference of a Circle
- Area and Perimeter of Quadrilaterals
- Area of Triangles
- Argand Diagram
- Arithmetic Sequences
- Average Rate of Change
- Bijective Functions
- Binomial Expansion
- Binomial Theorem
- Chain Rule
- Circle Theorems
- Circles
- Circles Maths
- Combination of Functions
- Combinatorics
- Common Factors
- Common Multiples
- Completing the Square
- Completing the Squares
- Complex Numbers
- Composite Functions
- Composition of Functions
- Compound Interest
- Compound Units
- Conic Sections
- Construction and Loci
- Converting Metrics
- Convexity and Concavity
- Coordinate Geometry
- Coordinates in Four Quadrants
- Cubic Function Graph
- Cubic Polynomial Graphs
- Data transformations
- De Moivre's Theorem
- Deductive Reasoning
- Definite Integrals
- Deriving Equations
- Determinant of Inverse Matrix
- Determinants
- Differential Equations
- Differentiation
- Differentiation Rules
- Differentiation from First Principles
- Differentiation of Hyperbolic Functions
- Direct and Inverse proportions
- Disjoint and Overlapping Events
- Disproof by Counterexample
- Distance from a Point to a Line
- Divisibility Tests
- Double Angle and Half Angle Formulas
- Drawing Conclusions from Examples
- Ellipse
- Equation of Line in 3D
- Equation of a Perpendicular Bisector
- Equation of a circle
- Equations
- Equations and Identities
- Equations and Inequalities
- Estimation in Real Life
- Euclidean Algorithm
- Evaluating and Graphing Polynomials
- Even Functions
- Exponential Form of Complex Numbers
- Exponential Rules
- Exponentials and Logarithms
- Expression Math
- Expressions and Formulas
- Faces Edges and Vertices
- Factorials
- Factoring Polynomials
- Factoring Quadratic Equations
- Factorising expressions
- Factors
- Finding Maxima and Minima Using Derivatives
- Finding Rational Zeros
- Finding the Area
- Forms of Quadratic Functions
- Fractional Powers
- Fractional Ratio
- Fractions
- Fractions and Decimals
- Fractions and Factors
- Fractions in Expressions and Equations
- Fractions, Decimals and Percentages
- Function Basics
- Functional Analysis
- Functions
- Fundamental Counting Principle
- Fundamental Theorem of Algebra
- Generating Terms of a Sequence
- Geometric Sequence
- Gradient and Intercept
- Graphical Representation
- Graphing Rational Functions
- Graphing Trigonometric Functions
- Graphs
- Graphs and Differentiation
- Graphs of Common Functions
- Graphs of Exponents and Logarithms
- Graphs of Trigonometric Functions
- Greatest Common Divisor
- Growth and Decay
- Growth of Functions
- Highest Common Factor
- Hyperbolas
- Imaginary Unit and Polar Bijection
- Implicit differentiation
- Inductive Reasoning
- Inequalities Maths
- Infinite geometric series
- Injective functions
- Instantaneous Rate of Change
- Integers
- Integrating Polynomials
- Integrating Trig Functions
- Integrating e^x and 1/x
- Integration
- Integration Using Partial Fractions
- Integration by Parts
- Integration by Substitution
- Integration of Hyperbolic Functions
- Interest
- Inverse Hyperbolic Functions
- Inverse Matrices
- Inverse and Joint Variation
- Inverse functions
- Iterative Methods
- Law of Cosines in Algebra
- Law of Sines in Algebra
- Laws of Logs
- Limits of Accuracy
- Linear Expressions
- Linear Systems
- Linear Transformations of Matrices
- Location of Roots
- Logarithm Base
- Logic
- Lower and Upper Bounds
- Lowest Common Denominator
- Lowest Common Multiple
- Math formula
- Matrices
- Matrix Addition and Subtraction
- Matrix Determinant
- Matrix Multiplication
- Metric and Imperial Units
- Misleading Graphs
- Mixed Expressions
- Modulus Functions
- Modulus and Phase
- Multiples of Pi
- Multiplication and Division of Fractions
- Multiplicative Relationship
- Multiplying and Dividing Rational Expressions
- Natural Logarithm
- Natural Numbers
- Notation
- Number
- Number Line
- Number Systems
- Numerical Methods
- Odd functions
- Open Sentences and Identities
- Operation with Complex Numbers
- Operations with Decimals
- Operations with Matrices
- Operations with Polynomials
- Order of Operations
- Parabola
- Parallel Lines
- Parametric Differentiation
- Parametric Equations
- Parametric Integration
- Partial Fractions
- Pascal's Triangle
- Percentage
- Percentage Increase and Decrease
- Percentage as fraction or decimals
- Perimeter of a Triangle
- Permutations and Combinations
- Perpendicular Lines
- Points Lines and Planes
- Polynomial Graphs
- Polynomials
- Powers Roots And Radicals
- Powers and Exponents
- Powers and Roots
- Prime Factorization
- Prime Numbers
- Problem-solving Models and Strategies
- Product Rule
- Proof
- Proof and Mathematical Induction
- Proof by Contradiction
- Proof by Deduction
- Proof by Exhaustion
- Proof by Induction
- Properties of Exponents
- Proportion
- Proving an Identity
- Pythagorean Identities
- Quadratic Equations
- Quadratic Function Graphs
- Quadratic Graphs
- Quadratic functions
- Quadrilaterals
- Quotient Rule
- Radians
- Radical Functions
- Rates of Change
- Ratio
- Ratio Fractions
- Rational Exponents
- Rational Expressions
- Rational Functions
- Rational Numbers and Fractions
- Ratios as Fractions
- Real Numbers
- Reciprocal Graphs
- Recurrence Relation
- Recursion and Special Sequences
- Remainder and Factor Theorems
- Representation of Complex Numbers
- Rewriting Formulas and Equations
- Roots of Complex Numbers
- Roots of Polynomials
- Roots of Unity
- Rounding
- SAS Theorem
- SSS Theorem
- Scalar Triple Product
- Scale Drawings and Maps
- Scale Factors
- Scientific Notation
- Second Order Recurrence Relation
- Sector of a Circle
- Segment of a Circle
- Sequences
- Sequences and Series
- Series Maths
- Sets Math
- Similar Triangles
- Similar and Congruent Shapes
- Simple Interest
- Simplifying Fractions
- Simplifying Radicals
- Simultaneous Equations
- Sine and Cosine Rules
- Small Angle Approximation
- Solving Linear Equations
- Solving Linear Systems
- Solving Quadratic Equations
- Solving Radical Inequalities
- Solving Rational Equations
- Solving Simultaneous Equations Using Matrices
- Solving Systems of Inequalities
- Solving Trigonometric Equations
- Solving and Graphing Quadratic Equations
- Solving and Graphing Quadratic Inequalities
- Special Products
- Standard Form
- Standard Integrals
- Standard Unit
- Straight Line Graphs
- Substraction and addition of fractions
- Sum and Difference of Angles Formulas
- Sum of Natural Numbers
- Surds
- Surjective functions
- Tables and Graphs
- Tangent of a Circle
- The Quadratic Formula and the Discriminant
- Transformations
- Transformations of Graphs
- Translations of Trigonometric Functions
- Triangle Rules
- Triangle trigonometry
- Trigonometric Functions
- Trigonometric Functions of General Angles
- Trigonometric Identities
- Trigonometric Ratios
- Trigonometry
- Turning Points
- Types of Functions
- Types of Numbers
- Types of Triangles
- Unit Circle
- Units
- Variables in Algebra
- Vectors
- Verifying Trigonometric Identities
- Writing Equations
- Writing Linear Equations
- Statistics
- Bias in Experiments
- Binomial Distribution
- Binomial Hypothesis Test
- Bivariate Data
- Box Plots
- Categorical Data
- Categorical Variables
- Central Limit Theorem
- Chi Square Test for Goodness of Fit
- Chi Square Test for Homogeneity
- Chi Square Test for Independence
- Chi-Square Distribution
- Combining Random Variables
- Comparing Data
- Comparing Two Means Hypothesis Testing
- Conditional Probability
- Conducting a Study
- Conducting a Survey
- Conducting an Experiment
- Confidence Interval for Population Mean
- Confidence Interval for Population Proportion
- Confidence Interval for Slope of Regression Line
- Confidence Interval for the Difference of Two Means
- Confidence Intervals
- Correlation Math
- Cumulative Distribution Function
- Cumulative Frequency
- Data Analysis
- Data Interpretation
- Degrees of Freedom
- Discrete Random Variable
- Distributions
- Dot Plot
- Empirical Rule
- Errors in Hypothesis Testing
- Estimator Bias
- Events (Probability)
- Frequency Polygons
- Generalization and Conclusions
- Geometric Distribution
- Histograms
- Hypothesis Test for Correlation
- Hypothesis Test for Regression Slope
- Hypothesis Test of Two Population Proportions
- Hypothesis Testing
- Inference for Distributions of Categorical Data
- Inferences in Statistics
- Large Data Set
- Least Squares Linear Regression
- Linear Interpolation
- Linear Regression
- Measures of Central Tendency
- Methods of Data Collection
- Normal Distribution
- Normal Distribution Hypothesis Test
- Normal Distribution Percentile
- Paired T-Test
- Point Estimation
- Probability
- Probability Calculations
- Probability Density Function
- Probability Distribution
- Probability Generating Function
- Quantitative Variables
- Quartiles
- Random Variables
- Randomized Block Design
- Residual Sum of Squares
- Residuals
- Sample Mean
- Sample Proportion
- Sampling
- Sampling Distribution
- Scatter Graphs
- Single Variable Data
- Skewness
- Spearman's Rank Correlation Coefficient
- Standard Deviation
- Standard Error
- Standard Normal Distribution
- Statistical Graphs
- Statistical Measures
- Stem and Leaf Graph
- Sum of Independent Random Variables
- Survey Bias
- T-distribution
- Transforming Random Variables
- Tree Diagram
- Two Categorical Variables
- Two Quantitative Variables
- Type I Error
- Type II Error
- Types of Data in Statistics
- Variance for Binomial Distribution
- Venn Diagrams

Have you ever thought of finding the number of male and female students in your college?

Or have you ever thought about measuring the weight or height of your classmates, or recording the ages of your classmates to determine who is the youngest or oldest in your class?

All these are forms of data that can be counted and/or measured and represented in a numerical form. In statistics, these data are called **quantitative variables. **

In this article, we are going to study deeper into quantitative variables and how they compare to another type of variable, the qualitative variables.

Quantitative variables are variables whose values are counted.

Examples of quantitative variables are height, weight, number of goals scored in a football match, age, length, time, temperature, exam score, etc.

Qualitative variables (also known as categorical variables) are variables that fit into categories and descriptions instead of numbers and measurements. Their values do not result from counting.

Examples of qualitative variables include hair color, eye color, religion, political affiliation, preferences, feelings, beliefs, etc.

**Quantitative variables** are divided into two types: **discrete quantitative variables** and **continuous quantitative variables**. Details and differences between these two types of quantitative variables are explained hereafter.

**Discrete quantitative variables** are quantitative variables that take values that are countable and have a *finite number of values*. The values are often but not always integers.

The best way to tell whether a data set represents discrete quantitative variables is when the variables are countable and the number of possibilities is finite.

**Continuous quantitative variables **are quantitative variables whose values are not countable.

The best way to tell whether a data set represents continuous quantitative variables is when the variables occur in an interval.

A **discrete quantitative variable** is a variable whose values are obtained by counting.

A **continuous quantitative variable** is a variable whose values are obtained by measuring.

When you count the number of goals scored in a sports game or the number of times a phone rings, this is a discrete quantitative variable.

When you measure the volume of water in a tank or the temperature of a patient, this is a continuous quantitative variable.

The table below contains examples of discrete quantitative and continuous quantitative variables,

Discrete quantitative variables | Continuous quantitative variables |

Number of children per household | Weight |

Number of students in a college | Speed of cars in a race |

Number of goals scored in a football match | Height |

Number of correct questions answered in exams | Temperature |

Number of people who took part in an election | Time |

Number of students in a school | Density |

Distinguish the types of the following variables between discrete and continuous.

- Time taken for an athlete to complete a race,
- Depth of a river,
- Numbers of students present at school,
- Number of pets owned,

**Solution**

Continuous variables.

- The time taken for an athlete to complete a race, in order to see this, let us think of this situation as if we start a watch for an athlete to complete a 5000m race. From the start of the watch to the end of the race, the athlete might take 15 minutes:10 seconds:3milliseconds:5microseconds and so on depending on the precision of the stopwatch. This makes it a continuous variable.
- Depth of a river: a river may be 5m:40cm:4mm deep. Thus, the depth of a river is a continuous variable.

Discrete variables.

- Number of students present at school: this is discrete because it will always involve direct whole numbers in counting the number of students in school. We can have 1, 2, 3, 4, ...............200 students for instance present at school with a consistent interval of +1. We can never have 5.5 students or anything like that at any point. This makes it a discrete variable.
- The explanation above applies to the number of pets owned.

Primary data is the data collected by a researcher to address a problem at hand, which is classified into qualitative data and quantitative data.

Qualitative variables deal with descriptions that can be noticed but not calculated.

Quantitative variables focus on amounts/numbers that can be calculated.

✓ Both quantitative and qualitative data are used in research and analysis.

✓ Both are used in conjunction to ensure that the data gathered is free from errors.

✓Both can be obtained from the same data unit. Only their variables are different, i.e. numerical variables in case of quantitative data and categorical variables in case of qualitative data.

Quantitative variable | Qualitative variable |

Can be counted and expressed in numbers and values. | Cannot be counted but contains a classification of objects based on attributes, features, and characteristics. |

The research methodology is conclusive in nature and aims at testing a specific hypothesis to determine the relationships. | The research methodology is exploratory, that is it provides insights and understanding. |

Has a focused approach and is objective. | The research approach is subjective. |

Uses statistical analysis methods of analysis. | The analysis is non-statistical. |

Ascertains the level of occurrence. | Determines the depth of understanding |

Sample size is large and drawn from the representative sample. | The sample size is usually small and is drawn from non-representative samples. |

Methods of data collection include experiments, surveys, and measurements. | Methods of data collection include interviews, focus groups, observation, and archival materials like newspapers. |

Examples include height, weight, age, exam scores, etc. | Examples include opinions, beliefs, eye color, description, etc. |

Determine if the following variables are quantitative or qualitative variables,

- hair color
- time
- gender
- distance in kilometers
- temperature
- music genre

**Solution**

Qualitative variables.

- Hair color: hair colors can be
**grouped**into various categories; whether you have blonde hair, brunette, red, or black. In a family of 5 people, 2 may have blonde hair, 2 may be brunette, 1 red, and 0 black and we can classify the people according to their hair colors. Therefore it is a categorical variable. - Gender: this is a categorical variable because obviously, each person falls under a particular gender based on certain characteristics. A person may be a male, female, or fall under any other gender category. If there are 20 workers in a company and we want to group them according to gender, we may have 15 females and 5 males. This makes gender a qualitative variable.
- Music genre: there are different genres to classify music. Either Jazz, Rock, Hip hop, Reggae, etc.

Quantitative variables.

These are the variables that can be counted or measured.

- time in minutes: it might take a student 10 hours to finish studying this topic. Here, we are interested in the numerical value of how long it can take to finish studying a topic. This makes the time a quantitative variable.
- Temperature in degrees Celsius: the temperature of a room in degrees Celsius is a quantitative variable as it is measured and recorded in numerical as say 25, 26, or 30 degrees Celsius.
- Distance in kilometers: this is also quantitative as it requires a certain numerical value in the unit given (kilometers).
Note that the distance as a quantitative variable is given in kilometers or measurable units otherwise distance may be described as short, long, or very long which then will make the variable qualitative/categorical.

Quantitative variables can generally be represented through graphs. There are many types of graphs that can be used to present distributions of quantitative variables.

**✓ Stem and leaf displays/plot. **A graphical type of display used to visualize quantitative data. Stem and leaf plots organize quantitative data and make it easier to determine the frequency of different types of values.

**✓ Histograms.** A type of graph that summarizes quantitative data that are continuous, meaning they a quantitative dataset that is measured on an interval. Histograms represent the distinctive characteristics of the data in a user-friendly and understandable manner.

**✓ Frequency polygons.** A line graph used for a visual representation of quantitative variables. Frequency polygons indicate shapes of distributions and are useful for comparing sets of data. In this type of data visualization, the data are plotted on a graph and a line is drawn connecting points to each other to understand the shape of the variables.

**✓ Box plots. **A graphical representation method for quantitative data that indicate the spread, skewness, and locality of the data through quartiles. Box plots are also known as whisker plots, and they show the distribution of numerical data through percentiles and quartiles.

**✓ Bar charts. A** graph in the form of rectangles of equal widths with their heights/lengths representing values of quantitative data. A bar graph/chart makes quantitative data easier to read as they convey information about the data in an understandable and comparable manner. The horizontal axis of a bar graph is called the y-axis while the vertical axis is the x-axis. Bar graphs make a comparison between data easier and more understandable.

**✓ Line graphs.** This is a line or curve that connects a series of quantitative data points called ‘markers’ on a graph. Similar to box plots and frequency polygons, line graphs indicate a continuous change in quantitative data and track changes over short and long periods of time.

**✓ Scatter plots.** Scatter plots use cartesian coordinates to show values for two variables for a set of data. Scatter plots basically show whether there is a correlation or relationship between the sets of data.

Note that some graph types such as stem and leaf displays are suitable for small to moderate amounts of data, while others such as histograms and bar graphs are suitable for large amounts of data. Graph types such as box plots are good when showing differences between distributions. Scatter plots are used to show the relationship or correlation between two variables.

- Quantitative variables are variables whose values result from counting or measuring something.
- Quantitative variables are divided into two types: discrete and continuous variables.
- Discrete variables take values that are countable and have a finite number of values.
- Continuous variables are variables whose values are not countable and have an infinite number of possibilities.
- Examples of methods for presenting quantitative variables include

The three types of quantitative variables are discrete, continuous, and mixed quantitative variables

Quantitative variables are variables whose values are counted.

Quantitative variables are variables whose values are counted.

More about Quantitative Variables

Be perfectly prepared on time with an individual plan.

Test your knowledge with gamified quizzes.

Create and find flashcards in record time.

Create beautiful notes faster than ever before.

Have all your study materials in one place.

Upload unlimited documents and save them online.

Identify your study strength and weaknesses.

Set individual study goals and earn points reaching them.

Stop procrastinating with our study reminders.

Earn points, unlock badges and level up while studying.

Create flashcards in notes completely automatically.

Create the most beautiful study materials using our templates.

Sign up to highlight and take notes. It’s 100% free.

Over 10 million students from across the world are already learning smarter.

Get Started for Free