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DP IB Maths: AI SL

Revision Notes

Home / IB / Maths: AI SL / DP / Revision Notes / 4. Statistics & Probability / 4.2 Correlation & Regression / 4.2.1 Bivariate data


4.2.1 Bivariate data


Scatter Diagrams

What does bivariate data mean?

  • Bivariate data is data which is collected on two variables and looks at how one of the factors affects the other
    • Each data value from one variable will be paired with a data value from the other variable
    • The two variables are often related, but do not have to be

What is a scatter diagram?

  • A scatter diagram is a way of graphing bivariate data
    • One variable will be on the x-axis and the other will be on the y-axis
    • The variable that can be controlled in the data collection is known as the independent or explanatory variable and is plotted on the x-axis
    • The variable that is measured or discovered in the data collection is known as the dependent or response variable and is plotted on the y-axis
  • Scatter diagrams can contain outliers that do not follow the trend of the data

Exam Tip

  • If you use scatter diagrams in your Internal Assessment then be aware that finding outliers for bivariate data is different to finding outliers for univariate data
    • (x, y) could be an outlier for the bivariate data even if x and y are not outliers for their separate univariate data

Correlation

What is correlation?

  • Correlation is how the two variables change in relation to each other
    • Correlation could be the result of a causal relationship but this is not always the case
  • Linear correlation is when the changes are proportional to each other
  • Perfect linear correlation means that the bivariate data will all lie on a straight line on a scatter diagram
  • When describing correlation mention
    • The type of the correlation
      • Positive correlation is when an increase in one variable results in the other variable increasing
      • Negative correlation is when an increase in one variable results in the other variable decreasing
      • No linear correlation is when the data points don’t appear to follow a trend
    • The strength of the correlation
      • Strong linear correlation is when the data points lie close to a straight line
      • Weak linear correlation is when the data points are not close to a straight line
  • If there is strong linear correlation you can draw a line of best fit (by eye)
    • The line of best fit will pass through the mean point left parenthesis x with bar on top comma space y with bar on top right parenthesis
    • If you are asked to draw a line of best fit
      • Plot the mean point
      • Draw a line going through it that follows the trend of the data

2-4-1-correlation-diagram-1

What is the difference between correlation and causation?

  • It is important to be aware that just because correlation exists, it does not mean that the change in one of the variables is causing the change in the other variable
    • Correlation does not imply causation!
  • If a change in one variable causes a change in the other then the two variables are said to have a causal relationship
    • Observing correlation between two variables does not always mean that there is a causal relationship
      • There could be underlying factors which is causing the correlation
    • Look at the two variables in question and consider the context of the question to decide if there could be a causal relationship
      • If the two variables are temperature and number of ice creams sold at a park then it is likely to be a causal relationship
      • Correlation may exist between global temperatures and the number of monkeys kept as pets in the UK but they are unlikely to have a causal relationship

Worked Example

A teacher is interested in the relationship between the number of hours her students spend on a phone per day and the number of hours they spend on a computer. She takes a sample of nine students and records the results in the table below.

Hours spent on a phone per day

7.6

7.0

8.9

3.0

3.0

7.5

2.1

1.3

5.8

Hours spent on a computer per day

1.7

1.1

0.7

5.8

5.2

1.7

6.9

7.1

3.3

a)
Draw a scatter diagram for the data.

4-2-1-ib-ai-sl-correlation-a-we-solution

b)
Describe the correlation.

4-2-1-ib-ai-sl-correlation-b-we-solution

c)
Draw a line of best fit.

sPCr4WYi_4-2-1-ib-ai-sl-correlation-c-we-solution



  • 1. Number & Algebra
    • 1.1 Number Toolkit
      • 1.1.1 Standard Form
        • 1.1.2 Exponents & Logarithms
          • 1.1.3 Approximation & Estimation
            • 1.1.4 GDC: Solving Equations
            • 1.2 Sequences & Series
              • 1.2.1 Language of Sequences & Series
                • 1.2.2 Arithmetic Sequences & Series
                  • 1.2.3 Geometric Sequences & Series
                    • 1.2.4 Applications of Sequences & Series
                    • 1.3 Financial Applications
                      • 1.3.1 Compound Interest & Depreciation
                        • 1.3.2 Amortisation & Annuities
                      • 2. Functions
                        • 2.1 Linear Functions & Graphs
                          • 2.1.1 Equations of a Straight Line
                          • 2.2 Further Functions & Graphs
                            • 2.2.1 Functions
                              • 2.2.2 Graphing Functions
                                • 2.2.3 Properties of Graphs
                                • 2.3 Modelling with Functions
                                  • 2.3.1 Linear & Piecewise Models
                                    • 2.3.2 Quadratic & Cubic Models
                                      • 2.3.3 Exponential Models
                                        • 2.3.4 Direct & Inverse Variation
                                          • 2.3.5 Sinusoidal Models
                                            • 2.3.6 Strategy for Modelling Functions
                                          • 3. Geometry & Trigonometry
                                            • 3.1 Geometry Toolkit
                                              • 3.1.1 Coordinate Geometry
                                                • 3.1.2 Arcs & Sectors
                                                • 3.2 Geometry of 3D Shapes
                                                  • 3.2.1 3D Coordinate Geometry
                                                    • 3.2.2 Volume & Surface Area
                                                    • 3.3 Trigonometry
                                                      • 3.3.1 Pythagoras & Right-Angled Triganometry
                                                        • 3.3.2 Non Right-Angled Trigonometry
                                                          • 3.3.3 Applications of Trigonometry & Pythagoras
                                                          • 3.4 Voronoi Diagrams
                                                            • 3.4.1 Voronoi Diagrams
                                                              • 3.4.2 Toxic Waste Dump Problem
                                                            • 4. Statistics & Probability
                                                              • 4.1 Statistics Toolkit
                                                                • 4.1.1 Sampling & Data Collection
                                                                  • 4.1.2 Statistical Measures
                                                                    • 4.1.3 Frequency Tables
                                                                      • 4.1.4 Linear Transformations of Data
                                                                        • 4.1.5 Outliers
                                                                          • 4.1.6 Univariate Data
                                                                            • 4.1.7 Interpreting Data
                                                                            • 4.2 Correlation & Regression
                                                                              • 4.2.1 Bivariate data
                                                                                • 4.2.2 Correlation Coefficients
                                                                                  • 4.2.3 Linear Regression
                                                                                  • 4.3 Probability
                                                                                    • 4.3.1 Probability & Types of Events
                                                                                      • 4.3.2 Conditional Probability
                                                                                        • 4.3.3 Sample Space Diagrams
                                                                                        • 4.4 Probability Distributions
                                                                                          • 4.4.1 Discrete Probability Distributions
                                                                                            • 4.4.2 Expected Values
                                                                                            • 4.5 Binomial Distribution
                                                                                              • 4.5.1 The Binomial Distribution
                                                                                                • 4.5.2 Calculating Binomial Probabilities
                                                                                                • 4.6 Normal Distribution
                                                                                                  • 4.6.1 The Normal Distribution
                                                                                                    • 4.6.2 Calculations with Normal Distribution
                                                                                                    • 4.7 Hypothesis Testing
                                                                                                      • 4.7.1 Hypothesis Testing
                                                                                                        • 4.7.2 Chi-squared Test for Independence
                                                                                                          • 4.7.3 Goodness of Fit Test
                                                                                                            • 4.7.4 The t-test
                                                                                                          • 5. Calculus
                                                                                                            • 5.1 Differentiation
                                                                                                              • 5.1.1 Introduction to Differentiation
                                                                                                                • 5.1.2 Applications of Differentiation
                                                                                                                  • 5.1.3 Modelling with Differentiation
                                                                                                                  • 5.2 Integration
                                                                                                                    • 5.2.1 Trapezoid Rule: Numerical Integration
                                                                                                                      • 5.2.2 Introduction to Integration
                                                                                                                        • 5.2.3 Applications of Integration
                                                                                                                      Daniel Finlay

                                                                                                                      Author: Daniel

                                                                                                                      Dan graduated from the University of Oxford with a First class degree in mathematics. As well as teaching maths for over 8 years, Dan has marked a range of exams for Edexcel, tutored students and taught A Level Accounting. Dan has a keen interest in statistics and probability and their real-life applications.


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