VCE Further Mathematics Units 3 and 4 Data Analysis
Course

VCE Further Mathematics Units 3 and 4 – Data Analysis

1.1 Descriptive Statistics
1.2 Representing Data
1.3 Exploring Data
1.4 Logarithms
1.5 Normal Distribution
1.6 Time Series

38 Lessons

Investigating data distributions, including:

  • review of types of data
  • review of representation, display and description of the distributions of categorical variables: data tables, two-way frequency tables and their associated segmented bar charts
  • use of the distribution/s of one or more categorical variables to answer statistical questions
  • review of representation, display and description of the distributions of numerical variables: dot plots, stem plots, histograms; the use of a log (base \(10\)) scale to display data ranging over several orders of magnitude and their interpretation in powers of ten
  • summary of the distributions of numerical variables; the five-number summary and boxplots (including the use of the lower fence \( \text{Q}1–1.5 \times \text{IQR} \) and upper fence \( \text{Q}3 + 1.5 \times \text{IQR} \) to identify and display possible outliers); the sample mean and standard deviation and their use in comparing data distributions in terms of centre and spread
  • use of the distribution/s of one or more numerical variables to answer statistical questions
  • the normal model for bell-shaped distributions and the use of the \( 68–95–99.7\% \) rule to estimate percentages and to give meaning to the standard deviation; standardised values (\(z\)-scores) and their use in comparing data values across distributions
  • population and sample, random numbers and their use to draw simple random samples from a population or randomly allocate subjects to groups, the difference between population parameters (e.g., \( \mu \) and \( \sigma \)), sample statistics (e.g., \( \overline{x} \) and \( s\) ).

Investigating associations between two variables, including:

  • response and explanatory variables and their role in investigating associations between variables
  • contingency (two-way) frequency tables, two-way frequency tables and their associated bar charts (including percentage segmented bar charts) and their use in identifying and describing associations between two categorical variables
  • back-to-back stem plots, parallel dot plots and boxplots and their use in identifying and describing associations between a numerical and a categorical variable
  • scatterplots and their use in identifying and qualitatively describing the association between two numerical variables in terms of direction (positive/negative), form (linear/non-linear) and strength (strong/moderate/weak)
  • answering statistical questions that require a knowledge of the associations between pairs of variables
  • Pearson correlation coefficient, r, its calculation and interpretation
  • cause and effect; the difference between observation and experimentation when collecting data and the need for experimentation to definitively determine cause and effect
  • non-causal explanations for an observed association including common response, confounding, and coincidence; discussion and communication of these explanations in a particular situation in a systematic and concise manner.

Investigating and modelling linear associations, including:

  • least squares line of best fit \( y = a + bx\), where \( x \) represents the explanatory variable and \( y \) represents the response variable; the determination of the coefficients \( a \) and \( b \) using technology, and the formulas \( \displaystyle b=r \frac{s_y}{s_x} \) and \( a = \overline{y}-b \overline{x} \)
  • modelling linear association between two numerical variables, including the:
    • identification of the explanatory and response variables
    • use of the least-squares method to fit a linear model to the data
  • interpretation of the slope and intercepts of the least-squares line in the context of the situation being modelled, including:
    • use of the rule of the fitted line to make predictions being aware of the limitations of extrapolation
    • use of the coefficient of determination, \( r^2 \), to assess the strength of the association in terms of explained variation
    • use of residual analysis to check the quality of fit
  • data transformation and its use in transforming some forms of non-linear data to linearity using a square, log or reciprocal transformation (on one axis only)
  • interpretation and use of the equation of the least-squares line fitted to the transformed data to make predictions.

Investigating and modelling time-series data, including:

  • qualitative features of time series plots; recognition of features such as trend (long-term direction), seasonality (systematic, calendar-related movements) and irregular fluctuations (unsystematic, short-term fluctuations); possible outliers and their sources, including one-off real-world events, and signs of a structural change such as a discontinuity in the time series
  • numerical smoothing of time series data using moving means with consideration of the number of terms required (using centring when appropriate) to help identify trends in time series plot with large fluctuations
  • graphical smoothing of time series plots using moving medians (involving an odd number of points only) to help identify long-term trends in time series with large fluctuations
  • seasonal adjustment including the use and interpretation of seasonal indices and their calculation using seasonal and yearly means
  • modelling trend by fitting a least-squares line to a time series with time as the explanatory variable (data de-seasonalised where necessary), and the use of the model to make forecasts (with re-seasonalisation where necessary) including consideration of the possible limitations of fitting a linear model and the limitations of extending into the future.

source – VCE Mathematics Study Design

DESCRIPTIVE STATISTICS

Types of Data

Discrete Data

Continuous Data

REPRESENTING DATA

Bar Graphs

Frequency Histogram and Frequency Polygons

Stem-and-Leaf Plots

Double Stem-and-Leaf Plots

Dot Plots

Box-and-Whisker Plots

Double Box-and-Whisker Plots

Scatter Plots

EXPLORING DATA

Mean

Standard Deviation

Comparing Spread using Standard Deviation

Variance and Standard Deviation

Effects of Standard Deviation

Mean and Standard Deviation of Binomial Random Variable

LOGARITHMS

Logarithms in Base 10

Logarithm Definition

Logarithmic Laws

Logarithmic Equations

Exponential Equations using Logarithms

Logarithm Change of Base Rule

NORMAL DISTRIBUTION

Normal Distribution

Probabilities using a Calculator

Standard Normal Distribution

z-Scores

Using z-Scores to Compare Data

Applications of Normal Distribution

Properties of Normal Distribution

Inverse Cumulative Normal Distribution Quantiles

Correlation Coefficient

Least Squares Regression Line