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*

Lessons

#### Box-and-Whisker Plots

- Video - Understanding Box-and-Whisker Plots (2:17)
- Video - Constructing Box-and-Whisker Plots (3:19)
- Video - Stem-and-Left Plots to Box-and-Whisker-Plots (3:19)
- Video - Drawing Box-and-Whisker Plots from Even Number of Scores (9:49)
- Video - Reading Box-and-Whisker Plots (6:40)
- Video - Drawing Box-and-Whisker Plots from Stem-and-Leaf Plots (8:37)
- Video - Drawing Box-and-Whisker Plots from Odd Number of Scores 1 (8:07)
- Video - Drawing Box-and-Whisker Plots from Odd Number of Scores 2 (2:09)

#### Mean

- Video - Calculating Mean (0:46)
- Video - Calculating Mean with Frequencies (0:47)
- Video - Calculating Mean using Calculators (3:59)
- Video - Calculating Mean with Frequencies using Calculators (7:39)
- Video - Calculating the Mean (2:31)
- Video - Calculating the Mean of Different Units (1:41)
- Video - Calculating New Mean by Adding Additional Scores (2:34)
- Video - Finding Unknown Scores from Mean (2:01)
- Video - Understanding Mean from UnGrouped Data (2:35)
- Video - Mean from UnGrouped Data by Frequency Table (1:03)
- Video - Missing Frequency Values from Frequency Table (3:02)
- Video - Finding Mean (2:51)
- Topic - Effects to Mean of Adding Constant to Each Score in Data Set
- Topic - Effects to Mean of Subtracting Constant to Each Score in Data Set
- Topic - Effects to Mean of Multiplying Constant to Each Score in Data Set
- Topic - Effects to Mean of Dividing Constant to Each Score in Data Set
- Topic - Effects to Mean of Mixed Operations to Each Score in Data Set

#### Standard Deviation

- Video - Calculating Standard Deviation using Tables (2:42)
- Video - Calculating Standard Deviation with Frequencies using Tables (2:32)
- Video - Calculating Standard Deviation using Calculators (5:13)
- Video - Calculating Standard Deviation with Frequencies using Calculators (7:25)
- Video - Standard Deviation and Data Consistency (1:26)

#### Variance and Standard Deviation

- Video - Finding the Variance (7:20)
- Video - Find the Variance using Expectation Theorems (5:11)
- Video - Finding unknown value given the Variance (6:13)
- Video - Standard Deviation and Dispersion (4:29)
- Video - Calculating Standard Deviation by Calculators (3:37)
- Video - Calculating Standard Deviation with Frequency by Calculators (3:56)
- Video - Standard Deviation and Consistency (1:39)
- Video - Evaluation by Standard Deviation (3:42)

#### Logarithms in Base 10

- Video - Simplifying Logarithms in base 10 (4:43)
- Video - Finding Exponents using Logarithms in base 10 (5:26)
- Video - Equations involving Logarithms in base 10 (2:36)
- Topic - Logarithms Base 10
- Topic - Logarithms Base 10 involving Decimal Numbers
- Topic - Logarithms Base 10 involving Surds
- Topic - Logarithms Base 10 involving Surds & Rational Form
- Topic - Logarithms Base 10 involving Indices
- Topic - Logarithms Base 10 involving Equations

#### Logarithmic Laws

- Video - Addition of Logarithms (7:29)
- Video - Subtraction of Logarithms (3:51)
- Video - Evaluation of Logarithms using Logarithmic Laws (7:04)
- Video - Simplifying Logarithms using Addition & Subtraction Laws (1:17)
- Video - Indices involving Logarithms (3:38)
- Video - Expressing Logarithms using Letters (2:28)
- Video - Zero Value involving Logarithms (4:04)
- Video - Unity of Logarithms (8:20)
- Video - Evaluating Multiple Logarithms (3:35)
- Video - Evaluating using Unity of Logarithms (5:20)
- Video - Interchangeable Logarithmic Forms (3:09)
- Video - Evaluation of Logarithmic Values (3:03)

#### Logarithmic Equations

- Video - Logarithmic Equations with Same Bases (5:51)
- Video - Logarithmic Equations with Different Bases (9:02)
- Video - Equations involving Definition of Logarithm (5:01)
- Video - Variables in Logarithms (3:57)
- Video - Variables in Base (5:58)
- Video - Variables in Other Side (3:20)
- Video - Variables in Multiple Terms (7:03)
- Video - Converting to an Index Form (4:42)
- Video - Multiple Terms (4:27)
- Video - Inverse of Natural Logarithmic Functions (6:21)
- Video - Logarithmic Literal Equations (5:58)

#### Exponential Equations using Logarithms

- Video - Equations involving Natural Logarithms (3:22)
- Video - Index Equations involving Logarithms (5:05)
- Video - Equations involving Logarithms (2:37)
- Topic - Indicial Equations by Logarithms: Bases > 1
- Topic - Indicial Equations by Logarithms: Bases < 1
- Topic - Indicial Equations by Logarithms: Bases > 1 and Non-Monic Indices
- Topic - Indicial Equations by Logarithms: Bases < 1 and Non-Monic Indices
- Topic - Indicial Equations by Logarithms: Two Indicial Expressions

#### Normal Distribution

- Topic - Drawing Normal Distribution Curves
- Topic - Percentage of Shaded Regions of Normal Distribution Curves
- Topic - Percentage of Certain Intervals
- Topic - Percentage of Certain Scores 1
- Topic - Percentage of Certain Scores 2
- Topic - Scores Based on Certain Percentages
- Topic - Scores Based on Above Certain Percentages
- Topic - Scores Based on Below Certain Percentages

#### Properties of Normal Distribution

- Video - Understanding Normal Distribution Curve (7:15)
- Video - Percentages or Probabilities from Normal Distribution Curve (9:46)
- Video - z-Scores and Associated Regions of Normal Distribution Curve (2:24)
- Video - z-Scores using Description from Normal Distribution Curve (3:02)
- Video - Find the Range of Data from Normal Distribution Curve (3:47)

#### Inverse Cumulative Normal Distribution Quantiles

- Topic - Match Corresponding Probabilities and Areas
- Topic - Find Quantiles for Certain Probabilities
- Topic - Find Quantiles for Complement Probabilities
- Topic - Find Negative Quantiles for Certain Probabilities
- Topic - Find Negative Quantiles for Complement Probabilities
- Topic - Find Quantiles for between k-values
- Topic - Find Quantiles for Non-Standard Normal Distribution