Data collection and analysis - advanced

This lesson comprises three (3) master classes focusing on:

• Calculating data spread and location
• Bivariate data and scatter plots
• Line of best fit
• Analysing time series data

Content:

MA5-DAT-C-01

Examine standard deviation as a measure of spread

• Identify standard deviation as a measure of spread
• Calculate the standard deviation of a small dataset using digital tools
• Compare small datasets using standard deviation

Determine quartiles and interquartile range

• Determine the 5-number summary for sets of numerical data
• Determine the 5-number summary from graphical representations
• Determine the interquartile range (IQR) for datasets
• Compare and explain the relative merits of range and IQR as measures of spread

Represent datasets using box plots and use them to compare datasets

• Represent numerical datasets using a box plot to display the median, upper and lower quartiles, and maximum and minimum values
• Compare 2 or more numerical datasets using parallel box plots drawn on the same scale
• Compare and contrast the centres, spreads and shapes of 2 or more numerical datasets, using box plots and numerical statistics, including the 5-number summary
• Determine quartiles from datasets displayed in histograms and dot plots, and represent these as a box plot
• Identify and describe skewness or symmetry of datasets displayed in histograms, dot plots and box plots
• Interpret box plots to draw conclusions and make inferences about the dataset

MA5-DAT-C-02

Identify and describe numerical datasets involving 2 variables

• Distinguish between situations involving 1-variable and 2-variable (bivariate) data and explain when each is needed
• Explain the difference between variables that have an association and variables that have a causal relationship
• Identify and describe the independent variable and dependent variable in relationships with possible cause and effect

Represent datasets involving 2 numerical variables, using a scatter plot and a line of best fit, by eye

• Gather data on a topic of interest involving 2 numerical variables
• Represent the data using a scatter plot
• Create a line of best fit, by eye, on an existing scatter plot

Interpret data involving 2 numerical variables, using graphical representations

• Describe informally the association between 2 numerical variables and apply terminology about form (linear), strength (strong, moderate or weak) and direction (positive or negative)
• Use the line of best fit, by eye, to make predictions between known data values (interpolation) and what might happen beyond known data values (extrapolation)
• Explain the limitations of the model when making predictions