
Editable PowerPoint slides on scatter plots for Years 8 to 10, covering how to plot paired data, identify positive, negative and no correlation, add a line of best fit, and read trends from a real data set.

Scatter plot slides for every confidence level. Introductory tasks start with reading plotted points and naming the direction of association; later tasks ask students to construct a scatter plot from a data table, sketch the line of best fit and write a one-sentence interpretation.


Walk classes through scatter plots with worked examples on the board. Slides build from a blank pair of axes to a finished plot, then prompt students to describe form, direction, strength and outliers using the same language Khan and the textbooks use.
Hand students a data set, ask them to plot it, then have them argue what the pattern means. Slides include height and arm-span, study time and test score, and ice-cream sales and temperature, so the statistical concept lands against something familiar.

Mid-lesson practice questions move from "name the type of correlation" to "plot, fit a line, and predict the next value." Worked solutions sit on the next slide so you can reveal one step at a time or assign the deck for independent practice.
Visual scaffolds break a scatter plot into four readable parts: the axes and scale, the plotted points, the trend, and the outliers. Students stop guessing and start describing what they see in words their teacher can mark against.
A short end-of-topic check covers reading a scatter plot, constructing one from data, describing the relationship, and the difference between correlation and causation. Use it as an exit ticket or as the spine of a 10-minute quiz.
- You in approximately four minutes
Plotting and reading scatter plots
Teach students how to create and interpret scatterplots for data analysis with engaging powerpoint slides. Explain the purpose of scatterplots in visualising the relationship between two variables. Use visual aids to demonstrate how to plot data points and interpret patterns such as positive correlation, negative correlation, and no correlation. Provide real-life examples like analysing trends in sales data, studying the relationship between height and weight, and examining scientific data. Interactive activities where students create scatterplots from given data sets and interpret their patterns will enhance their understanding and practical skills in data analysis.
Correlation, causation and the line of best fit
The first half of the deck teaches students how to plot paired data on a labelled set of axes, then how to read what the cloud of points is saying. Slides cover choosing a sensible scale, plotting (x, y) pairs accurately, and describing the shape of the data using the four standard descriptors: form (linear or non-linear), direction (positive, negative or none), strength (strong, moderate or weak), and any outliers. Worked examples use familiar pairings — hours studied and test score, height and arm-span, temperature and ice-cream sales — so the procedure sticks before students attempt their own.
Predicting trends and making decisions from data
The closing slides move beyond reading a scatter plot to using one. Students draw a line of best fit by eye, use it to predict a value the data set does not contain, and then critique the prediction — is the line trustworthy, is the prediction inside or outside the plotted range, and what would change the answer? A short worked example on advertising spend and sales shows the same workflow in a real context, and the final slide separates correlation from causation with a clear non-example so students do not over-claim what their plot proves.