All Lessons

(B) Determine the mean absolute deviation and use this quantity as a measure of the average distance data are from the mean using a data set of no more than 10 data points

(B) Analyze numerical data using measures of central tendency (mean, median, and mode) and variability (range, interquartile range or IQR, and standard deviation) in order to make inferences with normal distributions

(Y10A) Calculate and interpret the mean and standard deviation of data and use these to compare data sets (ACMSP278)

(Y11-12) Calculate and interpret statistical measures of spread, such as the range, interquartile range and standard deviation (ACMEM055)

HSS.IDA.2 Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets.

(A) Represent numeric data graphically, including dot plots, stem-and-leaf plots, histograms, and box plots

(Y11-12) Display numerical data as frequency distributions, dot plots, stem and leaf plots, and histograms (ACMEM046)

HSS.IDA.1 Represent data with plots on the real number line (dot plots, histograms, and box plots).

(D) Summarize categorical data with numerical and graphical summaries, including the mode, the percent of values in each category (relative frequency table), and the percent bar graph, and use these summaries to describe the data distribution.

(Y9) Calculate relative frequencies from given or collected data to estimate probabilities of events involving 'and' or 'or' (ACMSP226)

(Y11-12) Identify relative frequency as probability (ACMEM152)

HSS.IDA.5 Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data.

(A) Compare two groups of numeric data using comparative dot plots or box plots by comparing their shapes, centers, and spreads

(Y9) Compare data displays using mean, median and range to describe and interpret numerical data sets in terms of location (centre) and spread (ACMSP283)

(Y10) Construct and interpret box plots and use them to compare data sets (ACMSP249)

(Y10A) Calculate and interpret the mean and standard deviation of data and use these to compare data sets (ACMSP278)

(Y11-12) Compare back-to-back stem plots for different datasets (ACMEM057)

HSS.IDA.2 Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets.

(B) Write, using technology, quadratic functions that provide a reasonable fit to data to estimate solutions and make predictions for real-world problems.

(E) Write, using technology, exponential functions that provide a reasonable fit to data and make predictions for real-world problems.

(Y11-12) Find the line of best fit by eye (ACMEM141)

(Y11-12) Use the line of best fit to make predictions, both by interpolation and extrapolation (ACMEM145)

HSS.IDA.6.A Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Use given functions or choose a function suggested by the context. Emphasize linear, quadratic, and exponential models.

(C) Compare different linear models for the same set of data to determine best fit, including discussions about error

(Y11-12) Find the line of best fit by eye (ACMEM141)

(Y11-12) Use technology to find the line of best fit (ACMEM142)

HSS.IDA.6.B Informally assess the fit of a function by plotting and analyzing residuals.

(A) Calculate, using technology, the correlation coefficient between two quantitative variables and interpret this quantity as a measure of the strength of the linear association

(Y11-12) Use technology to find the correlation coefficient (an indicator of the strength of linear association) (ACMEM144)

HSS.IDA.8 Compute (using technology) and interpret the correlation coefficient of a linear fit.