• GA: HS: Stats/Prob
    Interpreting Categorical & Quantitative Data
    S-ID Summarize, represent, and interpret data on a single count or measurement variable
     
    MGSE9-12.S.ID.1 Represent data with plots on the real number line (dot plots, histograms, and box plots).
     
     
    MGSE9-12.S.ID.2 Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, mean absolute deviation, standard deviation) of two or more different data sets.
     
     
    MGSE9-12.S.ID.3 Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
     
    S-ID Summarize, represent, and interpret data on two categorical and quantitative variables
     
    MGSE9-12.S.ID.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.
     
     
    MGSE9-12.S.ID.6 Represent data on two quantitative variables on a scatter plot and describe how the variables are related.
     
     
    MGSE9-12.S.ID.6a Decide which type of function is most appropriate by observing graphed data, charted data, or by analysis of context to generate a viable (rough) function of best fit. Use this function to solve problems in context. Emphasize linear, quadratic and exponential models.
     
     
    MGSE9-12.S.ID.6c Using given or collected bivariate data, fit a linear function for a scatter plot that suggests a linear association.
     
    S-ID Interpret linear models
     
    MGSE9-12.S.ID.7 Interpret the slope (rate of change) and the intercept (constant term) of a linear fit in the context of the data.
     
     
    MGSE9-12.S.ID.8 Compute (using technology) and interpret the correlation coefficient “r” of a linear fit. (For instance, by looking at a scatterplot, students should be able to tell if the correlation coefficient ispositive or negative and give a reasonable estimate of the “r” value.) After calculating the line of best fit using technology, students should be able to describe how strong the goodness of fit of the regression is, using “r”.
     
     
    MGSE9-12.S.ID.9 Distinguish between correlation and causation.
  • Key Vocabulary 

    Association • Bivariate Data • Box Plot • Box-and-Whisper Plot • Categorical Variables • Center • Conditional Frequencies • Correlation Coefficient • Dot Plot • First Quartile • Five-Number Summary • Histogram • Interquartile Range • Joint Frequencies • Line of Best Fit • Marginal Frequencies • Mean Absolute Deviation • Outlier • Quantitative Variables • Scatter Plot • Second Quartile • Shape • Symmetry • Number of Peaks • Direction of Skew • Uniformity • Spread • Third Quartile • Trend • Two-Way Frequency Table