S-ID - Domain
Interpreting Categorical & Quantitative Data
High School Statistics & Probability · Common Core State Standards · Common Core 2010
Standard Unwrapping
AI-generated as a starting point — sign in to edit.Vocabulary
dataplotsreal number linedot plotshistogramsbox plotsstatisticsshapedata distributioncentermedianmeanspreadinterquartile rangestandard deviationdata setsshapecenterspreadextreme data pointsoutliersnormal distributionpopulation percentagescalculatorsspreadsheetstablesnormal curvecategorical datatwo categoriestwo-way frequency tablesrelative frequenciesjoint relative frequenciesmarginal relative frequenciesconditional relative frequenciesassociationstrendsscatter plotvariablesfunctionslinear modelsquadratic modelsexponential modelsresidualslinear associationsloperate of changeinterceptconstant termcorrelation coefficientcorrelationcausation
Skills
- Represent data (with plots on the real number line, such as dot plots, histograms, and box plots) #dok1
- Use statistics (appropriate to the shape of the data distribution to compare center and spread of data sets) #dok2
- Interpret differences (in shape, center, and spread in the context of data sets, accounting for extreme data points or outliers) #dok3
- Fit data (to a normal distribution using mean and standard deviation) #dok4
Learning Targets
- I can represent data using dot plots, histograms, and box plots. #dok1
- I can identify the center and spread of data sets. #dok1
- I can use appropriate statistics to compare different data sets based on their distributions. #dok2
- I can choose the correct measures of center and spread for comparison. #dok2
- I can interpret the impact of outliers and extreme points in data distribution. #dok3
- I can analyze differences in shape, center, and spread within data contexts. #dok3
- I can use the mean and standard deviation to fit data to a normal distribution. #dok4
- I can determine when a normal distribution model is not suitable for a data set. #dok4
- I can use technology to estimate areas under a normal curve. #dok4
Big Ideas
- Data can be visually represented to convey different aspects of distribution and trends.
- Analyzing statistical measures such as mean, median, and standard deviation provides insights into data sets.
Essential Questions
- How can different types of plots help represent data on a real number line?
- What role do median and mean play in comparing data sets?
- How do outliers affect the interpretation of a data set?
- In what situations is a normal distribution model inappropriate?
- How can relative frequencies reveal trends and associations in categorical data?