S-ID: Interpreting Categorical and Quantitative Data

S-ID.1: Represent data with plots on the real number line (dot plots, histograms, and box plots).

Box-and-Whisker Plots
Histograms
Mean, Median, and Mode

S-ID.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.

Box-and-Whisker Plots
Describing Data Using Statistics
Real-Time Histogram
Sight vs. Sound Reactions

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).

Mean, Median, and Mode
Reaction Time 2 (Graphs and Statistics)

S-ID.6: Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.

S-ID.6.a: Fit a function to the data; use functions fitted to data to solve problems in the context of the data.

Least-Squares Best Fit Lines
Solving Using Trend Lines

S-ID.6.b: Informally assess the fit of a function by plotting and analyzing residuals.

Least-Squares Best Fit Lines

S-ID.6.c: Fit a linear function for a scatter plot that suggests a linear association.

Least-Squares Best Fit Lines

S-ID.7: Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.

Cat and Mouse (Modeling with Linear Systems)

S-ID.8: Compute (using technology) and interpret the correlation coefficient of a linear fit.

Correlation

S-IC: Making Inferences and Justifying Conclusions

S-IC.4: Use data from a sample survey to estimate a population mean or proportion; develop a margin of error through the use of simulation models for random sampling.

Estimating Population Size
Polling: City
Polling: Neighborhood

S-IC.5: Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.

Real-Time Histogram
Sight vs. Sound Reactions

S-CP: Conditional Probability and the Rules of Probability

S-CP.1: Describe events as subsets of a sample space (the set of outcomes) using characteristics (or categories) of the outcomes, or as unions, intersections, or complements of other events (?or,? ?and,? ?not?).

Independent and Dependent Events
Probability Simulations
Theoretical and Experimental Probability

S-CP.2: Understand that two events ?? and ?? are independent if the probability of ?? and ?? occurring together is the product of their probabilities, and use this characterization to determine if they are independent.

Independent and Dependent Events

S-CP.3: Understand the conditional probability of ?? given ?? as ??(?? and ??)/??(??), and interpret independence of ?? and ?? as saying that the conditional probability of ?? given ?? is the same as the probability of ??, and the conditional probability of ?? given ?? is the same as the probability of ??.

Independent and Dependent Events

S-CP.9: Use permutations and combinations to compute probabilities of compound events and solve problems.

Binomial Probabilities
Permutations and Combinations

S-MD: Using Probability to Make Decisions

S-MD.3: Develop a probability distribution for a random variable defined for a sample space in which theoretical probabilities can be calculated; find the expected value.

Independent and Dependent Events
Probability Simulations
Theoretical and Experimental Probability

S-MD.4: Develop a probability distribution for a random variable defined for a sample space in which probabilities are assigned empirically; find the expected value.

Geometric Probability
Independent and Dependent Events
Probability Simulations
Theoretical and Experimental Probability