St a t i st i c s a n d Pr o b a b i l i t y
Interpreting Categorical and Q uantitative Data
Summarize, represent, and in te rp re t d a ta on a single count or m easurem ent v a riab le
S-ID.A.1 Represent data w ith plots on the real number line (dot plots, histograms, and box plots).
S-ID.A.2 Use statistics appropriate to the shape o f the data distribution to compare center (median, mean) and spread
(interquartile range, standard deviation) o f tw o or more different data sets.
S-ID.A.3 Interpret differences in shape, center, and spread in the context o f the data sets, accounting for possible effects
o f extreme data points (outliers).
S-ID.A.4 Use the mean and standard deviation o f a data set to fit it to a normal distribution and to estimate population
percentages. Recognize that there are data sets for which such a procedure is not appropriate. Use calculators,
spreadsheets, and tables to estimate areas under the normal curve.
Summarize, represent, and interpret data on tw o categorical and quantitative variables
S-ID.B.5 Summarize categorical data for tw o categories in tw o-w ay frequency tables. Interpret relative frequencies in
the context o f the data (including joint, marginal, and conditional relative frequencies). Recognize possible
associations and trends in the data.
S-ID.B.6 Represent data on tw o quantitative variables on a scatter plot, and describe how the variables are related.
S-ID.B.6a 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.
S-ID.B.6b Informally assess the fit of a function by plotting and analyzing residuals.
S-ID.B.6c Fit a linear function for a scatter plot th a t suggests a linear association.
Interpret linear models
S-ID.C.7 Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context o f the data.
5-ID.C.8 Compute (using technology) and interpret the correlation coefficient of a linear fit.
S-ID.C.9 Disinguish between correlation and causation.
M aking Inferences and Justifying Conclusions
M ake inferences and ju s tify conclusions fro m sam ple surveys, experim ents, and observatio nal studies
S-IC.B.3 Recognize the purposes o f and differences among sample surveys, experiments, and observational studies;
explain how randomization relates to each.
S-IC.B.5 Use data from a randomized experiment to compare tw o treatments; use simulations to decide if differences
between parameters are significant.
C onditional P robability and th e Rules o f P robability
U nderstand independence and conditional p ro b a b ility and use th e m to in te rp re t data
S-CP.A. 1 Describe events as subsets o f a sample space (the set of outcomes) using characteristics (or categories) o f the
outcomes, or as unions, intersections, or complements of other events ("o r,” "a n d," "n o t").
S-CP.A.4 Construct and interpret two-way frequency tables o f data when tw o categories are associated w ith each
object being classified. Use the tw o-w ay table as a sample space to decide if events are independent and to
approximate conditional probabilities.
Use th e rules of p ro b a b ility to com pute pro b a b ilitie s o f com pound events in a uniform p ro b a b ility m odel
S-CP.B.7 Apply the Addition Rule, P(A or B) = P(A) + P(B) - P(A and 8), and interpret the answer in terms of the model.
S-CP.B.8* ( + ) Apply the general M ultiplication Rule in a uniform probability model, P{A and 6) = P(A)P(B \ A) =
P{B)P{A 18), and interpret the answer in terms o f the model.
Using Your Book f or Success xxiii