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STANDARDS

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US-SC

Math

South Carolina Math

Probability and Statistics: Interpreting Data

PS.SPID.1

Partially covered
Select and create an appropriate display, including dot plots, histograms, and box plots, for data that includes only real numbers.
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PS.SPID.2

Fully covered
Use statistics appropriate to the shape of the data distribution to compare center and spread of two or more different data sets that include all real numbers.
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PS.SPID.3

Not covered
Summarize and represent data from a single data set. Interpret differences in shape, center, and spread in the context of the data set, accounting for possible effects of extreme data points (outliers).
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PS.SPID.4

Partially covered
Use the mean and standard deviation of 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.
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PS.SPID.5

Mostly covered
Analyze bivariate categorical data using two-way tables and identify possible associations between the two categories using marginal, joint, and conditional frequencies.

PS.SPID.6

Not covered
Using technology, create scatterplots and analyze those plots to compare the fit of linear, quadratic, or exponential models to a given data set. Select the appropriate model, fit a function to the data set, and uses the function to solve problems in the context of the data.
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PS.SPID.7

Mostly covered
Find linear models using median fit and regression methods to make predictions. Interpret the slope and intercept of a linear model in the context of the data.
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PS.SPID.8

Fully covered
Compute using technology and interpret the correlation coefficient of a linear fit.
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PS.SPID.9

Fully covered
Differentiate between correlation and causation when describing the relationship between two variables. Identify potential lurking variables which may explain an association between two variables.
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PS.SPID.10

Partially covered
Create residual plots and analyze those plots to compare the fit of linear, quadratic, and exponential models to a given data set. Select the appropriate model and use it for interpolation.
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