Основно съдържание
Math
Common Core Math
High School: Statistics & Probability: Interpreting Categorical and Quantitative Data
Represent data with plots on the real number line (dot plots, histograms, and box plots).
- Comparing data distributions
- Create histograms
- Creating a histogram
- Creating box plots
- Creating dot plots
- Frequency tables & dot plots
- Interpreting a histogram
- Interpreting box plots
- Interpreting quartiles
- Judging outliers in a dataset
- Read histograms
- Reading box plots
- Reading box plots
- Reading dot plots & frequency tables
- Worked example: Creating a box plot (even number of data points)
- Worked example: Creating a box plot (odd number of data points)
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.
Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
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.
- Basic normal calculations
- Calculate percentiles
- Calculating percentile
- Calculating z-scores
- Comparing with z-scores
- Empirical rule
- Finding z-score for a percentile
- Normal calculations in reverse
- Normal distribution problems: Empirical rule
- Normal distribution: Area above or below a point
- Normal distribution: Area between two points
- Qualitative sense of normal distributions
- Standard normal table for proportion above
- Standard normal table for proportion below
- Standard normal table for proportion between values
- Threshold for low percentile
- Z-score introduction
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.
- Conditional distributions
- Conditional distributions and relationships
- Create two-way frequency tables
- Identify marginal and conditional distributions
- Interpret two-way tables
- Interpreting two-way tables
- Marginal and conditional distributions
- Marginal distributions
- Read two-way frequency tables
- Read two-way relative frequency tables
- Trends in categorical data
- Two-way frequency tables and Venn diagrams
- Two-way relative frequency tables
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.
Informally assess the fit of a function by plotting and analyzing residuals.
Fit a linear function for a scatter plot that suggests a linear association.
Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.
Compute (using technology) and interpret the correlation coefficient of a linear fit.
Distinguish between correlation and causation.