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Common Core Algebra - Statistics Unit: Describing Data POSTERS

Common Core Standards
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30 Posters! Each 8.5" x 11" in size. Posters are aligned with Common Core's Algebra - Statistics: Describing Data Unit.

Posters Included:
Mean
Median
Mode
Range
Interquartile Range
Outlier
Mean Absolute Deviation
Measures of Center
Dot Plot
Histogram
Box Plot
Normal/ Bell Shaped Distribution
Skewed Right/Positive Skew
Skewed Left/ Negative Skew
Uniform
2 Way Frequency Table
Joint Frequency
Marginal Frequency
2 Way Relative Frequency Table
Conditional Relative Frequency
Scatter Plot
Correlation
Correlation Coefficient, r
Causation
Correlation ≠ Causation
Line of Best Fit/ Trend Line/ Regression Line
How to Create a Line of Best Fit
Slope/ Rate of Change
Y-intercept

Common Core Standards:
CCSS.HSS.ID.A.1 Represent data with plots on the real number line (dot plots, histograms, and box plots).
CCSS.HSS.ID.A.2 Use statistics appropriate to the shape of the data distribution to compare center and spread of two or more different data sets.
CCSS.HSS.ID.A.3 Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
CCSS.HSS.ID.B.5 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.
CCSS.HSS.ID.B.6 Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.
CCSS.HSS.ID.B.6c Using given or collected bivariate data, fit a linear function for a scatter plot that suggests a linear association.
CCSS.HSS.ID.C.7 Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.
CCSS.HSS.ID.C.8 Compute (using technology) and interpret the correlation coefficient “r” of a linear fit. (For instance, by looking at a scatterplot, students should be able to tell if the correlation coefficient is positive or negative and give a reasonable estimate of the “r” value.) After calculating the line of best fit using technology, students should be able to describe how strong the goodness of fit of the regression is, using “r”.
CCSS.HSS.ID.C.9 Distinguish between correlation and causation.

See similar resources:
Statistics: Representing and Interpreting Data(Box Plots, Histograms, Dot Plots) CARD SORT
Statistics: 2 Way Frequency Tables BINGO GAME
Statistics: Scatter Plots and Lines of Best Fit FOLDABLE
Statistics: Math Article (Case Studies of Bad Graphs found in the Media)
Statistics Project: U.S. Home Energy & Tax Themed Analysis of Real-World Data
Statistics: Vocabulary Activities
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Statistics: Line of Best Fit MYSTERY ACTIVITY (FREE!)

This topic is also covered in:
Algebra State Exam Prep: Study Guide w/ Notes and 2 Practice Tests

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