Data and Statistic Internet Sites for further study
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MA.4.3.1 Represent data with plots on the real number line (dot plots, histograms, and box plots). |
Construct appropriate graphical displays (dot plots, histogram, and box plot) to describe sets of data values. |
Represent Data with Plots
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MA.4.3.2 Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers). |
Understand and be able to use the context of the data to explain why its distribution takes on a particular shape (e.g. are there real-life limits to the values of the data that force skewness? are there outliers?)
Understand that the higher the value of a measure of variability, the more spread out the data set is.
Explain the effect of any outliers on the shape, center, and spread of the data sets. |
Interpreting Categorical and Quantitative Data https://www.shmoop.com/common-core-standards/ccss-hs-s-id-3.html
https://www.thirteen.org/get-the-math/teachers/math-in-restaurants-lesson-plan/standards/187/
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MA.4.3.3 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. |
Create a two-way frequency table from a set of data on two categorical variables. Calculate joint, marginal, and conditional relative frequencies and interpret in context. Joint relative frequencies are compound probabilities of using AND to combine one possible outcome of each categorical variable (P(A and B)). Marginal relative frequencies are the probabilities for the outcomes of one of the two categorical variables in a two-way table, without considering the other variable. Conditional relative frequencies are the probabilities of one particular outcome of a categorical variable occurring, given that one particular outcome of the other categorical variable has already occurred. Recognize associations and trends in data from a two-way table. |
Interpreting Quantitative and Categorical data https://www.ct4me.net/Common-Core/hsstatistics/hss-interpreting-categorical-quantitative-data.htm
https://ccssmath.org/?page_id=2341
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MA.4.3.4 Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data. |
Understand that the key feature of a linear function is a constant rate of change. Interpret in the context of the data, i.e. as x increases (or decreases) by one unit, y increases (or decreases) by a fixed amount. Interpret the y-intercept in the context of the data, i.e. an initial value or a one-time fixed amount. |
Interpreting Slope and Intercepts
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MA.4.3.5 Distinguish between correlation and causation. |
Understand that just because two quantities have a strong correlation, we cannot assume that the explanatory (independent) variable causes a change in the response (dependent) variable. The best method for establishing causation is conducting an experiment that carefully controls for the effects of lurking variables (if this is not feasible or ethical, causation can be established by a body of evidence collected over time e.g. smoking causes cancer). |
Correlation and Causation https://learnzillion.com/lessonsets/585-distinguish-between-correlation-and-causation
https://abspd.appstate.edu/node/377 accessed 1/4/2015
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