Adaptive Perseverance: Students will tackle complex data sets and unpredictable outcomes, adjusting their approaches as needed.
Critical Thinking: Evaluate data and probability models to make informed predictions and decisions.
Communication: Clearly present findings and predictions based on data analysis and probability, using appropriate graphs and statistical language.
Collaboration: Work in teams to collect data, analyze findings, and present conclusions, leveraging diverse perspectives for a more comprehensive analysis.
How can we determine the most effective method of data collection to ensure our analysis of central tendencies and probability is accurate and meaningful?
In what ways do multiplication and division of fractions reveal deeper insights into the relationship between parts and wholes in practical scenarios?
How can visualizing data through graphs enhance our understanding of central tendencies and probability in making predictions?
Students are able to convert quantitative problems that use words into mathematical expressions.
CCSS.MATH.CONTENT.6.SP.A.1 Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.
CCSS.MATH.CONTENT.7.SP.C.5 Understand that the probability of a chance event is a number between 0 and 1 that expresses the likelihood of the event occurring.
CCSS.MATH.CONTENT.7.SP.C.6 Approximate the probability of a chance event by collecting data on the chance process that produces it and observing its long-term relative frequency.
CCSS.MATH.CONTENT.HSS.ID.A.1 Represent data with plots on the real number line (dot plots, histograms, and box plots).
CCSS.MATH.CONTENT.HSS.ID.A.2 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.
CCSS.MATH.CONTENT.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).
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