Standard
Benchmark
Indicator | Description | Lesson Plans | Thinkfinity | Resources |
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4
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The student uses concepts and procedures of data analysis in a variety of situations.
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4.1
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The student applies probability theory to draw conclusions, generate convincing arguments, make predictions, and decisions, and analyze decisions including the use of concrete objects in a variety of situations.
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4.1.A1
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conducts an experiment or simulation with two dependent events; records the results in charts, tables, or graphs; and uses the results to generate convincing arguments, draw conclusions and make predictions (2.4.A1h-i).
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4.1.K1
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finds the probability of two independent events in an experiment, simulation, or situation (2.4.K1k) ($).
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4.1.K2
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finds the conditional probability of two dependent events in an experiment, simulation, or situation (2.4.K1k).
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4.1.A2
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uses theoretical or empirical probability of a simple or compound event composed of two or more simple, independent events to make predictions and analyze decisions about real-world situations including:
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4.1.A2A
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work in economics, quality control, genetics, meteorology, and other areas of science (2.4.A1a);
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4.1.A2B
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games (2.4.A1a);
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4.1.A2c
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situations involving geometric models, e.g., spinners or dartboards (2.4.A1f).
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4.1.A3
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compares theoretical probability (expected results) with empirical probability (experimental results) of two independent and/or dependent events and understands that the larger the sample size, the greater the likelihood that experimental results will match theoretical probability (2.4.A1h).
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4.1.K3
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explains the relationship between probability and odds and computes one given the other (2.4.K1a, k).
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4.1.A4
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uses conditional probabilities of two dependent events in an experiment, simulation, or situation to make predictions and analyze decisions.
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4.2
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The student collects, organizes, displays, explains, and interprets numerical (rational numbers) and non numerical data sets in a variety of situations with a special emphasis on measures of central tendency.
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4.2.A1
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uses data analysis (mean, median, mode, range, quartile, interquartile range) in real-world problems with rational number data sets to compare and contrast two sets of data, to make accurate inferences and predictions, to analyze decisions, and to develop convincing arguments from these data displays (2.4.A1i) ($):
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4.2.A1A
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frequency tables and line plots;
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4.2.A1B
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bar, line, and circle graphs;
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4.2.A1C
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Venn diagrams or other pictorial displays;
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4.2.A1D
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charts and tables;
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4.2.A1E
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stem-and-leaf plots (single and double);
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4.2.A1F
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scatter plots
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4.2.A1G
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box-and-whiskers plots;
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4.2.A1H
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histograms.
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4.2.K1
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organizes, displays, and reads quantitative (numerical) and qualitative (non-numerical) data in a clear, organized, and accurate manner including a title, labels, categories, and rational number intervals using these data displays (2.4.K1l).
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4.2.K1A
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frequency tables;
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4.2.K1B
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bar, line, and circle graphs;
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4.2.K1C
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Venn diagrams or other pictorial displays;
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4.2.K1D
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charts and tables;
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4.2.K1E
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stem-and-leaf plots (single and double);
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4.2.K1F
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scatter plots;
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4.2.K1G
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box-and-whiskers plots;
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4.2.K1H
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histograms.
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4.2.K2
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explains how the reader’s bias, measurement errors, and display distortions can affect the interpretation of data.
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4.2.A2
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determines and describes appropriate data collection techniques (observations, surveys, or interviews) and sampling techniques (random sampling, samples of convenience, biased sampling, census of total population, or purposeful sampling) in a given situation.
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4.2.A3
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uses changes in scales, intervals, and categories to help support a particular interpretation of the data (2.4.A1i).
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4.2.K3
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calculates and explains the meaning of range, quartiles and interquartile range for a real number data set (2.4.K1a).
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4.2.K4
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explains the effects of outliers on the measures of central tendency (mean, median, mode) and range and interquartile range of a real number data set (2.4.K1a).
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4.2.A4
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determines and explains the advantages and disadvantages of using each measure of central tendency and the range to describe a data set (2.4.K1i).
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4.2.A5
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analyzes the effects of: outliers on the mean, median, and range of a real number data set, changes within a real number data set on mean, median, mode, range, quartiles, and interquartile range.
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4.2.K5
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approximates a line of best fit given a scatter plot and makes predictions using the equation of that line (2.4.K1k).range of a real number data set (2.4.K1a).
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4.2.K6
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compares and contrasts the dispersion of two given sets of data in terms of range and the shape of the distribution including (2.4.K1k):
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4.2.K6A
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symmetrical (including normal),
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4.2.K6B
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skew (left or right),
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4.2.K6C
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bimodal,
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4.2.K6D
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uniform (rectangular).
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4.2.A6
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approximates a line of best fit given a scatter plot, makes predictions, and analyzes decisions using the equation of that line (2.4.A1i).
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