Resources |
PROBABILITY AND STATISTICS |
CC.7.SP.1 (1) Examine the statistics of a sample of a population to gain information about the entire population.
-Recognize and identify that different sampling techniques must be used in real life situations, because it is very difficult to survey an entire population. CC.7.SP.1 (2) Describe why random sampling must be representative of a population in order to support valid inferences. -Select appropriate sample sizes based on a population in real life situations and explain why generalizations about a population from a sample are valid only if the sample is. -Understand that random sampling tends to produce representative samples and support valid inferences. CC.7.SP.2 (1) Use data from a random sample to draw inferences about a population with an unknown characteristic or interest. -Make inferences about the whole population from a random sample. Ex: Estimate the number of words in a book by counting the words on random pages. Ex: Predict winner of an election. CC.7.SP.2 (2) Generate multiple samples (or simulated samples) of the same size to gauge the variation in estimates or predictions. -Generate multiple samples of the same size to gauge the variation in estimates or predictions. -Generate multiple simulated samples of the same size to gauge the variation in estimates or predictions. CC.7.SP.3 Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities, measuring the difference between the centers by expressing it as a multiple of a measure of variability. -Visually compare the medians of 2 data distributions. -Display numerical data in plots on a number line, including dot plots, and box plots. -Express difference between the centers of two data distributions as a multiple of a measure of variability. -Summarize numerical data sets in relation to their context, such as by: 1. Reporting the number of observations. 2. Describing the nature of the attribute under investigation, including how it was measured and its units of measurement giving quantitative measures of center (median and/or mean) and variability (inter-quartile range and/or mean absolute deviation), as well as describing any overall pattern and any striking deviations from the overall pattern with reference to the context in which the data were gathered. 3. Giving quantitative measures of center (median and/or mean) and variability (inter-quartile range and/or mean absolute deviation), as well as describing any overall pattern and any striking deviations from the overall pattern with reference to the context in which the data were gathered. CC.7.SP.4 Use measures of center and measures of variability for numerical data from random samples to draw informal comparative inferences about two populations. -Understand that a measure of center for a numerical data set summarizes all of its values with a single number, while a measure of variation describes how its values vary with a single number. -Find and use measures of variability from random samples to draw informal comparative inferences about two populations. Box & Whisker Plots & Dot Plots(formerly Line Plots) CC.7.SP.5 Define the probability of a chance event as a number between 0 and 1 that expresses the likelihood of the event occurring. CC.7.SP.6 Approximate the probability of a chance event by collecting data on the chance process that produces it and observing its long-run relative frequency, and predict the approximate relative frequency given the probability. For example, when rolling a number cube 600 times, predict that a 3 or 6 would be rolled roughly 200 times, but probably not exactly 200 times. CC.7.SP.7a Develop a uniform probability model by assigning equal probability to all outcomes, and use the model to determine probabilities of events. For example, if a student is selected at random from a class, find the probability that Jane will be selected and the probability that a girl will be selected. CC.7.SP.7b Develop a probability model (which may not be uniform) by observing frequencies in data generated from a chance process. For example, find the approximate probability that a spinning penny will land heads up or that a tossed paper cup will land open‐end down. Do the outcomes for the spinning penny appear to be equally likely based on the observed frequencies? CC.7.SP.8a Represent the probability of a compound event as a fraction of all the outcomes in the sample space for the event. CC.7.SP.8b Identify in everyday language the outcomes in the sample space which compose an event. (e.g., “rolling double sixes”) -Tree Diagrams -Tables -Organized Lists CC.7.SP.8c Design and use a simulation to generate frequencies for compound events. For example, use random digits as a simulation tool to approximate the answer to the question: If 40% of donors have type A blood, what is the probability that it will take at least 4 donors to find one with type A blood? |