AIM HIGH SCHOOL. Curriculum Map W. 12 Mile Road Farmington Hills, MI (248)

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1 AIM HIGH SCHOOL Curriculum Map 2923 W. 12 Mile Road Farmington Hills, MI (248) COURSE TITLE: Statistics DESCRIPTION OF COURSE: PREREQUISITES: Algebra 2 Students will learn basic concepts of probability and statistical inference, focusing on an intuitive approach to understanding concepts and methodologies. Get an introduction to statistical and critical thinking. Including descriptive statistics, probability, sampling distributions, interval estimation, hypothesis testing and regression. Every student should understand and use all concepts and skills from the previous grade levels. The standard is designed so that new learning builds on preceding skills.

2 CONTENT CORE CONCEPTS ASSESSMENTS Unit 1 o Probability experiment o Casino Games Probability o Outcome o Monopoly Project o Sample space o Simple Events o Fundamental Counting Principal o Empirical probability o Subjective probability o Conditional probability o Independent and Dependent o Multiplication Rule o Mutually Exclusive (Disjoint) o Addition Rule o Distinguishable Permutation o Factorial Unit 2 Introduction to Statistics o Combination o Statistics o Data o Population o Sample space o Parameter o Descriptive Statistics o Inferential Statistics o Qualitative/Quantitative Data o Levels of Measurement o Observational Study o Experiment, Simulation o Survey o Census o Sampling o Bias o School Survey Data Collection. o Propaganda Campaign Project. ALTERNATE ASSESSMENTS NATIONAL AND STATE STANDARDS o S-CP-1 o S-CP-2 o S-CP-3 o S-CP-4 o S-CP-5 o S-CP-6 o S-IC-1 o S-IC-3 o S-IC-6 Unit 3 Descriptive Statistics o Frequency Distribution o Histogram o Frequency Polygon, Ogive o Stem-and-Leaf plot o Dot plot o Time series plot o Survey results continuation. o Propaganda campaign continuation o The Four Color Rule o A week of my life. o S-ID-1 o S-ID-2 o S-ID-3 o S-ID-5 o S-ID-6

3 o Pie chart o Mean, median, mode o Distribution shapes o Range, standard deviation o Variance o Empirical rule o 5 number summary o Box plot o Percentile o Z-score. Unit 4 Correlation and Regression Unit 5 Probability Distribution Unit 6 Sampling Distribution o Z Scatter plot o Correlation o Independent variable o Dependent variable o Correlation coefficient o Residuals o Regression Line o Total deviation o Explained deviation o Unexplained deviation o Coefficient of determination o Standard error of estimate o Random Variable o Discrete o Continuous o Probability Distribution o Mean of a Random Variable o Standard Deviation of a Random Variable o Expected Value o Normal Distribution Curve o Standard Normal Curve o Z-Score o Sampling Distribution o Standard error of the mean o Central Limit Theorem o Point Estimate o Interval Estimate o Science experiment with stress and strains o Distance vs Cost o Comparing ticket prices. o Microsoft Excel project part 1 o Excel project part 2 o S-ID-6 o S-ID-6A o S-ID-6B o S-ID-6C o S-ID-7 o S-ID-8 o S-ID-9 o S-MD-1 o S-MD-2 o S-MD-3 o S-MD-4 o S-MD-5 o S-MD-6 o S-IC-2 o S-IC-4 o S-IC-5 o S-IC-6 o S-IC-7

4 o Level of Confidence o Critical Values o Sampling Error o Margin of Error o Confidence Interval o Hypothesis Test o Null Hypothesis o Alternative Hypothesis o Level of Significance statistics o P-Value o Z-Test

5 Standards Code Description S-IC-1: S-IC-2: S-IC-3: S-IC-4: S-IC-5: S-IC-6: S-ID-1: S-ID-2: S-ID-3: S-ID-5: S-ID-6: S-ID-6A: S-ID-6B: S-ID-6C: S-ID-7: S-ID-8: S-ID-9: S-CP-1: S-CP-2: S-CP-3: S-CP-4: S-CP-5: S-CP-6: S-CP-7: S-CP-8: S-CP-9: Understand Statistics as a process for making inferences about population parameters based on random sample from that population. Decide if a specified model is consistent with results from a given data-generated process, e.g., using simulation. Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each. Use data from a sample survey to estimate a population mean or proportion; develop a margin of error through the use of simulation models for random sampling. Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant. Evaluate reports based on data. Represent data with plots on the real number line (dot plots, histograms, and box plots). Use statistics appropriately 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. Interpret differences in shape, center, and spread in context of the data sets, accounting for possible effects of extreme data points (outliers). 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). Represent data on two quantitative variables on a scatter plot, and describe how the variables are related. Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Informally asses the fit of a function by plotting and analyzing residuals. Fit a linear function for a scatter plot that suggests a linear association. Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data. Compute (using technology) and interpret the correlation coefficient of a linear fit. Distinguish between correlation and causation. Describe events as subsets of a sample space (the set of outcomes) using characteristics (or categories) of the outcomes, or as unions, intersections, or complements of other events ( or, and, not ). Understand that two events A and B are independent if the probability of A and B occurring together is the product of their probabilities, and use this characterization to determine if they are independent. Understand the conditional probability of A given B as P(A and B)/P(B), and interpret independence of A and B as saying that the conditional probability of B given A is the same as the probability of B. Construct and interpret two-way frequency tables of data when two categories are associated with each object being ified. Use the two way table as a sample space to decide if events are independent and to approximate conditional probabilities. Recognized and explain the concepts of conditional probability and independence in everyday language and everyday situations. Find the conditional probability of A given B as a fraction of B s outcomes that also belong to A, and interpret the answer in terms of the model. Apply the Addition Rule, P(A or B) = P(A) + P(B) P(A and B), and interpret the answer in terms of the model. Apply the general Multiplication Rule in a uniform probability model, P(A and B) = P(A)P(B A) = P(B)P(A B) and interpret the answer in terms of the model Use permutations and combinations to compute probabilities of compound events and solve problems.

6 S-MD-1: S-MD-2: S-MD-3: S-MD-4: S-MD-5: S-MD-5A: S-MD-5B: S-MD-6: S-MD-7: Define a random variable for a quantity of interest by assigning a numerical value to each event in a sample space, graph the corresponding probabilities distribution using the same graphical display as for data distribution. Calculate the expected value of a random variable, interpret it as the mean of the probability distribution. Develop a probability distribution for a random variable defined for a sample space in which theoretical probabilities can be calculated, find the expected value. Develop a probability distribution for a random variable defined for a sample space in which probabilities are assigned empirically, find the expected values. Weight the possible outcomes of a decision by assigning probabilities to payoff values and finding expected values. Find the expected payoff for a game of chance. Evaluate and compare strategies on the basis of expected values. Use probabilities to make fair decisions (e.g. drawing by lots, using a random number generator). Analyze decisions and strategies using probability concepts (e.g. product testing, medical testing, pulling a hockey goalie at the end of a game).