MichiganMichigan High School Statistics Curriculum
Video lessons and practice for every high school Statistics topic. Aligned to Michigan Mathematics Standards so students can keep up with class or get ahead.
Michigan High School Statistics Curriculum | StudyPugHelp
ID | Standard | StudyPug Topic |
|---|---|---|
CC.HSS.ID.A.1 | Represent data with plots on the real number line (dot plots, histograms, and box plots). |
CC.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. |
CC.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). |
CC.HSS.ID.A.4 | Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages. Recognize that there are data sets for which such a procedure is not appropriate. Use calculators, spreadsheets, and tables to estimate areas under the normal curve. |
CC.HSS.ID.B.5 | 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. |
CC.HSS.ID.B.6 | Represent data on two quantitative variables on a scatter plot, and describe how the variables are related. |
CC.HSS.IC.A.1 | Understand statistics as a process for making inferences about population parameters based on a random sample from that population. |
CC.HSS.IC.A.2 | Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. |
CC.HSS.IC.B.3 | Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each. |
CC.HSS.IC.B.4 | 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. |
CC.HSS.IC.B.5 | Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant. |
CC.HSS.IC.B.6 | Evaluate reports based on data. |
CC.HSS.CP.A.2 | 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. |
CC.HSS.CP.A.3 | 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 A given B is the same as the probability of A, and the conditional probability of B given A is the same as the probability of B. |
CC.HSS.CP.A.5 | Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations. |
CC.HSS.CP.B.7 | 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. |
CC.HSS.CP.B.9 | Use permutations and combinations to compute probabilities of compound events and solve problems. |
CC.HSS.MD.A.1 | Define a random variable for a quantity of interest by assigning a numerical value to each event in a sample space; graph the corresponding probability distribution using the same graphical displays as for data distributions. |
CC.HSS.MD.A.2 | Calculate the expected value of a random variable; interpret it as the mean of the probability distribution. |
CC.HSS.MD.A.3 | Develop a probability distribution for a random variable defined for a sample space in which theoretical probabilities can be calculated; find the expected value. |
CC.HSS.MD.B.7 | Analyze decisions and strategies using probability concepts (e.g., product testing, medical testing, pulling a hockey goalie at the end of a game). |
Michigan High School Statistics: What Students Learn
High school Statistics in Michigan covers a wide range of topics that help students understand, analyze, and interpret data. Aligned to Michigan Mathematics Standards, the course moves from basic data representation all the way through probability theory and statistical inference.
Data Analysis and Distributions
Students start by learning how to represent data using dot plots, histograms, and box plots. From there, they compare data sets by examining center (mean and median) and spread (standard deviation and interquartile range). A key milestone is fitting data to a normal distribution and using it to estimate population percentages — a skill that appears in science, social studies, and standardized tests.
- Dot plots, histograms, and box plots
- Mean, median, standard deviation, and IQR
- Normal distribution and area under the curve
- Two-way frequency tables and relative frequencies
Scatter Plots and Linear Relationships
Michigan Statistics students learn to plot two quantitative variables, identify trends, and compute the correlation coefficient using technology. Importantly, students learn to distinguish between correlation and causation — a critical thinking skill that applies well beyond math class.
Statistical Inference and Sampling
One of the most practical parts of the course involves understanding how data from a sample can tell us something about a larger population. Students explore sample surveys, experiments, and observational studies. They use simulation to develop margins of error and evaluate whether differences between treatments are statistically significant.
- Random sampling and population estimates
- Margin of error through simulation
- Comparing two treatments from randomized experiments
- Evaluating reports based on data
Probability
The probability unit builds from independence and conditional probability through to the Addition Rule and Multiplication Rule. Students construct two-way frequency tables to approximate probabilities and use permutations and combinations to count compound outcomes.
Random Variables and Expected Value
Students define random variables, build probability distributions, and calculate expected value — both from theoretical models and empirical data. These ideas connect directly to real-world decision-making, from product testing to medical screening.