MarylandMaryland High School Statistics Curriculum
Video lessons and practice for every high school Statistics topic. Aligned to Maryland College Career Ready Standards for Maryland students.
Maryland 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). |
Maryland High School Statistics Topics
Maryland high school Statistics students study a wide range of topics aligned to the Maryland College Career Ready Standards. From interpreting data plots to applying probability rules and making statistical inferences, the course builds the analytical skills students need for college and career readiness.
Data Analysis and Distributions
Students learn to represent data using dot plots, histograms, and box plots. They compare data sets by examining center (mean and median) and spread (interquartile range and standard deviation). Students also interpret differences in shape and spread, accounting for the effects of outliers on the data.
- Represent data with dot plots, histograms, and box plots
- Compare center and spread across two or more data sets
- Use mean and standard deviation to fit data to a normal distribution
- Estimate population percentages using areas under the normal curve
- Summarize categorical data in two-way frequency tables
Scatter Plots and Linear Models
Maryland Statistics students represent quantitative data on scatter plots and analyze relationships between variables. They compute and interpret correlation coefficients using technology, and they learn the critical distinction between correlation and causation.
- Create and interpret scatter plots for two quantitative variables
- Compute correlation coefficients using technology
- Distinguish between correlation and causation
Statistical Inference and Sampling
Students understand statistics as a process for making inferences about populations from random samples. They explore sample surveys, experiments, and observational studies, and they use simulation to estimate margins of error and evaluate the significance of experimental results.
- Understand population parameters and random sampling
- Recognize differences among surveys, experiments, and observational studies
- Estimate population means and proportions with margin of error
- Use simulation to compare two treatments from randomized experiments
- Evaluate reports based on data for accuracy and validity
Probability Rules and Conditional Probability
High school Statistics in Maryland includes a deep study of probability. Students apply the Addition Rule, the general Multiplication Rule, and learn to compute conditional probabilities. They use two-way frequency tables to determine independence and approximate conditional probabilities.
- Apply the Addition Rule: P(A or B) = P(A) + P(B) - P(A and B)
- Apply the Multiplication Rule: P(A and B) = P(A)P(B|A)
- Understand and compute conditional probability P(A|B)
- Determine independence using probability definitions
- Use permutations and combinations for compound event probabilities
Random Variables and Expected Value
Students define random variables, graph probability distributions, and calculate expected values. They develop probability distributions from both theoretical calculations and empirical data, then apply expected value to real-world decision-making scenarios.
- Define random variables and graph probability distributions
- Calculate expected value as the mean of a probability distribution
- Develop distributions from theoretical and empirical probabilities
- Weigh outcomes using expected values for decision analysis
- Analyze decisions and strategies using probability concepts