OhioOhio High School Statistics and Probability
Video lessons and practice for every Statistics and Probability topic. Aligned to Ohio's Learning Standards Math for high school students.
Ohio High School Statistics and Probability | 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). |
Ohio High School Statistics and Probability Curriculum
Ohio high school students studying Statistics and Probability are expected to master a wide range of skills — from reading data displays to applying probability rules and drawing statistical inferences. StudyPug covers every topic aligned to Ohio's Learning Standards Math, so students always have support when they need it.
Data Analysis and Interpretation
Students learn to represent data using dot plots, histograms, and box plots. They compare data sets using measures of center and spread, including mean, median, interquartile range, and standard deviation. The course also covers interpreting two-way frequency tables and identifying trends in categorical data.
- Dot plots, histograms, and box plots
- Mean, median, standard deviation, and IQR
- Outliers and their effect on data
- Two-way frequency tables and relative frequencies
- Scatter plots and linear association
Normal Distributions and Inference
Students use the mean and standard deviation to fit data to a normal distribution and estimate population percentages. They learn to use calculators and tables to find areas under the normal curve. Statistical inference topics include sample surveys, experiments, observational studies, margin of error, and evaluating data reports.
- Normal distribution and the empirical rule
- Estimating areas under the normal curve
- Sample surveys vs. experiments vs. observational studies
- Margin of error and simulation models
- Evaluating statistical reports
Probability Rules and Concepts
This section covers the foundational rules of probability, including independence, conditional probability, the Addition Rule, and the Multiplication Rule. Students work with two-way tables as sample spaces and apply permutations and combinations to compound events.
- Independent events and the product rule
- Conditional probability: P(A|B) = P(A and B)/P(B)
- Addition Rule: P(A or B) = P(A) + P(B) - P(A and B)
- General Multiplication Rule
- Permutations and combinations
Random Variables and Expected Value
Students define random variables, graph probability distributions, and calculate expected values. They develop distributions using both theoretical and empirical probabilities, and apply expected value to real-world decisions and strategies.
- Defining random variables and probability distributions
- Expected value as the mean of a distribution
- Theoretical vs. empirical probability distributions
- Using expected value to weigh decisions
- Analyzing strategies using probability concepts