GeorgiaGeorgia Statistical Reasoning Curriculum
Video lessons and practice for every Statistical Reasoning topic. Aligned to Georgia Standards of Excellence so high school students can keep up or get ahead.
Georgia Statistical Reasoning 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). |
Statistical Reasoning in Georgia High Schools
Statistical Reasoning is a high school math course that builds skills in data analysis, probability, and statistical inference. Georgia students in this course follow the Georgia Standards of Excellence, preparing them to interpret real-world data and make evidence-based decisions.
Data Analysis and Distributions
Students begin by learning to represent data using dot plots, histograms, and box plots. From there, they compare data sets using measures of center like mean and median, and measures of spread like interquartile range and standard deviation. Understanding how outliers affect a data set is a key skill covered early in the course.
- Represent data on the real number line
- Compare shape, center, and spread across data sets
- Fit data to a normal distribution and estimate population percentages
- Summarize categorical data in two-way frequency tables
Correlation, Regression, and Causation
Students explore scatter plots and learn to describe relationships between two quantitative variables. They compute and interpret the correlation coefficient of a linear fit using technology, and critically distinguish between correlation and causation — a concept with broad real-world applications.
Statistical Inference and Sampling
This unit introduces statistics as a process for making inferences about populations from random samples. Students learn the differences among sample surveys, experiments, and observational studies, and how randomization plays a role in each. They use simulations to develop margins of error and evaluate the significance of differences between experimental treatments.
- Estimate population mean or proportion from sample data
- Develop margin of error using simulation models
- Evaluate reports based on data critically
Probability and Conditional Probability
Students develop a solid foundation in probability, including independent events, conditional probability, and the Addition and Multiplication Rules. Two-way frequency tables serve as sample spaces to test independence and approximate conditional probabilities in everyday contexts.
- Understand and apply the Addition Rule: P(A or B) = P(A) + P(B) - P(A and B)
- Apply the general Multiplication Rule in uniform probability models
- Use permutations and combinations to find probabilities of compound events
Random Variables and Expected Value
In the final major unit, students define random variables, graph probability distributions, and calculate expected values. They develop probability distributions using both theoretical and empirical methods, and apply expected value to real decision-making scenarios such as product testing and medical testing.