MissouriMissouri High School Statistics Curriculum
Video lessons and practice for every high school Statistics topic. Aligned to Missouri Learning Standards Math so students can keep up with class or get ahead.
Missouri 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). |
Missouri High School Statistics: Topics and Standards
Missouri high school Statistics follows the Missouri Learning Standards Math, covering a wide range of data, probability, and inference topics. StudyPug organizes every standard into clear video lessons with matching practice problems so students always know exactly what to study.
Data Analysis and Distributions
Students learn to represent data using dot plots, histograms, and box plots. They compare data sets using measures of center (mean and median) and spread (standard deviation and interquartile range). Key skills include interpreting the shape of distributions and recognizing how outliers affect conclusions.
- Dot plots, histograms, and box plots
- Mean, median, interquartile range, and standard deviation
- Normal distribution and estimating population percentages
- Two-way frequency tables and relative frequencies
Scatter Plots, Correlation, and Causation
Students plot two quantitative variables, describe their relationship, and compute the correlation coefficient using technology. A critical skill at this level is understanding the difference between correlation and causation — a concept that appears in college entrance exams and real-world data literacy.
Statistical Inference and Sampling
Statistics as a discipline is about drawing conclusions from data. Missouri students learn to distinguish between sample surveys, experiments, and observational studies. They use simulation to estimate margins of error and evaluate whether differences between treatments are statistically significant.
- Random sampling and margin of error
- Comparing two treatments using randomized experiments
- Evaluating reports based on data
Probability
Probability topics include independent and dependent events, conditional probability, the Addition Rule, and the general Multiplication Rule. Students also work with permutations and combinations to find probabilities of compound events.
Random Variables and Expected Value
Students define random variables, build probability distributions, and calculate expected values. They apply these concepts to real decisions — such as product testing, medical testing, and game strategy — by weighing outcomes using probability.
- Discrete probability distributions
- Expected value as the mean of a distribution
- Theoretical and empirical probability distributions
- Decision analysis using expected value