KansasKansas High School Statistics Curriculum
Video lessons and practice for every high school Statistics topic. Aligned to Kansas Mathematics Standards so students can keep up with class or get ahead.
Kansas 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). |
Kansas High School Statistics: What Students Learn
Kansas high school Statistics introduces students to the tools and reasoning used to collect, analyze, and interpret data. The course aligns to Kansas Mathematics Standards and prepares students for both the KAP grade 11 math assessment and college-level quantitative reasoning.
Data Analysis and Distributions
Students begin by representing data using dot plots, histograms, and box plots. They compare data sets using measures of center (mean and median) and spread (interquartile range and standard deviation). Students also learn to interpret differences in shape and identify the effects of outliers on a distribution.
- Dot plots, histograms, and box plots
- Mean, median, interquartile range, and standard deviation
- Outliers and their effect on shape, center, and spread
- Normal distributions and estimating population percentages
Bivariate Data and Regression
Students move on to analyzing relationships between two variables. They create scatter plots, compute and interpret correlation coefficients, and distinguish between correlation and causation — a critical thinking skill applied throughout the course.
- Scatter plots and linear models
- Correlation coefficient interpretation
- Correlation vs. causation
- Two-way frequency tables and relative frequencies
Statistical Inference and Study Design
This section covers how statisticians draw conclusions from data. Students learn the difference 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 population estimates
- Margin of error using simulation
- Randomized experiments and treatment comparisons
- Evaluating data-based reports
Probability Rules and Conditional Probability
Students explore foundational probability concepts including independence, conditional probability, the Addition Rule, and the general Multiplication Rule. Real-world contexts help students recognize these concepts in everyday situations.
- Independent events and the Multiplication Rule
- Conditional probability: P(A|B) = P(A and B)/P(B)
- Two-way tables as sample spaces
- Addition Rule: P(A or B) = P(A) + P(B) − P(A and B)
- Permutations and combinations for compound events
Random Variables and Expected Value
The final major topic covers random variables and probability distributions. Students define random variables, graph their distributions, and calculate expected values — skills used in decision-making and real-world probability applications.
- Defining and graphing random variables
- Expected value as the mean of a probability distribution
- Theoretical and empirical probability distributions
- Using expected value to analyze decisions and strategies