New HampshireNew Hampshire High School Statistics Curriculum
Video lessons and practice for every high school Statistics topic. Aligned to NH Mathematics Model Competencies standards so New Hampshire students stay on track.
New Hampshire 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). |
High School Statistics in New Hampshire
New Hampshire high school Statistics gives students the tools to collect, analyze, and interpret data. The course follows the NH Mathematics Model Competencies and prepares students for college-level quantitative reasoning. StudyPug covers every standard with clear video lessons and practice problems students can use alongside their schoolwork.
Data Analysis and Distributions
Students start by learning 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). StudyPug walks through each graphical display step by step so students understand not just how to build them, but how to read and interpret them.
- Dot plots, histograms, and box plots on the real number line
- Comparing center and spread across two or more data sets
- Identifying and accounting for outliers
- Fitting data to a normal distribution and estimating population percentages
Bivariate Data and Linear Regression
Students learn to represent two quantitative variables on a scatter plot and fit a linear model. They compute the correlation coefficient using technology and learn the critical difference between correlation and causation — a concept that appears on assessments and in everyday decision-making.
- Scatter plots for two quantitative variables
- Computing and interpreting the correlation coefficient
- Distinguishing correlation from causation
- Two-way frequency tables for categorical data
Statistical Inference and Sampling
Statistics is ultimately about making conclusions beyond the data you have. Students study random sampling, margin of error, and the differences among surveys, experiments, and observational studies. They use simulation to decide whether differences between groups are statistically significant.
- Sample surveys, experiments, and observational studies
- Estimating population means and proportions from sample data
- Margin of error through simulation models
- Evaluating reports based on data quality and methodology
Probability
The probability unit builds from basic rules to conditional probability and independence. Students apply the Addition Rule and the general Multiplication Rule, then use permutations and combinations to find probabilities of compound events.
- Independent events and the Multiplication Rule
- Conditional probability: P(A|B) = P(A and B)/P(B)
- Two-way frequency 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
Students define random variables, graph probability distributions, and calculate expected value. They develop distributions both theoretically and empirically, then apply expected value to real decisions — from games of chance to product testing strategies.
- Defining random variables and graphing probability distributions
- Calculating expected value as the mean of a probability distribution
- Theoretical vs. empirical probability distributions
- Using expected value to analyze decisions and strategies