Will this help with my child's High School Statistics homework?

Yes. StudyPug covers every strand in Maine high school Statistics, including data displays, normal distributions, scatter plots, probability rules, and random variables, so students can get help right when they need it.

How long are the video lessons?

Most video lessons run between a few minutes and about ten minutes, so students can watch a lesson on correlation, conditional probability, or any other topic without losing their place in a long session.

Is there a state test for High School Statistics in Maine?

Maine uses the Through Year assessment program for high school math. Statistics is a course-level subject, so students should confirm with their school which assessments apply to their specific course and graduation pathway.

Can my child use StudyPug on a tablet or phone?

Yes. StudyPug works on tablets, phones, and computers, so your child can review Statistics lessons on data distributions, probability, or inference from any device at any time.

Can I try StudyPug before paying?

Yes. StudyPug offers free sample lessons so you can see the content before subscribing. There is also a 30-day money-back guarantee if the course is not the right fit for your child.

ME High School Statistics Curriculum

Give your high schooler the support they need to succeed in Statistics. Our lessons and practice problems follow what Maine schools teach, from data analysis to probability.

ME High School Statistics Curriculum

IDStandardStudyPug Topic

CC.HSS.ID.A.1Represent data with plots on the real number line (dot plots, histograms, and box plots).
- Why learn log?
- Equation of the best fit line
- The determinant of a 2 x 2 matrix
- Frequency distribution and histograms
CC.HSS.ID.A.2Use 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.
- Median and mode
- Mean
- Range and outliers
- Center of a data set: mean, median, mode
- Spread of a data set - standard deviation & variance
CC.HSS.ID.A.3Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
- Application of averages
- Shapes of distributions
CC.HSS.ID.A.4Use 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.
- Introduction to normal distribution
- Normal distribution and continuous random variable
- Z-scores and random continuous variables
CC.HSS.ID.B.5Summarize 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.
- Reading and drawing Venn diagrams
- Probability with Venn diagrams
CC.HSS.ID.B.6Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.
- Reading and drawing line graphs
- Bivariate, scatter plots and correlation
CC.HSS.IC.A.1Understand statistics as a process for making inferences about population parameters based on a random sample from that population.
- Sampling methods
CC.HSS.IC.A.2Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation.
- Sampling distributions
- Rare event rule
CC.HSS.IC.B.3Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each.
- Census and bias
CC.HSS.IC.B.4Use 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.
- Margin of error
- Confidence intervals to estimate population mean
- Making a confidence interval
CC.HSS.IC.B.5Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.
- Traditional hypothesis testing
- P-value hypothesis testing
- Analysis of variance (ANOVA)
CC.HSS.IC.B.6Evaluate reports based on data.
- Influencing factors in data collection
CC.HSS.CP.A.2Understand 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.
- Probability of independent events
- Multiplication rule for
- Probability of independent events
CC.HSS.CP.A.3Understand 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.
- Conditional probability
CC.HSS.CP.A.5Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations.
- Comparing experimental and theoretical probability
CC.HSS.CP.B.7Apply the Addition Rule, P(A or B) = P(A) + P(B) - P(A and B), and interpret the answer in terms of the model.
- Addition rule for
CC.HSS.CP.B.9Use permutations and combinations to compute probabilities of compound events and solve problems.
- Permutations
- Combinations
- Probability involving permutations and combinations
CC.HSS.MD.A.1Define 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.
- Probability distribution - histogram, mean, variance & standard deviation
CC.HSS.MD.A.2Calculate the expected value of a random variable; interpret it as the mean of the probability distribution.
- Properties of expectation
CC.HSS.MD.A.3Develop a probability distribution for a random variable defined for a sample space in which theoretical probabilities can be calculated; find the expected value.
- Binomial distribution
- Mean and standard deviation of binomial distribution
CC.HSS.MD.B.7Analyze decisions and strategies using probability concepts (e.g., product testing, medical testing, pulling a hockey goalie at the end of a game).
- Type 1 and type 2 errors

High School Statistics

About High School Maine Statistics

Maine high school Statistics follows the Maine Learning Results, covering data analysis, statistical inference, probability, and random variables. Students work with data displays, measures of center and spread, normal distributions, scatter plots, correlation, and probability rules including conditional probability and independence. StudyPug provides video lessons and step-by-step practice problems for every strand in this course, aligned to the Maine Learning Results.

Strands: Data Analysis, Statistical Inference, Probability, and Random Variables
Video lessons for every Statistics topic in the Maine Learning Results
Step-by-step practice problems with worked examples for each strand
Content aligned to Maine Learning Results for high school Statistics

Curriculum topics by strand

Lessons and practice problems for every topic, aligned to the Maine Learning Results for high school Statistics.

Topic includes:

Practice

Video

Quiz

Standard

StudyPug Topic

ME High School Statistics Topics

Data Displays: Represent data with dot plots, histograms, and box plots on the real number line.
Comparing Data Sets: Use measures of center (median, mean) and spread (interquartile range, standard deviation) to compare two or more data sets, and interpret differences including the effects of outliers.
Normal Distributions: Fit data to a normal distribution using the mean and standard deviation, estimate population percentages, and use technology to find areas under the normal curve.
Categorical Data and Two-Way Tables: Summarize categorical data in two-way frequency tables and interpret joint, marginal, and conditional relative frequencies.
Scatter Plots and Correlation: Represent two quantitative variables on a scatter plot, compute and interpret the correlation coefficient, and distinguish between correlation and causation.
Statistical Inference: Understand sampling as a basis for inference, design surveys and experiments, use simulation to estimate margins of error, and evaluate data-based reports.
Probability: Apply addition and multiplication rules, work with conditional probability and independence, and use permutations and combinations for compound events.
Random Variables: Define random variables, build probability distributions, calculate expected values, and analyze decisions using probability concepts.

How StudyPug Helps Maine High School Statistics Students

When a student gets stuck on a concept like the normal distribution, conditional probability, or interpreting a scatter plot, StudyPug provides clear video lessons and worked examples they can revisit as many times as needed. Each lesson breaks the topic into manageable steps so students can build confidence before moving on.

Practice problems with step-by-step solutions let students check their work on every strand, from two-way frequency tables to expected value calculations. Students can move at their own pace, review a lesson before a test, or catch up on a topic they missed in class.

Aligned to Maine High School Statistics Curriculum

Every lesson and practice problem on StudyPug is built around the Maine Learning Results for high school Statistics, so the content students see here reflects exactly what their teachers are covering in class - from data analysis and statistical inference to probability and random variables.

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Written by the StudyPug Math Team

Last updated June 5, 2026

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