How does High School Math differ from Middle School Math in Common Core?

High School Math in Common Core introduces more advanced concepts, emphasizes abstract thinking, and focuses on applying mathematical skills to real-world scenarios and problem-solving.

What support is available for students struggling with High School Math?

Support includes online tutoring, video lessons, practice problems, study groups, and additional resources provided by schools. Many districts also offer after-school math help programs.

How challenging is the High School Algebra course in Common Core?

The Algebra course can be challenging as it introduces abstract concepts. It covers linear equations, quadratic functions, and systems of equations. With consistent practice and support, most students can succeed.

What career paths benefit from strong High School Math skills?

Strong math skills are valuable in STEM fields, finance, economics, data analysis, engineering, computer science, and many other careers. Math develops critical thinking applicable to various professions.

How does the Common Core Statistics and Probability course prepare students for college?

This course introduces data analysis, probability theory, and statistical inference, preparing students for college-level statistics courses and research methods across various disciplines.

Common Core

Math

Kindergarten

Grade 1

Grade 2

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8

High School

High School Math Courses - Common Core Curriculum

Explore comprehensive High School math courses aligned with Common Core standards. From Number and Quantity to Statistics, our curriculum guides students through essential mathematical concepts and problem-solving skills.

High School (Number and Quantity)

High School (Algebra)

High School (Functions)

High School (Geometry)

High School (Statistics and Probability)

Common Core High School Math Curriculum

Common Core ID	Standard	StudyPug Topic
CC.HSS.ID.A.1	Represent data with plots on the real number line (dot plots, histograms, and box plots).	Reading and drawing histograms Box-and-whisker plots and scatter plots Frequency tables and dot plots Frequency distribution and histograms

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.

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.3

Interpret 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.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.

Organizing data

Introduction to normal distribution

Normal distribution and continuous random variable

Z-scores and random continuous variables

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.

Reading and drawing Venn diagrams

Probability with Venn diagrams

CC.HSS.ID.B.6

Represent 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.ID.C.7

Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.

Slope intercept form: y = mx + b

Rate of change

Regression analysis

CC.HSS.IC.A.1

Understand statistics as a process for making inferences about population parameters based on a random sample from that population.

Sampling methods

CC.HSS.IC.A.2

Decide 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.3

Recognize 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.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.

Margin of error

Confidence intervals to estimate population mean

Making a confidence interval

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.

Traditional hypothesis testing

P-value hypothesis testing

Analysis of variance (ANOVA)

CC.HSS.IC.B.6

Evaluate reports based on data.

Influencing factors in data collection

CC.HSS.CP.A.1

Describe events as subsets of a sample space (the set of outcomes) using characteristics (or categories) of the outcomes, or as unions, intersections, or complements of other events ("or," "and," "not").

Introduction to probability

Organizing outcomes

Set notation

Set builder notation

Intersection and union of 2 sets

Intersection and union of 3 sets

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.

Probability of independent events

Multiplication rule for "AND"

Probability of independent events

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.

Conditional probability

CC.HSS.CP.A.5

Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations.

Comparing experimental and theoretical probability

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.

Addition rule for "OR"

CC.HSS.CP.B.9

Use permutations and combinations to compute probabilities of compound events and solve problems.

Permutations

Combinations

Probability involving permutations and combinations

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.

Probability distribution - histogram, mean, variance & standard deviation

CC.HSS.MD.A.2

Calculate the expected value of a random variable; interpret it as the mean of the probability distribution.

Properties of expectation

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.

Binomial distribution

Mean and standard deviation of binomial distribution

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).

Type 1 and type 2 errors

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