How do you read a two-way frequency table and compute joint, marginal, and conditional relative frequencies?
Summarize two-variable categorical data in two-way tables and interpret joint, marginal, and conditional relative frequencies (NC.M1.S-ID.5).
An NC Math 1 EOC answer on two-way frequency tables (NC.M1.S-ID.5): reading counts, computing joint, marginal, and conditional relative frequencies, and recognizing possible association between two categorical variables.
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What this topic is asking
NC.M1.S-ID.5 asks you to summarize two-variable categorical data in a two-way frequency table and interpret joint, marginal, and conditional relative frequencies, recognizing possible association between the variables. This is the categorical counterpart to scatter plots for numerical data.
The three kinds of frequency
The names tell you where in the table to look.
The phrase "of those who..." is the signal for a conditional frequency: it restricts the denominator to a subgroup.
Reading a two-way table
Recognizing association
Two variables may be associated if a conditional frequency differs noticeably across groups. If of coffee drinkers are morning people but only of non-coffee drinkers are, the difference suggests an association between coffee drinking and being a morning person. Equal conditional frequencies suggest no association.
How the NC Math 1 EOC examines this topic
- Gridded response. Compute a joint, marginal, or conditional relative frequency as a fraction or percent.
- Multiple choice. Identify the type of frequency, or whether the data suggests association.
- Technology-enhanced. Fill in a two-way table, or select the correct conditional statement.
Two-way tables handle categorical pairs, while scatter plots handle numerical pairs; both are about relationships between two variables, a theme continued in correlation and causation.
Why conditional frequencies reveal relationships
A joint or marginal frequency describes the whole group, but a conditional frequency zooms into a subgroup and asks how the other variable behaves there. That zoom is what exposes a relationship: if knowing one category changes the likelihood of the other, the variables are associated. This is exactly why the EOC distinguishes the three frequency types so carefully, they answer different questions. "What fraction of everyone drinks coffee and is a morning person" (joint) is not the same as "what fraction of coffee drinkers are morning people" (conditional). Misreading "of those who" as a fraction of the whole is the single most common error, and getting the denominator right is the heart of S-ID.5.
Try this
Q1. A table of people has who own a pet and also exercise. What is this joint relative frequency? [1 point]
- Cue. of everyone.
Q2. Of pet owners, exercise. What is the conditional relative frequency? [2 points]
- Cue. Condition on the owners: .
Exam-style practice questions
Practice questions written in the style of NCDPI exam questions on this dot point, with worked answer explainers. The year tag is the paper they imitate, not the source.
NC Math 1 EOC (style)2 marksIn a survey of students, like pizza and of those also play sports. What fraction of pizza-likers play sports?Show worked answer →
The conditional relative frequency is , about .
A conditional relative frequency restricts to a subgroup. Here we condition on the pizza-likers and ask how many play sports: . Note this differs from the joint frequency (out of everyone). Reading "of those" as a conditional is the S-ID.5 skill.
NC Math 1 EOC (style)1 marksIn a two-way table, the row and column totals in the margins are called: (A) joint frequencies (B) marginal frequencies (C) conditional frequencies (D) outliersShow worked answer →
The correct answer is (B), marginal frequencies.
The totals in the margins (the bottom row and right column) are the marginal frequencies, giving the total for each category of one variable. A joint frequency is a single inner cell; a conditional frequency divides within a row or column. The names come from where the numbers sit in the table.
Related dot points
- Represent data with dot plots, histograms, and box plots, and interpret the shape of a distribution (NC.M1.S-ID.1, S-ID.3).
An NC Math 1 EOC answer on representing data (NC.M1.S-ID.1, S-ID.3): reading and building dot plots, histograms, and box plots, and describing distribution shape, symmetry, skew, and outliers.
- Use statistics appropriate to the shape of the distribution to compare center and spread of two or more data sets (NC.M1.S-ID.2).
An NC Math 1 EOC answer on center and spread (NC.M1.S-ID.2): mean versus median, range and IQR, choosing measures based on shape and outliers, and comparing two data sets.
- Represent two quantitative variables on a scatter plot, fit a linear model, and interpret slope and intercept in context (NC.M1.S-ID.6, S-ID.7).
An NC Math 1 EOC answer on scatter plots and linear models (NC.M1.S-ID.6, S-ID.7): describing form and strength, fitting a line of best fit, using it to predict, and interpreting slope and intercept in context.
- Use the correlation coefficient to describe the strength and direction of a linear relationship and distinguish correlation from causation (NC.M1.S-ID.8, S-ID.6c).
An NC Math 1 EOC answer on correlation (NC.M1.S-ID.8, S-ID.6c): what the correlation coefficient r measures, reading its sign and size, why correlation does not imply causation, and assessing fit with residuals.
- Create linear, quadratic, and exponential equations and inequalities in one or two variables to model and solve problems (NC.M1.A-CED.1, A-CED.2).
An NC Math 1 EOC answer on creating equations and inequalities (NC.M1.A-CED.1, A-CED.2): defining the variable, translating rates and fixed amounts, choosing the right inequality symbol, and judging viability.
Sources & how we know this
- North Carolina Standard Course of Study for Mathematics — NC Department of Public Instruction (2024)
- EOC NC Math 1 and NC Math 3 Test Specifications — NC Department of Public Instruction (2024)