Section 1
Creating Frequency Tables
Property
A frequency table organizes data by counting how many values fall within each interval or category.
The sum of all frequencies equals the total number of data points:
In this Grade 11 enVision Algebra 1 lesson from Chapter 11: Statistics, students learn how to organize and interpret data sets using dot plots, histograms, and box plots. They practice identifying clusters and outliers in dot plots, building frequency tables to construct histograms, and using a 5-number summary to create box plots that reveal data distribution. Each display type is applied to real-world scenarios so students understand which tool best communicates different kinds of data.
Section 1
Creating Frequency Tables
A frequency table organizes data by counting how many values fall within each interval or category.
The sum of all frequencies equals the total number of data points:
Section 2
Dot Plots
An easy graph to make for numerical data is called a dot plot.
To create a dot plot, first draw a number line and then place a dot above the number line at the location of each data value.
If a value is repeated, this is represented by placing another dot above the previous instance(s) of that value.
This type of graph allows us to identify clusters (data points together in a group), gaps (intervals without any reported values), peaks (data where there are more responses than for nearby values), and outliers (values that are significantly different from the rest of the data).
Dot plots are perfect for smaller sets of data. They let you see every single data point at a glance, making it easy to spot where data clumps together (clusters) or where the most common value is (peak).
Section 3
Clustering and Outliers in Data Displays
In data displays, clustering refers to a set of data points that are grouped closely together, showing similar values.
Outliers are data points that are noticeably separated from the main group of data and stand out from the general pattern.
These features can be identified visually in various data displays such as dot plots, histograms, and box plots.
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Section 1
Creating Frequency Tables
A frequency table organizes data by counting how many values fall within each interval or category.
The sum of all frequencies equals the total number of data points:
Section 2
Dot Plots
An easy graph to make for numerical data is called a dot plot.
To create a dot plot, first draw a number line and then place a dot above the number line at the location of each data value.
If a value is repeated, this is represented by placing another dot above the previous instance(s) of that value.
This type of graph allows us to identify clusters (data points together in a group), gaps (intervals without any reported values), peaks (data where there are more responses than for nearby values), and outliers (values that are significantly different from the rest of the data).
Dot plots are perfect for smaller sets of data. They let you see every single data point at a glance, making it easy to spot where data clumps together (clusters) or where the most common value is (peak).
Section 3
Clustering and Outliers in Data Displays
In data displays, clustering refers to a set of data points that are grouped closely together, showing similar values.
Outliers are data points that are noticeably separated from the main group of data and stand out from the general pattern.
These features can be identified visually in various data displays such as dot plots, histograms, and box plots.
Book overview
Jump across lessons in the current chapter without opening the full course modal.
Continue this chapter