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Math Worksheets For All Ages

Math Worksheets Land

Math Worksheets For All Ages

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Comparing Data Worksheets

There is an astounding number or reasons why you may elect to compare the likeness and differences between a selection of two or more data sets. The most common reason is to find a common sense of connection between them. You may also select to compare data sets to spot growth or decline between or among the series of observations that were made to compile your data. These worksheets and lessons help students be able to compare and evaluate two separate data sets that. This is a form of application math.

Aligned Standard: HSS-IC.B.5

  • Answer Keys - These are for all the unlocked materials above.

Homework Sheets

The first data set is thick. Sheets 2 and 3 are lighter on the data.

  • Homework 1 - A science teacher is trying to determine if the help of an after school tutor helps improve exams scores. Class 1 has the help of an after school tutor. Class 2 does not have a tutor. Based on the data is there any reason to believe that Class 1 has an advantage with a tutor?
  • Homework 2 - The data chart shows the number of pizzas eaten in a month by a random sample of college and high school students. Which of the following statements are true?
  • Homework 3 - The college median is equal to the high school median.

Practice Worksheets

This is the typical format that you will see questions in on exams.

  • Practice 1 - Which of the following statements are true?
  • Practice 2 - The scores of English subject median are equal to the physics subject median.
  • Practice 3 - The back-to-back stem plot shows the amount of money raised by 7 boys and 7 girls at the recent fundraiser.

Math Skill Quizzes

I'm not a big fan of asking students to find the most correct answer, but I needed to get it in order.

  • Quiz 1 - Three class A students did not use any pencil during this month.
  • Quiz 2 - By looking at three statistics we can gauge subtle differences between two sets of data. We will start with mean and median.
  • Quiz 3 - Twenty-nine A students did not read any books during this month.

How to Compare Data Sets

Professor Balancing on an Apple

We will find many occasions where it is appropriate for us to compare data sets. It may be to provide a sense of uniformity meaning there are no differences between them. Other times, we will want to be able to pinpoint where differences lie within them either to highlight outliers or to exhibit some form of contamination within the data collection process.

There are different graphical tools that can be used for comparing two data sets, such as boxplots, stem plots, bar charts and so many more. However, when you are comparing two or more data sets, there are a few things you need to consider and ultimately be on the hunt for.

Center: Graphically, the center of the distribution is a point where half of the observations are on both sides of the center. This in a sense points out the concept of where the average data point is located and gives you a sense of how each piece of data differs from the concept of normal.

Spread: This indicates the degree of variability of the data. This is a measure that tells you how far away from the center that the data goes in both directions (high and low). If the observations are spread throughout a wide range, the spread is larger, and if the observation revolves around one single value, the spread is smaller.

Shape: This describes the data in a graphically form regardless of the type of graph that is used. When we put it in the form of a graph, we can use this concept to express the characteristics of the graph and discus id there is a sense of symmetry, describe the volume of the peaks, and examine if the data may be skewed in a certain direction.

Anomalies: This is where there may be complete gaps in data caused by some outliers that may by stretched across the range. The data may possess a sense of deviance where sections or cascades differ greatly from the rest of the date.

While these four characteristics of a data set may seem elementary, they are pivotal in helping us explain our results in just about any experiment or research-based situation. This is common terminology used by statisticians and quantitative analysts.

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