How To Make a Relative Frequency Distribution Table

The Organic Chemistry Tutor
8 Jan 201905:09
EducationalLearning
32 Likes 10 Comments

TLDRThe script explains the process of creating a relative frequency distribution table from a dataset. It first lists out all the values in the dataset and counts the frequency or occurrences of each value. It then sums the frequencies to get the total frequency. Relative frequency is calculated by dividing each value's frequency by the total frequency. The sums of the relative frequencies should equal 1 to check the work. The video encourages viewers to subscribe and watch a follow up video on cumulative relative frequency to build on the concepts covered here regarding statistics.

Takeaways
  • 😀 The goal is to make a relative frequency distribution table from a dataset.
  • 👉 First list the values and frequencies of each value in the dataset.
  • 📊 Calculate relative frequency by dividing each frequency by the total frequency.
  • 🧮 Add up the relative frequencies to check your work - it should equal 1.
  • 📈 Start with the lowest value and move to the highest to fill in the table.
  • 😎 Subscribe to the channel and click the notification bell for more videos.
  • 🔢 There are examples with the numbers: 2, 3, 4, 5, 7, 8.
  • 📊 The video explains how to create the frequency and relative frequency columns.
  • ✏️ You can test your work by making sure the relative frequencies add up to 1.
  • 🤓 There is a follow up video on cumulative relative frequency to build on this concept.
Q & A

  • What are the steps to make a relative frequency distribution table?

    -The steps are: 1) List out all the values in the dataset. 2) Count the frequency of each value. 3) Sum the frequencies. 4) Calculate the relative frequency by dividing each frequency by the total frequency.

  • How do you calculate relative frequency?

    -To calculate relative frequency, take the frequency of a value and divide it by the total frequency.

  • What is the purpose of a relative frequency distribution?

    -A relative frequency distribution shows the proportion of data that belongs to each class. It allows you to compare frequencies across different datasets.

  • What is the difference between frequency and relative frequency?

    -Frequency is the count of each value. Relative frequency is the frequency divided by the total count, which gives the proportion.

  • Why should the relative frequencies add up to 1?

    -The relative frequencies add up to 1 because they represent the proportional values out of the whole dataset. The total of all proportions equals 1.

  • How can you check if you calculated relative frequency correctly?

    -Add up all the relative frequencies. If the total equals 1, then the calculations are correct.

  • What is cumulative relative frequency?

    -Cumulative relative frequency is the sum of the relative frequencies up to and including a given value. It shows the proportion of data below a certain point.

  • How is a frequency table constructed?

    -A frequency table has columns for the data values, frequencies, and relative frequencies. The values are listed from lowest to highest.

  • What is the first step in making a relative frequency table?

    -The first step is to list out all the values in the dataset and count the frequency or occurrences of each one.

  • Why are relative frequency tables useful?

    -Relative frequency tables allow you to see the distribution of data. They make it easy to visualize patterns and compare frequencies across datasets.

Outlines
00:00
😀 Creating a frequency distribution table

This paragraph explains how to create a frequency distribution table from a dataset. It goes through an example dataset, listing out the values and counting the frequencies for each unique value. It then calculates the total frequency by summing the individual frequencies. The relative frequency is calculated by dividing each frequency by the total frequency.

05:02
😃 Calculating and checking relative frequencies

This paragraph continues the example, walking through calculating the relative frequencies for each value by dividing the frequencies by the total frequency. It explains that the relative frequencies should sum to 1, providing a way to double check that the calculations were done correctly.

Mindmap
Keywords
💡frequency
Frequency refers to how often a particular value shows up in the dataset. It is important for making a relative frequency distribution table because we need to tally up the frequencies for each unique value as the first step. For example, the frequency for the number 2 is 6 since there are six 2's in the dataset provided in the script.
💡relative frequency
Relative frequency indicates the proportion of data points that have a particular value. It allows us to compare frequencies across different datasets. To calculate it, we divide the frequency for a value by the total number of data points. For example, the relative frequency for 2 is 6/20 = 0.30.
💡distribution
A distribution shows how the different data values are distributed in a dataset. The video focuses on a relative frequency distribution, which displays the relative frequencies for each unique data value. This allows us to see which values show up most often.
💡table
A table is a structured format used to organize data. In this case, a frequency table organizes the values and frequencies, while a relative frequency distribution table adds a column for the relative frequencies. The video demonstrates how to systematically build this type of table.
💡data set
A data set refers to an entire collection of data points. The video provides a sample data set of 20 numbers to use for demonstrating the steps to create a relative frequency distribution table.
💡value
Values refer to the actual data points or numbers in the dataset. When making the distribution table, the values column lists the unique numbers that appear in the data set.
💡total frequency
The total frequency is the sum of all the individual frequencies in the frequency table. It represents the total number of data points. We divide by the total frequency when calculating relative frequencies.
💡pattern
Noticing patterns in data allows us to work efficiently, rather than having to repeat all the calculations manually each time. The pattern here is to take the frequency for each value and divide it by the total frequency to derive the relative frequency.
💡notification bell
The narrator encourages viewers to click the notification bell after subscribing so they receive alerts when new videos are posted. This relates to the purpose of building audience engagement.
💡cumulative relative frequency
This term refers to an advanced concept mentioned at the end of the video - a cumulative relative frequency distribution shows the running totals of the relative frequencies. The narrator recommends viewers to watch the next video focused on this concept.
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Transcripts

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