25th: 3.00

75th: 3.00

Min: 1.00

Max: 6.00

10th: 2.00

90th: 5.40

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The five number summary is a set of basic descriptive statistics which provides information about a set of data. It
identifies the shape, center, and spread of a statistic in universal terms which can be used to analyze any sample,
regardless of the underlying distribution. It consists of 5 key metrics: the median value (the center), the range
of a distribution (25th percentile to 75th percentile), and the maximum and minimum observed values.

The five number summary is a concise description of a set of observations. It can be quickly calculated, describes the
general shape of the distribution, identifies the likely range of values, and - most importantly - does not involve any
assumptions about the shape of the underlying distribution. In this sense, the five number summary is a universal description
of the key practical elements of a distribution of observations.

Well the simple way is to use our five number summary calculator. But if you're doing this by hand:

- Sort The observations, ranking by value
- Count the Total Number of Observations
- For each percentile, take the appropriate point in the ranked list
- If the precise percentile falls between two points, average the nearest two points.

And if this was your homework assignment, you're welcome.....

Well the simple way is to use our five number summary calculator. But if you're doing this by hand...

See list sorting exercise above (rank observations by value). Count the total number of records. Divide by 4. That is the observation in the list for the 25th percentile (Q1, the 1st quartile). Multiply this amount by 3. That is the observation in the list for the 75th percentile (the start of the upper quartile or the top of the 3rd quartile). Anything outside of that range is an outlier.

If an observation falls between two points, the general convention is to average the points. There are more complicated approaches (a weighted average) but this usually will suffice.

The second quartile is the gap between the 25th percentile and the median. The fourth quartile is the gap between the 75th percentile and the maximum value. This captures your interquartile distance.

You can identify the upper half and lower half of a distribution using the smallest value, middle value, and largest value of the sample. This approach is independent of sample size.

The five number summary can be used to create a box plot graph. The range of the graph is denoted as the top of the first quartile and the top of the third quartile. You are treating the upper quartile and lower quartile as outlier data points. The quartile value is used to show the range of the quartile. The whisker diagram shows the range between the extreme values (maximum value, minimum value) of the data.

There is another form of the boxplot referred to as a modified box plot. This adjusts the box and whisker plot so to drop outlier data value points. This site uses a histogram to as a descriptive statistic tool; we can add a modified boxplot if there's sufficient demand.While the five number summary is a good basic measure of a distribution, it doesn't show a full view of the standard deviation, mean, or variance. You need to carefully manage any suspected outlier data points.

You can use the information from the 5 number summary calculator to calculate this. The upper and lower fences are a simple estimate of the potential outliers of a distribution. This approach uses the interquartile range (Q3 - Q1 values) to assess how far outliers may exist. The inner fence is 1.5 x the interquartile range above / below the 1st and 3rd quartiles (respectively). The outer fence is 3.0 x the interquartile range. Note that the lower bounds of these ranges can be a negative number (if the IQR is wide and the absolute values of the first quartile are small. This is common in many logistics problems. In most cases, the underlying data isn't from a normal distribution.

In order to utilize a 5 number summary calculator effectively, it is crucial for users to understand some important concepts. For instance, the data set must be organized in ascending order, which aids in identifying the median, or middle value of the data. Quartiles, another vital aspect of the 5 number summary, divide the data set into four equal parts, with the 1st quartile representing the 25th percentile and the 3rd quartile representing the 75th percentile. Additionally, the interquartile range, which is the difference between the upper and lower quartiles, illustrates the dispersion of data values around the median. It is important to note that the 5 number summary provides only a brief overview of a data set and does not offer deeper insights into data properties, such as variance or standard deviation.

The use of a 5 number summary calculator is not only limited to academic and scientific applications; it is also beneficial to individuals who are interested in better understanding the distribution of any given data set. Whether evaluating the performance of a group of students or analyzing a large set of financial data, a 5 number summary calculator can provide valuable information in just a few simple steps. With its broad range of applications and ease of use, this tool is an indispensable resource for those seeking an efficient method to analyze data and make informed decisions.

The 5 number summary is a collection of five statistical values that provide a comprehensive description of a given data set. It consists of the minimum value, first quartile (25th percentile), median (50th percentile), third quartile (75th percentile), and maximum value. The 5 number summary calculator is a handy tool for computing these values quickly and efficiently, making it easier to perform descriptive statistics on your data set.

The minimum value in a data set is the smallest data value, while the maximum value is the largest data value. To find these values, sort the data in ascending order and identify the first and last numbers in the sorted list. These values provide insight into the range of the data set, helping to establish its limits and identify any outliers or extreme values.

Quartiles divide the data set into four equal parts, with each quartile representing 25% of the data. The first quartile (Q1) represents the lower 25%, while the third quartile (Q3) represents the upper 75%. The median, or second quartile (Q2), separates the lower and upper halves of the data set, representing the middle value. When the data set has an odd number of observations, the median is the middle number; when the total number of observations is even, calculate the median by averaging the two middle numbers.

To find the quartiles, first sort the data in ascending order. For the first and third quartiles, determine the positions for the 25th and 75th percentiles respectively, using either the cumulative frequency method or interpolation. The median can also be found using similar methods, depending on the size and distribution of the data set.

Understanding the quartiles and median is essential to determining the interquartile range (IQR), which is the difference between the third and first quartiles (Q3 - Q1). This value helps measure the spread of the data, highlighting the central 50% of the data set, which often holds the area of greatest interest.

By employing a 5 number summary calculator, you can quickly and accurately find these critical statistical values, making it easier to analyze and interpret a given data set. This approach streamlines the process of descriptive statistics, providing valuable insights about the data's distribution and range without requiring complex calculations or extensive study.

The calculator arranges the data set in ascending order, allowing for easier identification of important values. It begins by determining the smallest value (minimum value) and largest value (maximum value) of the given data. These values represent the range of the data set.

Next, the calculator identifies the median (second quartile) of the data set. This middle value is found by locating the middle number if there is an odd number of observations or calculating the average of the two middle numbers if there is an even number of observations.

Once the median is found, the data set is divided into the lower half and upper half. For each of these halves, the calculator finds the respective 25th percentile (1st quartile or lower quartile) and the 75th percentile (3rd quartile or upper quartile). These values signify the middle value of the lower half and upper half, respectively.

The 5 number summary consists of the following statistics: minimum value, 1st quartile, median, 3rd quartile, and maximum value. Together, they provide a comprehensive overview of the given data's distribution and dispersion. These statistical values are essential in various fields of interest, such as finance, social sciences, and data analysis, for a deeper understanding of data patterns and trends.

Once you have input your data set into the 5 number summary calculator, the tool generates various statistical values that provide insights into your data. Interpreting these results is crucial for understanding the key descriptive statistics and patterns within your data set.

Percentiles divide your data into 100 equal parts, with each percentile representing a specific data value within the ascending ordered data set. Quartiles, on the other hand, divide the data into four equal parts. The 25th percentile corresponds to the lower quartile (1st quartile), the 50th percentile corresponds to the median (2nd quartile), and the 75th percentile corresponds to the upper quartile (3rd quartile).

Understanding the percentiles and quartiles within your data helps identify the data value's relative position within the data set. It can also give you an indication of the overall interest, as well as the spread and distribution of your data.

The interquartile range (IQR) is an important descriptive statistic that represents the difference between the upper quartile and the lower quartile. It measures the spread of the middle 50% of your data. The IQR provides a measure of the variance within your data set and can help identify potential outliers.

Ultimately, interpreting the results generated by the 5 number summary calculator allows you to better understand the patterns, central tendencies, and dispersion within your data. Familiarizing yourself with these key statistical values and methodologies equips you with the tools necessary to make informed decisions and analyses based on your data set.

Descriptive statistics are essential for understanding and interpreting data in different contexts. They provide key insights and help summarize various aspects of a dataset, such as central tendency, dispersion, distribution, and the position of values within the dataset. In this section, we will specifically discuss standard deviation, variance, cumulative frequency, and squared deviations, as they relate to the five number summary calculator.

Standard deviation and variance are important descriptive statistics that measure the dispersion of data within a dataset. They provide an indication of how much the individual data values deviate from the average (mean) value of the dataset. The 5 number summary calculator helps to compute the standard deviation and variance of a given dataset easily and quickly.

In the context of the five number summary, standard deviation gives valuable information on the spread of data around the median, while variance is the average of squared deviations from the mean. These key measures of dispersion allow users to understand a dataset's distribution more effectively and make informed decisions based on the data.

Cumulative frequency is another descriptive statistic that can be useful in understanding a dataset's distribution. It represents the total number of data values that are less than or equal to a specified value in a sorted dataset. By examining the cumulative frequency, individuals can better grasp the frequency distribution of the given data. Furthermore, cumulative frequency can help in determining the percentiles, including the 25th (lower quartile), 50th (median or second quartile), and 75th (upper quartile) percentiles.

Squared deviations are individual data values minus the mean value, squared. This measure is especially useful when calculating variance, as it mitigates the effect of negative differences. The use of squared deviations prevents deviations from cancelling each other out, leading to a more accurate representation of the dataset's overall dispersion.

In summary, standard deviation, variance, cumulative frequency, and squared deviations are crucial descriptive statistics that help better understand a dataset's characteristics. The 5 number summary calculator is a valuable tool for efficiently deriving these statistical values from a given dataset, providing users with an effective way to analyze and evaluate their data.

The 5 number summary calculator provides essential information about a given set of data, offering valuable insights and assisting in various real-world situations. In this section, we will explore some applications of the calculator, focusing on real-world examples, and comparison methods.

Using a 5 number summary calculator can benefit users in several ways, such as:

- Education: Teachers can use the calculator to quickly analyze students' scores and determine areas where extra assistance may be necessary.
- Finance: Financial analysts can use the calculator to discover patterns and trends in investment data, helping to inform investment decisions.
- Medicine: Medical researchers can analyze patient data, identifying trends in results, and determining any outliers that may warrant further investigation.
- Sports: Coaches and performance analysts can use five number summary statistics to evaluate players' performance and make data-driven decisions for improvement.
- Market Research: By calculating descriptive statistics for survey responses or product sales, companies can make informed decisions based on actual customer behavior and preferences.

The 5 number summary calculator simplifies the process of comparing multiple data sets. Key measures provided by the calculator include the minimum value, 1st quartile (25th percentile), median (2nd quartile, 50th percentile), upper (3rd) quartile (75th percentile), and maximum value. By comparing these values, users can identify differences between the distributions of the data sets.

For example, a user could compare the sales performance of two products by analyzing their respective 5 number summaries. Differences in the medians or quartiles could reveal which product has a higher overall sales volume or satisfies customer preferences better.

Another application involves comparing the performance of different investments. By analyzing the return on investment (ROI) for multiple options using the 5 number summary, investors can make better-informed decisions about which investments to pursue, taking into consideration factors such as their risk tolerance and investment goals.

Finally, researchers could use the 5 number summary to compare the effectiveness of different treatment methods in medical studies. By comparing the summary statistics of patient outcomes across different treatment groups, researchers can identify which methods may be most effective and warrant further investigation.

In conclusion, the 5 number summary calculator is a versatile tool that can provide valuable insights and inform decision-making across various fields and applications. By understanding and utilizing its capabilities, users can make more data-driven choices and improve their understanding of underlying trends and patterns.

For convenience, we've enclosed two additional measures (10th and 90th percentile) which can be used to generate
a similar package known as the seven number summary. The additional two metrics gives you better visibility into
what is happening at the tails of the distribution. While outliers and distribution tails are a small fraction of
your data, they can frequently have a disproportionate impact on overall performance. For example, a group of
likely voters may exhibit a range of satisfaction scores with a particular candidate - but only the top and bottom
10% is truly motivated enough to take action based on their opinions. In business, similar models can be used to
explain customer defection to another supplier and contribution margin economics within a distribution business.

This tool is designed to make it easy to repeat statistical calculations. You can save your data to local device
storage (if your phone or computer supports HTML5), allowing you to retrieve and edit data from past calculations.
A list of saved datasets is provided below the main calculation area - click on the name of the dataset and the
data table above will update. Important: these are locally saved only (cannot be accessed on other devices, are
not sent to our servers, and will be deleted if your cache is cleared). If you need to save this data permanently
or share it between devices (or with a colleage), send it as a link. Click on the dataset name to load it into the
list of data points in the calculator, hit the calculate button, and copy the URL. You can easily email the URL to
your colleagues or post it on a message board. When anyone clicks on the URL, it will contain the shared values.