Interquartile range

수학노트
둘러보기로 가기 검색하러 가기

노트

  • The interquartile range (IQR) is a measure of variability, based on dividing a data set into quartiles.[1]
  • You may have heard the term interquartile range and asked yourself, "What is interquartile range anyway?".[2]
  • The interquartile range (IQR) of a dataset tells us how bunched up or spread out its values are (its a measure of variability).[2]
  • This equation is how to calculate IQR and is the interquartile range definition.[2]
  • On a box plot, the IQR is shown as the main body of the plot (the box's height).[2]
  • In this tutorial, you're going to learn about the range and the interquartile range.[3]
  • The interquartile range, also abbreviated IQR, is the difference between the two quartiles.[3]
  • You shouldn't mix and match saying the mean is the measure of center and then reporting IQR as the measure of spread.[3]
  • So we talked about range and interquartile range.[3]
  • Before studying interquartile range, we first should study quartiles for they act as a base for the interquartile range.[4]
  • Now comes the turn of interquartile range.[4]
  • The most notable difference is with respect to the practice of narrowing the range, using statistical tools such as the interquartile range.[5]
  • The IQR accentuates the central range of the data rather than the maximum and minimum values.[6]
  • To determine the IQR, the data are first arranged in ascending order and subdivided into four ...[6]
  • The rng parameter allows this function to compute other percentile ranges than the actual IQR.[7]
  • The default is to compute the IQR for the entire array.[7]
  • The difference between the 75th and 25th percentile is called the interquartile range.[8]
  • results by incorporating the additional information of the interquartile range (IQR).[9]
  • One main reason to report C 3 is because the IQR is usually less sensitive to outliers compared to the range.[9]
  • Interquartile range gives another measure of variability.[10]
  • Carefully, observe the above first IQR example when it is plotted in a boxplot.[10]
  • The interquartile range is a widely accepted method to find outliers in data.[11]
  • When using the interquartile range, or IQR, the full dataset is split into four equal segments, or quartiles.[11]
  • We calculate the interquartile range by first finding the value in the middle of the top group, which is 54 in this case.[11]
  • But we can still work out the interquartile range if we had an even number of ages and couldn’t find middle values.[11]
  • The "interquartile range", abbreviated "IQR", is just the width of the box in the box-and-whisker plot.[12]
  • The IQR can be used as a measure of how spread-out the values are.[12]
  • Then click the button and scroll down to "Find the Interquartile Range (H-Spread)" to compare your answer to Mathway's.[12]
  • To find out if there are any outliers, I first have to find the IQR.[12]
  • We can use the IQR method of identifying outliers to set up a “fence” outside of Q1 and Q3.[13]
  • To build this fence we take 1.5 times the IQR and then subtract this value from Q1 and add this value to Q3.[13]
  • Any observations that are more than 1.5 IQR below Q1 or more than 1.5 IQR above Q3 are considered outliers.[13]
  • A long box in the boxplot indicates a large IQR, so the middle half of the data has a lot of variability.[14]
  • A short box in the boxplot indicates a small IQR.[14]
  • In each data set, the middle half of the data varies from 7 to 14, so the IQR is 7.[14]
  • It activity is to develop a deeper understanding of how the interquartile range (IQR) measures variability about the median.[14]
  • As seen above, the interquartile range is built upon the calculation of other statistics.[15]
  • Before determining the interquartile range, we first need to know the values of the first quartile and third quartile.[15]
  • Once we have determined the values of the first and third quartiles, the interquartile range is very easy to calculate.[15]
  • How far we should go depends upon the value of the interquartile range.[15]
  • To calculate the interquartile range, I just have to find the difference between these two things.[16]
  • So the interquartile range for this first example is going to be 13 minus five.[16]
  • Find the interquartile range of the data in the dot plot below.[16]
  • The IQR is a measure of variability, based on dividing a data set into quartiles.[17]
  • The IQR of a set of values is calculated as the difference between the upper and lower quartiles, Q 3 and Q 1 .[17]
  • The interquartile range is often used to find outliers in data.[17]
  • Outliers here are defined as observations that fall below Q1 − 1.5 IQR or above Q3 + 1.5 IQR.[17]
  • Like most technology, SPSS has several ways that you can calculate the IQR.[18]
  • You could take this route and then subtract the third quartile from the first to get the IQR.[18]
  • However, the easiest way to find the interquartile range in SPSS by using the “Explore” command.[18]
  • As such, the IQR of that data set is 6.5, calculated as 10.5 minus 4.[18]
  • We can see from these examples that using the inclusive method gives us a smaller IQR.[19]
  • In a boxplot, the width of the box shows you the interquartile range.[19]
  • To calculate the interquartile range from a set of numerical values, enter the observed values in the box.[20]
  • In order to calculate the IQR, we need to begin by ordering the values of the data set from the least to the greatest.[21]

소스

  1. ↑ Interquartile Range Formula (IQR Formula)
  2. ↑ 2.0 2.1 2.2 2.3 How to Find Interquartile Range?
  3. ↑ 3.0 3.1 3.2 3.3 Range and Interquartile Range (IQR)
  4. ↑ 4.0 4.1 Interquartile Range (IQR)
  5. ↑ The use of the interquartile range in transfer pricing1
  6. ↑ 6.0 6.1 SAGE Research Methods
  7. ↑ 7.0 7.1 scipy.stats.iqr — SciPy v1.5.4 Reference Guide
  8. ↑ Interpreting results: Quartiles and the interquartile range
  9. ↑ 9.0 9.1 Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range
  10. ↑ 10.0 10.1 Explore your Data: Range, interquartile range and box plot
  11. ↑ 11.0 11.1 11.2 11.3 What is the Interquartile Range?
  12. ↑ 12.0 12.1 12.2 12.3 Interquartile Ranges (IQRs) & Outliers
  13. ↑ 13.0 13.1 13.2 3.2 - Identifying Outliers: IQR Method
  14. ↑ 14.0 14.1 14.2 14.3 Concepts in Statistics
  15. ↑ 15.0 15.1 15.2 15.3 Understanding the Interquartile Range in Statistics
  16. ↑ 16.0 16.1 16.2 Interquartile range (IQR) (video)
  17. ↑ 17.0 17.1 17.2 17.3 Interquartile range
  18. ↑ 18.0 18.1 18.2 18.3 Interquartile Range (IQR): What it is and How to Find it
  19. ↑ 19.0 19.1 Understand, Calculate & Visualize IQR
  20. ↑ Interquartile Range Calculator
  21. ↑ How to find interquartile range

메타데이터

위키데이터

Spacy 패턴 목록

  • [{'LOWER': 'interquartile'}, {'LEMMA': 'range'}]
  • [{'LEMMA': 'IQR'}]
  • [{'LEMMA': 'midspread'}]
  • [{'LOWER': 'middle'}, {'LOWER': '50'}, {'LEMMA': '%'}]
  • [{'LOWER': 'h'}, {'OP': '*'}, {'LEMMA': 'spread'}]
원본 주소 "https://wiki.mathnt.net/index.php?title=Interquartile_range&oldid=51509"