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artiny
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Homework Statement
On this vector I didnt get the right answer:
x = [ 5 8 10 11 137 ] [/B]
q3: i = (p/100)*n = (75/100)*5 = 3.75 >> 4th in vector is 11
q1: i = (25/100)*5 = 1.25 >> 2nd is 8
in MATLAB is solution
{statics} Kvartil (quartiles) why I didnt get right solution ?
- Thread starter artiny
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In summary, quartiles are values that divide a dataset into four equal parts and are important in statistics for providing information about the spread and distribution of data. To calculate quartiles, the data must be arranged in ascending order and the median of the entire dataset is used to determine the first and third quartiles, with the second quartile being the same as the median. Different methods of calculating quartiles can result in slightly different values, and quartiles can be used to compare datasets, although other factors should also be considered. If unable to find a solution using quartiles, other measures of central tendency and variability can be used, or assistance from a statistician may be helpful.
On this vector I didnt get the right answer:
x = [ 5 8 10 11 137 ] [/B]
q3: i = (p/100)*n = (75/100)*5 = 3.75 >> 4th in vector is 11
q1: i = (25/100)*5 = 1.25 >> 2nd is 8
in MATLAB is solution
- #2
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Never seen this concept before, but from a little reading at http://au.mathworks.com/help/stats/quantiles-and-percentiles.html it seems that the vector is interpreted as:
1/10 are <= 5
3/10 are <= 8,
5/10 are <= 10
etc.
For the first quartile, we want find how many are <= 0.25. This is done by interpolation between1/10 and 3/10. 0.25 is positioned (0.25-0.1)/(0.3-0.1) along that interval, so gets that share of (8-5):
5 + (8-5)*(0.25-0.1)/(0.3-0.1) = 7.25.
1/10 are <= 5
3/10 are <= 8,
5/10 are <= 10
etc.
For the first quartile, we want find how many are <= 0.25. This is done by interpolation between1/10 and 3/10. 0.25 is positioned (0.25-0.1)/(0.3-0.1) along that interval, so gets that share of (8-5):
5 + (8-5)*(0.25-0.1)/(0.3-0.1) = 7.25.
Related to {statics} Kvartil (quartiles) why I didnt get right solution ?
1. What are quartiles and why are they important in statistics?
Quartiles are values that divide a dataset into four equal parts. They are important in statistics because they provide information about the spread and distribution of the data. They also help identify outliers and determine the overall shape of the data.
2. How do you calculate quartiles in statistics?
To calculate quartiles, you first need to arrange the data in ascending order. Then, find the median of the entire dataset. The first quartile (Q1) is the median of the lower half of the data, and the third quartile (Q3) is the median of the upper half of the data. The second quartile (Q2) is the same as the median of the entire dataset.
3. Why do I sometimes get different quartile values for the same dataset?
Different methods can be used to calculate quartiles, such as the exclusive and inclusive methods. These methods may result in slightly different quartile values. Additionally, if the dataset has an even number of values, there may be two different values that could be considered the median, resulting in different quartile values depending on which value is chosen.
4. Can quartiles be used to compare datasets?
Yes, quartiles can be used to compare datasets. By looking at the quartile values, you can see how the data is spread out and identify any differences between the datasets. However, it is important to consider other factors as well, such as the mean and standard deviation, when comparing datasets.
5. What should I do if I am unable to find a solution using quartiles?
If you are unable to find a solution using quartiles, it may be helpful to look at other measures of central tendency and variability, such as the mean and standard deviation. You can also try using different methods for calculating quartiles or consult with a statistician for assistance.
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