Mathematics/Statistics/Data Characteristics - Revision history

Brodriguez: Brodriguez moved page Statistics/Data Characteristics to Mathematics/Statistics/Data Characteristics

2020-10-25T17:20:34Z

Brodriguez moved page Statistics/Data Characteristics to Mathematics/Statistics/Data Characteristics

← Older revision

Revision as of 17:20, 25 October 2020

Brodriguez: Brodriguez moved page Statistics - Data Characteristics to Statistics/Data Characteristics: Clean url with subpages

2020-05-15T08:51:38Z

Brodriguez moved page Statistics - Data Characteristics to Statistics/Data Characteristics: Clean url with subpages

← Older revision

Revision as of 08:51, 15 May 2020

Brodriguez: Add notation note

2020-05-13T23:53:19Z

Add notation note

← Older revision		Revision as of 23:53, 13 May 2020
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	80th quantile - Indicates that 80% of the data is below it and 20% of the data is above it.		80th quantile - Indicates that 80% of the data is below it and 20% of the data is above it.
	90th quantile - Indicates that 90% of the data is below it and 10% of the data is above it.		90th quantile - Indicates that 90% of the data is below it and 10% of the data is above it.

			{{ Note \| Quantiles sometimes have different names, depending on the text. For example, what we define here as the "50th quantile" may sometimes be referred to in other texts as "the 0.5 quantile" or "the 50% quantile". Yay inconsistent notations! }}

Brodriguez: Add quantile example

2020-05-13T06:04:33Z

Add quantile example

← Older revision		Revision as of 06:04, 13 May 2020
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	== Quantiles ==		== Quantiles ==
	Similarly to quartiles, a quantile splits up data into evenly sized sections. The difference is that a quartile is a specific type of quantile, that always divides into fourths. Meanwhile, a quantile can be split into any number of sections.		Similarly to quartiles, a quantile splits up data into evenly sized sections. The difference is that a quartile is a specific type of quantile, that always divides into fourths. Meanwhile, a quantile can be split into any number of sections.

			For example, given a set of data, we can determine a quantile for every 10%. In this case, we would have the following quantiles:<br>
			10th quantile - Indicates that 10% of the data is below it and 90% of the data is above it.
			20th quantile - Indicates that 20% of the data is below it and 80% of the data is above it.
			30th quantile - Indicates that 30% of the data is below it and 70% of the data is above it.
			40th quantile - Indicates that 40% of the data is below it and 60% of the data is above it.
			50th quantile - Indicates that 50% of the data is below it and 50% of the data is above it.
			60th quantile - Indicates that 60% of the data is below it and 40% of the data is above it.
			70th quantile - Indicates that 70% of the data is below it and 30% of the data is above it.
			80th quantile - Indicates that 80% of the data is below it and 20% of the data is above it.
			90th quantile - Indicates that 90% of the data is below it and 10% of the data is above it.

Brodriguez: Correct quantile section

2020-05-13T05:41:56Z

Correct quantile section

← Older revision		Revision as of 05:41, 13 May 2020
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	== Quantiles ==		== Quantiles ==
	Similarly to quartiles, a quantile splits up data into ~~even~~ sections.~~<br>~~		Similarly to quartiles, a quantile splits up data into evenly sized sections. The difference is that a quartile is a specific type of quantile, that always divides into fourths. Meanwhile, a quantile can be split into any number of sections.
	The difference is that a quartile is a specific type of quantile, that always divides into fourths.~~<br>~~
	A quantile can be split into any number of sections.

	{{ Note \| Sometimes, rather than splitting data i nto evenly spaced sections, they are used to divide a [[Statistics - Distributions\|distribution]] into equal sized groups, where each group contains an equivalent fraction of data. }}

Brodriguez: Add quantile section

2020-05-13T05:34:15Z

Add quantile section

← Older revision		Revision as of 05:34, 13 May 2020
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	Q3 - [7, 8]		Q3 - [7, 8]
	Q4 - [10, 11]		Q4 - [10, 11]


			== Quantiles ==
			Similarly to quartiles, a quantile splits up data into even sections.<br>
			The difference is that a quartile is a specific type of quantile, that always divides into fourths.<br>
			A quantile can be split into any number of sections.

			{{ Note \| Sometimes, rather than splitting data i nto evenly spaced sections, they are used to divide a [[Statistics - Distributions\|distribution]] into equal sized groups, where each group contains an equivalent fraction of data. }}

Brodriguez: More formatting

2020-05-12T11:49:44Z

More formatting

← Older revision		Revision as of 11:49, 12 May 2020
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	50th - 3.5		50th - 3.5
	75th - 5		75th - 5


	== Quantiles ==		== Quantiles ==

Brodriguez: General page formatting

2020-05-12T11:49:23Z

General page formatting

← Older revision		Revision as of 11:49, 12 May 2020
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	The following details some basic characteristics of data in statistics.		The following details some basic characteristics of data in statistics. See also [[Statistics - Core Measurements\|Core Measurements]].

	~~See also [[Statistics - Core Measurements\|Core Measurements]]~~.		Note that all of the following assume an ordered set of items.


	~~{{ ToDo \| Expand page. }}~~

	== Min ==		== Min ==
	~~Given a set of items, the~~ Min is the lowest single unique value.		The Min is the lowest single unique value.
	For example, given a set of [1, 1, 2, 2, 3, 3, 4, 4, 5, 5], 1 is the min.		For example, given a set of [1, 1, 2, 2, 3, 3, 4, 4, 5, 5], 1 is the min.


	== Max ==		== Max ==
	~~Given a set of items, the~~ Max is the highest single unique value.		The Max is the highest single unique value.
	For example, given a set of [1, 1, 2, 2, 3, 3, 4, 4, 5, 5], 5 is the max.		For example, given a set of [1, 1, 2, 2, 3, 3, 4, 4, 5, 5], 5 is the max.


	== Percentile ==		== Percentile ==
	~~Given a set of items, a~~ Percentile is the value that marks 25%, 50% or 75% in your data.		A Percentile is the value that marks 25%, 50% or 75% in your data.

	In other words, find the [[Statistics - Core Measurements#Median \| median]] of your data. This marks the 50th percentile.<br>		In other words, find the [[Statistics - Core Measurements#Median \| median]] of your data. This marks the 50th percentile.<br>
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	== Quantiles ==		== Quantiles ==
	~~Given a set of items, a~~ Quartile is a segment of the data when split up into four chunks. Note that the set must be ordered for this.		A Quartile is a segment of the data when split up into four chunks. Note that the set must be ordered for this.

	There are four distinct quartiles with unique names:<br>		There are four distinct quartiles with unique names:<br>
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	50th - 6		50th - 6
	75th - 9		75th - 9

	If including, we first split into [1, 2, 3, 4, 5, 6] and [6, 7, 8, 9, 10, 11].		If including, we first split into [1, 2, 3, 4, 5, 6] and [6, 7, 8, 9, 10, 11].
	Then we split again to get our quartiles.		Then we split again to get our quartiles.
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	Q3 - [6, 7, 8]		Q3 - [6, 7, 8]
	Q4 - [9, 10, 11]		Q4 - [9, 10, 11]

	If excluding, we first split into [1, 2, 3, 4, 5] and [7, 8, 9, 10, 11].		If excluding, we first split into [1, 2, 3, 4, 5] and [7, 8, 9, 10, 11].
	Then we split again to get our quartiles:		Then we split again to get our quartiles:
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	Q3 - [7, 8]		Q3 - [7, 8]
	Q4 - [10, 11]		Q4 - [10, 11]


	== Outliers ==		== Outliers ==
	~~Given a set of items, an~~ outlier is an item that does not fit in with the rest. It's usually extremely low or extremely high, compared to the other values in the set.		An outlier is an item that does not fit in with the rest. It's usually extremely low or extremely high, compared to the other values in the set.
	For example, given a set of [1, 2, 3, 4, 50], 50 is an outlier, as it's much higher than the rest of the values.		For example, given a set of [1, 2, 3, 4, 50], 50 is an outlier, as it's much higher than the rest of the values.

Brodriguez: Add percentiles and quartiles

2020-05-12T11:47:18Z

Add percentiles and quartiles

@@ Line 7: / Line 7: @@
 == Min ==
-Given a set of items, the min is the lowest single unique value.
+Given a set of items, the Min is the lowest single unique value.
   For example, given a set of [1, 1, 2, 2, 3, 3, 4, 4, 5, 5], 1 is the min.
 == Max ==
-Given a set of items, the max is the highest single unique value.
+Given a set of items, the Max is the highest single unique value.
   For example, given a set of [1, 1, 2, 2, 3, 3, 4, 4, 5, 5], 5 is the max.
 == Quantiles ==
 == Outliers ==
 Given a set of items, an outlier is an item that does not fit in with the rest. It's usually extremely low or extremely high, compared to the other values in the set.
   For example, given a set of [1, 2, 3, 4, 50], 50 is an outlier, as it's much higher than the rest of the values.

Brodriguez: Add min and max

2020-05-12T10:38:25Z

Add min and max

← Older revision		Revision as of 10:38, 12 May 2020
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	== Min ==		== Min ==
			Given a set of items, the min is the lowest single unique value.
			For example, given a set of [1, 1, 2, 2, 3, 3, 4, 4, 5, 5], 1 is the min.

	== Max ==		== Max ==
			Given a set of items, the max is the highest single unique value.
			For example, given a set of [1, 1, 2, 2, 3, 3, 4, 4, 5, 5], 5 is the max.

	== Quantiles ==		== Quantiles ==
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	== Outliers ==		== Outliers ==
	Given a set of items, an outlier is an item that does not fit in with the rest. It's usually extremely low or extremely high, compared to the other values in the set.		Given a set of items, an outlier is an item that does not fit in with the rest. It's usually extremely low or extremely high, compared to the other values in the set.

	For example, given a set of [1, 2, 3, 4, 50], 50 is an outlier, as it's much higher than the rest of the values.		For example, given a set of [1, 2, 3, 4, 50], 50 is an outlier, as it's much higher than the rest of the values.