Central Tendency केंद्रीय प्रवृति
Central tendency is the middle point of a distribution.केंद्रीय प्रवृति भनेको संपूर्ण तथ्याङ्गकलाई प्रतिनिधित्व गर्ने एउटा मान वा औसत हो।
Types of measure Central Tendency केंद्रीय प्रवृति नापका प्रकार
- Airthmetic Mean अंक गणितीय मध्यक
- Mean मध्यिका
- Mode बहुलक
Airthmetic Mean अंक गणितीय मध्यक
The Arthmetic mean is defiened as the sum of all observation or items divided by the nmber of observations. विभिन्न मान तथा पदहरुका मूल्यहरूको योगफललाई मान वा पदहरुको संख़्याले भाग गर्दा प्राप्त हुने संख्या वा मानलाई अंकगणितीय मध्यक भनिन्छ।
Computation Method Of Airthmetic Mean अंक गणितीय मध्यक गणना गर्ने बिधि
- Individual Series ब्याग्तिगत श्रेणी
- Discrete Series खंडित श्रेणी
- Continuous Series अविच्छिन्न श्रेणी
Advantage Of Airthmetic Mean
- It is easy to calculate and easy to understand
- It is based on all observations
- It is rigidly defiened
- It is least affected by fluctuations of sampling
- It is suitable for furthur mathmetical applications.
Median मध्यिका
Median is the middle most or the most central value of the variable in a set of observations.
कुनै पनि श्रेणीको वा तथ्याङ्कको समूहमा केंद्रीय स्थानमा रहेको एउटा ठीक बिच व केंद्रीय पदको मानलाइ मध्यिका भनिन्छ।
Computation Method Of Median मध्यिका गणना गर्ने बिधि
- Individual Series ब्याग्तिगत श्रेणी
- Discrete Series खंडित श्रेणी
- Continuous Series अविच्छिन्न श्रेणी
Advantages of Median
- It is easy to understood
- It can be located graphically
- It is the best measures for qualitative data like beauty, intelligence, honsty etc.
Mode बहुलक
The number of items that occurs most often in a series or data is called mode.
कुनै पनि श्रेणी वा तथ्याङ्कका मानहरू मध्ये सबैभन्दा बढ़ी बारम्बार दोहोरिने पदमानलाई बहुलक Mode भनिन्छ।
नोट : कुनै श्रेणीमा जुन पदको बढ़ी आवृति (Frequency ) हुन्छ त्यहि पदमान नै बहुलक Mode हो।
उदहारण : 2,4,3,2,1,5
दिएको श्रेणीमा बढ़ी पटक आउने पद 2 छन।
तसर्थ बहुलक (Mode ) = 2 हुन्छ।
Advantades Of Mode बहुलक का फाईदाहरु
- It can be easy to underastand
- In some cases, it can be located by inspections.
- It is not affected by extreme values.
- It is useful for both qualitative and quantative data
- It can be calculated for ended classes.
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