Measure of Central Tendency Statistics:- PDF / PPT

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Measure of Central

                    Tendency

                       Statistics

 

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Measure of Central Tendency

• Central tendency is a statistical

  measure that determines a single

  value that accurately describes the

  center of the distribution and

  represents the entire distribution of

  scores.

 

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Types of Averages

There are five common type, namely;

Arithmetic Mean (AM)

Median

Mode

Geometric Mean (GM)

Harmonic Mean (HM)

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Arithmetic Mean

• “The sum of all observations divide by the

  total number of observation”.

 

             𝑆𝑢𝑚 𝑜𝑓 𝑎𝑙𝑙 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛

  Mean =

           𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑙𝑙 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠

 

 

 

 

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Arithmetic Mean

 AM for raw data

 

      Find mean for the data

 45, 32, 37, 46, 39, 36, 41, 48, 36

                n=9

     45+32+37+46+39+36+41+48+36

𝑥= ∙

                   𝑛

                𝟑𝟔𝟎 Sample Population data

          𝒙= = 40 data

                 𝟗 www.DuloMix.com n N

Arithmetic Mean

         AM for Grouped data

 

Classes Frequency Mid Points (x) fx

 68-87 10 77.5 775

88-107 13 97.5 1267.5

108-127 15 117.5 1762.5

128-147 9 137.5 1237.5

148-167 4 157.5 630

 

              𝑓 = 51 𝑓𝑥 = 5402.5

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Median

• “A value which divides a data set that

  have been ordered into two equal parts”.

 

                     OR

 

• “A median is a value at or below which

  50% of ordered data lie”.

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Median

 

 

 

 

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Median

Median for raw data

  For even size

            Find Median for the data

                 0, 5, 3, 2, 6, 7

 

                      0, 2, 3, 5, 6, 7

                           n=6

                              𝑡ℎ 𝑡ℎ

                  1 6 6

𝑥 = The size of item + +1 item

                  2 2 2

 

o Symmetrical distribution

o Asymmetrical distribution

 

 

1. Symmetrical distribution

 The observations are equally distributed.

 The values of mean, median and mode are always equal.

   i.e. Mean = Median = Mode

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Relations Among Averages

2. Asymmetrical distribution

    The observations are not equally distributed.

    Two possibilities are there:

 

        Positively Skewed Negatively Skewed

 

 

 

 

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Quantiles

• When the number of observation is quite large, the

  principle according to which a distribution or an

  ordered data set is divided into two equal parts, may

  be extended to any number of divisions.

• These are:

1. Quartiles

2. Deciles

3. Percentiles

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1. Quartiles

• “The three values which divide the

  distribution into four equal parts”.

• These values are denoted by 𝑸𝟏, 𝑸𝟐 𝒂𝒏𝒅 𝑸𝟑.

• 𝑄1 𝑖𝑠 𝑐𝑎𝑙𝑙𝑒𝑑 𝑓𝑖𝑟𝑠𝑡 𝑜𝑟 𝑙𝑜𝑤𝑒𝑟 𝑞𝑢𝑎𝑟𝑡𝑖𝑙𝑒.

• 𝑄2 𝑖𝑠 𝑐𝑎𝑙𝑙𝑒𝑑 𝑠𝑒𝑐𝑜𝑛𝑑 𝑜𝑟 𝑚𝑖𝑑𝑑𝑙𝑒 𝑞𝑢𝑎𝑟𝑡𝑖𝑙𝑒.

• 𝑄3 𝑖𝑠 𝑐𝑎𝑙𝑙𝑒𝑑 𝑡ℎ𝑖𝑟𝑑 𝑜𝑟 𝑢𝑝𝑝𝑒𝑟 𝑞𝑢𝑎𝑟𝑡𝑖𝑙𝑒.

 

 

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Quartiles

 

                𝑛

𝑸𝟏 = size of + 1 𝑡ℎ 𝒊𝒕𝒆𝒎

                4

               2𝑛

𝑸𝟐 = size of + 1 𝑡ℎ 𝒊𝒕𝒆𝒎

                4

               3𝑛

𝑸𝟑 = size of + 1 𝑡ℎ 𝒊𝒕𝒆𝒎

                4

 

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2. Deciles

• “The nine values which divide

  the distribution into ten equal

  parts”.

• These values are denoted by 𝑫𝟏, 𝑫𝟐, … , 𝑫𝟗.

• Each Decile contains 10% of the total number

  of observations.

 

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Deciles

               𝑛 6𝑛

𝑫𝟏 = size of + 1 𝑡ℎ 𝒊𝒕𝒆𝒎 𝑫𝟔 = size of + 1 𝑡ℎ 𝒊𝒕𝒆𝒎

               10 10

               2𝑛

𝑫𝟐 = size of + 1 𝑡ℎ 𝒊𝒕𝒆𝒎 7𝑛

               10 𝑫𝟕 = size of + 1 𝑡ℎ 𝒊𝒕𝒆𝒎

               3𝑛 10

𝑫𝟑 = size of + 1 𝑡ℎ 𝒊𝒕𝒆𝒎

               10 8𝑛

               4𝑛 𝑫𝟖 = size of + 1 𝑡ℎ 𝒊𝒕𝒆𝒎

𝑫𝟒 = size of + 1 𝑡ℎ 𝒊𝒕𝒆𝒎 10

               10

               5𝑛 9𝑛

𝑫𝟓 = size of + 1 𝑡ℎ 𝒊𝒕𝒆𝒎 𝑫𝟗 = size of + 1 𝑡ℎ 𝒊𝒕𝒆𝒎

               10 10

 

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3. Percentiles

• “The ninety nine values which divide

  the distribution into hundred equal

  parts”.

• These values are denoted by 𝑷𝟏, 𝑷𝟐, … , 𝑷𝟗𝟗.

• Each Decile contains 1% of the total number

  of observations.

 

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