Ukuran Penyebaran

Appropriate Measures of Central Tendency  Nominal variables

Mode

 Ordinal variables

Median

 Interval level variables

Mean - If the distribution is normal (median is better with skewed distribution)

Central tendency and the shape of the distribution

Ukuran Penyebaran  Suatu

data yang mempunyai kecenderungan (tendensi) pusat misalnya rata-rata yang sama belum tentu mempunyai penyebaran data yang sama pula.  Ukuran penyebaran (variasi) menyatakan seberapa jauh nilai amatan yang sebenarnya menyimpang atau berbeda dengan nilai pusatnya.  kegunaan dari ukuran variasi ini : untuk mengetahui seberapa jauh observasi melenceng dari nilai rataratanya.

 How well does the mean represent the scores in a

distribution? The logic here is to determine how much spread is in the scores.  How much do the scores "deviate" from the mean? Think of the mean as the true score or as your best guess.  If every X were very close to the Mean, the mean would be a very good predictor.  If the distribution is very sharply peaked then the mean is a good measure of central tendency and if you were to use the mean to make predictions you would be right or close much of the time.

 The larger the standard deviation figure, the wider the range of

distribution away from the measure of central tendency

Ukuran-Ukuran Penyebaran  Range (Jangkauan)  Merupakan selisih nilai observasi tertinggi dengan

nilai observasi terendah.  Contoh

Variansi  Variansi populasi

Contoh : Tentukan variansi dari 5, 7, 2, 2, 4 ! Peny : Rata-rata =5 Variansi = 4.5