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