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Descriptive Statistics
Watch comprehensive video reviews for Descriptive Statistics, designed for final exam preparation. Each video includes integrated problems you can solve alongside detailed solutions.
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Interactive Problems: Solve problems alongside the video with step-by-step guidance and detailed solutions.
Exam Preparation: Complete unit reviews designed for final exam preparation with all key concepts covered systematically.
Expert Teaching: High-quality instruction from Perplex co-founder James Mullen with clear explanations, worked examples, and exam tips.
Not your average video:
Interactive Problems: Solve problems alongside the video with step-by-step guidance and detailed solutions.
Exam Preparation: Complete unit reviews designed for final exam preparation with all key concepts covered systematically.
Expert Teaching: High-quality instruction from Perplex co-founder James Mullen with clear explanations, worked examples, and exam tips.
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SL 4.3
The interquartile range, denoted IQR, is the difference between the third and first quartile:
A value x in a dataset is said to be an outlier if x<Q1−1.5×IQR or x>Q1+1.5×IQR.
For example, in the dataset [0,7,8,9,10,11,18], the quartiles are Q1=7, Q2=9, Q3=11.
Hence
Then since 0<Q1−1.5×IQR=1, 0 is an outlier.
Similarly, since 18>Q3+1.5×IQR=17, so 18 is an outlier.
SL 4.3
The interquartile range, denoted IQR, is the difference between the third and first quartile:
A value x in a dataset is said to be an outlier if x<Q1−1.5×IQR or x>Q1+1.5×IQR.
For example, in the dataset [0,7,8,9,10,11,18], the quartiles are Q1=7, Q2=9, Q3=11.
Hence
Then since 0<Q1−1.5×IQR=1, 0 is an outlier.
Similarly, since 18>Q3+1.5×IQR=17, so 18 is an outlier.