Statistics Lectures - 6: Skewness, Normal Distribution & Empirical Rule

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4: Central Tendency
5: Variance & Percentiles
7: Z-Scores
8: Probability
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Statistics - Lecture 16: Skewness

Skewness describes how the distribution of data “leans” away from a normal curve.
A symmetrical distribution has no skew. The mean exists perfectly at the center.
If a distribution is skewed to the right. The mean is pulled to the left from the center.
If a distribution is skewed to the left. The mean is pulled to the right from the center.
Skewness is important to recognize because it has implications in hypothesis testing.

Statistics - Lecture 17: The Normal Curve And Empirical Rule

The Normal Curve or normal distribution describes a bell-shaped symmetrical distribution of data.
The distribution of most continuous random variables will follow the shape of the normal curve.
Mean, Median, and Mode all exists at the center.
The graph changes direction at the inflection point.
The Empirical Rule states that:

• 65% of all values fall within 1 standard deviation of the mean.
• 95% of all values fall within 2 standard deviations from the mean.
• 99.7% of all values fall within 3 standard deviations from the mean.

Statistics Lecture Series - Table Of Contents

• Descriptive Statistics
• Probability
• Correlation
• Inferential Statistics

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