Discuss a normal distribution, a positively skewed distribution, and a negatively skewed distribution.
A normal distributionis a distribution that is symmetrical, which means that if you drew a line down the center of the distribution, the left half would look exactly like the right half. A normal distribution has a single peak in the middle of the distribution, with fewer and fewer cases as you move away from this middle. The ends of a distribution of continuous scores are often called the “tails” of the distribution. A distribution has both a left or negative tail and a right or positive tail.
A positive skew has the long tail of the distribution to the right, and the right side of the number line moves toward positive numbers. In a distribution with a positive skew, therefore, most of the scores cluster at lower values of the variable and there is a long right tail, indicating that there are a lot of high values with few cases at each value.
A negative skew has the long tail of the distribution to the left, and the left side.Most of the scores cluster at the higher end of the variable.
A normal distributionis a distribution that is symmetrical, which means that if you drew a line down the center of the distribution, the left half would look exactly like the right half. A normal distribution has a single peak in the middle of the distribution, with fewer and fewer cases as you move away from this middle. The ends of a distribution of continuous scores are often called the “tails” of the distribution. A distribution has both a left or negative tail and a right or positive tail.
A positive skew has the long tail of the distribution to the right, and the right side of the number line moves toward positive numbers. In a distribution with a positive skew, therefore, most of the scores cluster at lower values of the variable and there is a long right tail, indicating that there are a lot of high values with few cases at each value.
A negative skew has the long tail of the distribution to the left, and the left side.Most of the scores cluster at the higher end of the variable.
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