📊 Skewness and kurtosis are calculations used to measure the distortion in a normal curve.

⚖️ A symmetrical distribution is equally distributed on both sides of the mean, with a bell-shaped or U-shaped graph.

📈 Pearson's coefficient of skewness measures the degree of skewness in a distribution.

📊 Skewness is used to measure the level of asymmetry in a distribution.

🔄 Skewed distributions can be either positively skewed or negatively skewed.

⬆️⬇️ In a negatively skewed distribution, the data is concentrated towards the right, while in a positively skewed distribution, it is concentrated towards the left.

📈 Skewness refers to the departure from symmetry in a frequency distribution, with positive skewness indicating a longer right tail and negative skewness indicating a longer left tail.

📐 Kurtosis measures the shape of a distribution by assessing the concentration of values in the tails compared to the center.

🧮 Pearson's coefficient of skewness calculates the horizontal distance between the mean and the mode to determine the degree and direction of skewness.

📊 Skewness is a measure of the asymmetry of a distribution, with positive value indicating right-skewed distribution and negative value indicating left-skewed distribution.

📏 Pearson's coefficient is used to calculate skewness, with a value between -0.5 and 0.5 indicating a symmetrical distribution.

📈 Kurtosis measures the presence of outliers in the data, with high kurtosis indicating heavy-tailed distributions.

📊 Skewness measures the extent of symmetry in a distribution.

🏔️ Kurtosis measures the degree of peakness in a distribution.

📈 Skewness and kurtosis are used to describe the spread of a normal distribution.

📊 Skewness and kurtosis are measures used to understand how spread out the data is and the shape of its distribution.

📈 Skewness measures the asymmetry of the data, while kurtosis measures the peakedness of the distribution.

📘 Understanding skewness and kurtosis can help in analyzing data patterns and making statistical inferences.

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