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Aug 6th, 2022
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How to Transformation Statistic Deed For Free

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The video tutorial discusses a random variable x representing the height of a person, shown with a normal distribution curve. The mean and standard deviation are illustrated, and the video explores how these values are affected when adding or scaling the random variable. The impact on the distribution, mean, and standard deviation is the focus of the tutorial.

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Too many extreme values in a data set will result in a skewed distribution. Normality of data can be achieved by cleaning the data. This involves determining measurement errors, data-entry errors and outliers, and removing them from the data for valid reasons.
Checking the distribution with Skewness. Checking the distribution of some variables using Histogram. Checking the distribution of variables using KDE plot. Checking the distribution of variables using a Q-Q plot. Transformations to change the distribution of features.
If you do have normal distribution you can use mean and standard deviation for summary. If you do not have normal distribution you need to use median and IQR instead.
The answer is no! The variable that is supposed to be normally distributed is just the prediction error.
Some common heuristics transformations for non-normal data include: square-root for moderate skew: sqrt(x) for positively skewed data, log for greater skew: log10(x) for positively skewed data, inverse for severe skew: 1/x for positively skewed data. Linearity and heteroscedasticity:
Checking the distribution with Skewness. Checking the distribution of some variables using Histogram. Checking the distribution of variables using KDE plot. Checking the distribution of variables using a Q-Q plot. Transformations to change the distribution of features.
A skewed distribution is neither symmetric nor normal because the data values trail off more sharply on one side than on the other. In business, you often find skewness in data sets that represent sizes using positive numbers (eg, sales or assets).
Checking the distribution with Skewness. Checking the distribution of some variables using Histogram. Checking the distribution of variables using KDE plot. Checking the distribution of variables using a Q-Q plot. Transformations to change the distribution of features.
Too many extreme values in a data set will result in a skewed distribution. Normality of data can be achieved by cleaning the data. This involves determining measurement errors, data-entry errors and outliers, and removing them from the data for valid reasons.
x=log(x+1) -often used for transforming data that are right-skewed, but also include zero values. -note that the shape of the resulting distribution will depend on how big x is compared to the constant 1. Therefore the shape of the resulting distribution depends on the units in which x was measured.

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