Topic 1 Question 106
A Data Scientist is building a linear regression model and will use resulting p-values to evaluate the statistical significance of each coefficient. Upon inspection of the dataset, the Data Scientist discovers that most of the features are normally distributed. The plot of one feature in the dataset is shown in the graphic.
What transformation should the Data Scientist apply to satisfy the statistical assumptions of the linear regression model?Exponential transformation
Logarithmic transformation
Polynomial transformation
Sinusoidal transformation
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コメント(7)
I would say B. Logarithmic transformation converts skewed distributions towards normal
👍 20astonm132021/09/21I would also go for B, as Log transformation is often mentioned, when we are talking about right (positive) skewness.
👍 3konradL2021/10/01I think it's B. reference: https://corporatefinanceinstitute.com/resources/knowledge/other/positively-skewed-distribution/#:~:text=For%20positively%20skewed%20distributions%2C%20the,each%20value%20in%20the%20dataset. "For positively skewed distributions, the most popular transformation is the log transformation. The log transformation implies the calculations of the natural logarithm for each value in the dataset. The method reduces the skew of a distribution. Statistical tests are usually run only when the transformation of the data is complete."
👍 2YJ42192021/10/07
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