Skewness example sentences
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- "Skewness" example sentences
Related (8): asymmetry, non-normality, peakedness, kurtosis, tails, outliers, variance, distribution
"Skewness" Example Sentences
- The distribution had a positive skewness, indicating a longer tail on the right side.
- The data set showed a significant degree of skewness, with most values clustered around the median.
- The histogram displayed a clear amount of skewness, with a larger number of data points on one side.
- It is important to consider the skewness of the data when interpreting the results.
- The outlier values in the data set were responsible for the skewness in the overall distribution.
- The skewness in the data set made it difficult to determine the true central tendency of the variables.
- The distribution of the data was highly skewed, indicating a non-normal distribution.
- The skewness of the data was corrected using a log transformation to normalize the distribution.
- The skewness of the histogram suggested that the data set may contain some extreme values.
- The lack of skewness in the data set indicated a relatively uniform distribution of values.
- The data set had a negative skewness, with most values clustered around the higher end.
- The skewness in the data was caused by the presence of a few outlier values.
- The distribution displayed a moderate degree of skewness, which was accounted for in the analysis.
- It is important to account for the skewness of the data when performing statistical tests.
- The skewness of the distribution was corrected using a power transformation.
- The skewness of the data set made it difficult to compare the results to other studies.
- The degree of skewness in the data set varied depending on the analysis method used.
- The skewness in the data set was analyzed using a box plot.
- The distribution was found to have a high degree of positive skewness, indicating a non-normal distribution.
- The skewness in the data set was accounted for using a robust regression analysis.
- The skewness in the variable made it difficult to draw any meaningful conclusions from the data.
- The presence of skewness in the data set could be due to measurement error or sampling bias.
- The skewness of the data set was evident in the scatter plot.
- The skewness of the distribution suggested that normality assumptions may not be met.
- The degree of skewness in the data set was found to be statistically significant.
- The skewness in the data set may be due to an underlying asymmetry in the population.
- The level of skewness in the data set required a non-parametric statistical analysis.
- The presence of skewness in the data set was accounted for using a robust standard error estimation method.
- The degree of skewness in the data set was reduced using data transformation techniques.
- The skewness in the data set was found to be closely related to the presence of extreme values.
Common Phrases
- The data distribution is positively skewed
- The skewness measure indicates a negatively skewed distribution
- Skewness is a measure of the data's asymmetry
- The skewness coefficient is higher than 0, indicating a right-skewed distribution
- The data is skewed towards the right tail
- The mean is greater than the mode, indicating positive skewness
- The distribution has a longer tail on the left, as indicated by negative skewness
- Skewness can affect the accuracy of certain statistical tests
- The data is not normally distributed due to significant skewness
- Transformation of the data may be necessary to correct skewness.
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