Row 8477

Row ID: 8477 | Dataset Entry | Axioma AXP Content Repository

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I'm following some tutorials on doing some linear regression and as I was building my notebook, I'm working on outlier detection and amongst the techniques described for doing outlier detection, one of them involved calculating the Standard Deviation, but for this I need to know if my columns are of Guassian distribution. I'm aware that there are different techniques like:

* Histograms * KDE Plot * Q-Q Plot * Kolomogorov-Smirnov Test * Shapiro-Wilk Test * D'Agostino and Pearson's Test

And I bet there are a few more as well. So what is the best one to use? I guess Histograms just give a clue but do not show the real intention. What is the standard practice to identify if the dataset is Guassian or not?

FieldValue
text I'm following some tutorials on doing some linear regression and as I was building my notebook, I'm working on outlier detection and amongst the techniques described for doing outlier detection, one of them involved calculating the Standard Deviation, but for this I need to know if my columns are of Guassian distribution. I'm aware that there are different techniques like: * Histograms * KDE Plot * Q-Q Plot * Kolomogorov-Smirnov Test * Shapiro-Wilk Test * D'Agostino and Pearson's Test And I be…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-19
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url_encoded Z0FBQUFBQm5Lak9ITnpMQUxDWEljMVg5azZhQWxhUnRpeVBPMHluLWc0Tkk0VmxrWnZiVlNaT1BXUWM4UWx6eFdIdWkzTUc5UUxmbW9jWXZqQ1R6S0xWcGpQeDh6THBQcXhYZ0U1MVY0VThOSjRlS0hmaURjOG5lYk1EaVI2cVdsWXhwMmRtTFFDM3IySnlEeE0yNzNtMks0TUQwZS1iT3hidlBOTnMxMjFKUzZKWXJjVlpjZkVQMUZMSHhjdHhra1ZFdG5tamlCX3JjdDQwbFZzdVcwMnVMYTB1bFhNakZhUT09

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{
  "text": "I'm following some tutorials on doing some linear regression and as I was building my notebook, I'm working on outlier detection and amongst the techniques described for doing outlier detection, one of them involved calculating the Standard Deviation, but for this I need to know if my columns are of Guassian distribution. I'm aware that there are different techniques like:\n\n* Histograms\n* KDE Plot\n* Q-Q Plot\n* Kolomogorov-Smirnov Test\n* Shapiro-Wilk Test\n* D'Agostino and Pearson's Test\n\nAnd I bet there are a few more as well. So what is the best one to use? I guess Histograms just give a clue but do not show the real intention. What is the standard practice to identify if the dataset is Guassian or not?",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-19",
  "username_encoded": "Z0FBQUFBQm5Lakw0VHUwNWNaZnd3aEREMTdKdjUzQ01TaVYxQ2YwWXZ1UV85X3hlb0pSdlpQay1xQm44ZmpfaS1WR3VmTk5uRHJDVE1UTXFlV2E4WDFzYXU2XzNBblc0X3ZNSVJZVXBkQ1JjOTJERUhSaktZYnM9",
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}
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