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vault backup: 2024-01-03 14:37:12

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zleyyij 2024-01-03 14:37:12 -07:00
parent 8729e3289e
commit d85c2073fd
2 changed files with 2 additions and 2 deletions
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2
.obsidian/plugins/obsidian-git/data.json vendored
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@ -2,7 +2,7 @@
"commitMessage": "vault backup: {{date}}", "commitMessage": "vault backup: {{date}}",
"autoCommitMessage": "vault backup: {{date}}", "autoCommitMessage": "vault backup: {{date}}",
"commitDateFormat": "YYYY-MM-DD HH:mm:ss", "commitDateFormat": "YYYY-MM-DD HH:mm:ss",
"autoSaveInterval": 1, "autoSaveInterval": 5,
"autoPushInterval": 0, "autoPushInterval": 0,
"autoPullInterval": 5, "autoPullInterval": 5,
"autoPullOnBoot": false, "autoPullOnBoot": false,

2
education/statistics/Correlation and Regression.md
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@ -83,7 +83,7 @@ Given a scatter diagram where the average of each set lies on the point $(75, 70
### The Regression Line/Least Squared Regression Line (LSRL) ### The Regression Line/Least Squared Regression Line (LSRL)
- This line has a more moderate slope than the SD line. it does not go through the peaks of the "football" - This line has a more moderate slope than the SD line. it does not go through the peaks of the "football"
- Predictions can only be made if the data displays a linear association (is a football shape). - Predictions can only be made if the data displays a linear association (is a football shape).
- The regression line is *used to predict* the y variable when the x variable is given - The regression line is *used to predict* the y variable when the x variable is given. It should only be relied on if it is a controlled experiment, observational studies have too many confounding factors.
- In regression, the $x$ variable is the known variable, and $y$ is the value being solved for. - In regression, the $x$ variable is the known variable, and $y$ is the value being solved for.
- The regression line goes through the point of averages, and can be positive or negative - The regression line goes through the point of averages, and can be positive or negative
$$ slope = r(\frac{\sigma_y}{\sigma_x}) $$ $$ slope = r(\frac{\sigma_y}{\sigma_x}) $$