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vault backup: 2024-02-07 20:04:09

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zleyyij 2024-02-07 20:04:10 -07:00
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commit 8d78c55573
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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": 5, "autoSaveInterval": 1,
"autoPushInterval": 0, "autoPushInterval": 0,
"autoPullInterval": 5, "autoPullInterval": 5,
"autoPullOnBoot": false, "autoPullOnBoot": false,

2
education/statistics/Hypothesis Tests.md
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@ -11,7 +11,7 @@ If an observed value is too many SEs away from the expected value, it is hard to
- Null: *x* is *y*. This is often given in the problem - Null: *x* is *y*. This is often given in the problem
- Alternative: If you're being asked to determine if something has changed, you're determining whether or not *x* is equal to. If you're being asked to find the more than, or less than, it's a one sided test. - Alternative: If you're being asked to determine if something has changed, you're determining whether or not *x* is equal to. If you're being asked to find the more than, or less than, it's a one sided test.
2. Then find the SE. This is usually found with: $\frac{SD}{\sqrt{num_{draws}}}$. 2. Then find the SE. This is usually found with: $\frac{SD}{\sqrt{num_{draws}}}$.
3. The EV (Expected Value) is usually given as the population %. Then with the above info, you can find the $z$ score with the formula $z = \frac{observed_\% - expected_\%}{SE_\%}$. 3. The EV (Expected Value) is usually given as the population %. Then with the above info, you can find the $z$ score with the formula $z = \frac{expected_\% - observed_\%}{SE_\%}$.
4. You can use this $z$ score combined with something like $normalcdf$ to find the amount that is outside of the expected range. If that total amount is less than 5%, than the null hypothesis should be rejected. If that total amount is more than 5%, the difference is too small, and it should not be rejected. 4. You can use this $z$ score combined with something like $normalcdf$ to find the amount that is outside of the expected range. If that total amount is less than 5%, than the null hypothesis should be rejected. If that total amount is more than 5%, the difference is too small, and it should not be rejected.
Then you can provide a conclusion based off of either the null hypothesis, or the alternative hypothesis. Then you can provide a conclusion based off of either the null hypothesis, or the alternative hypothesis.