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ap_statistics [2024/04/09 20:28] mrdoughap_statistics [2024/05/07 00:59] (current) mrdough
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-====== 6062b249804f2baef22989a2_SS-AP-Statistics ====== +=======AP Statistics Study Guide======== 
- +Google doc Cheat sheet with stuff not on Formula Sheet [[https://www.wlwv.k12.or.us/cms/lib8/OR01001812/Centricity/Domain/2064/Review%20AP%20Stats.pdf |here]] 
- +----
-**AP Statistics Study Guide** +
 **From Simple Studies,** [[https://simplestudies.edublogs.org/|**https:%%//%%simplestudies.edublogs.org**]] **& @simplestudies4 on** Instagram **From Simple Studies,** [[https://simplestudies.edublogs.org/|**https:%%//%%simplestudies.edublogs.org**]] **& @simplestudies4 on** Instagram
  
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   * Individual observations must be independent   * Individual observations must be independent
-    * This allows us to calculate the standard deviation $\sqrt{\frac{p_o(1-p_0)}{n}}$+    * This allows us to calculate the standard deviation {{:pasted:20240409-223044.png}}
     * When sampling without replacement, the 10% condition must be met (n < 0.10N)     * When sampling without replacement, the 10% condition must be met (n < 0.10N)
  
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 When the standard deviation of a statistic is estimated from data, the result is called the **standard error** of the statistic When the standard deviation of a statistic is estimated from data, the result is called the **standard error** of the statistic
  
-  * $\sqrt{\frac{\hat{p}(1-\hat{p})}{n}}$+  * {{:pasted:20240409-223058.png}}
  
 These are the four-steps you MUST take when constructing a confidence interval: These are the four-steps you MUST take when constructing a confidence interval:
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   * As the degrees of freedom increase, the density curve approaches the standard normal distribution more closely   * As the degrees of freedom increase, the density curve approaches the standard normal distribution more closely
  
-When the conditions are met, a C% confidence interval for the unknown mean is $x^-\pm t*(\frac{s_x}{\sqrt{n}})$+When the conditions are met, a C% confidence interval for the unknown mean is {{:pasted:20240409-223118.png}}
  
   * t* is the critical value for the t distribution with n - 1 degrees of freedom and C% of its area between -t* and t*   * t* is the critical value for the t distribution with n - 1 degrees of freedom and C% of its area between -t* and t*
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   * Individual observations must be independent   * Individual observations must be independent
-    * This allows us to calculate the standard deviation using the formula $(\frac{s_x}{\sqrt{n}})$+    * This allows us to calculate the standard deviation using the formula {{:pasted:20240409-223440.png}}
     * When sampling without replacement, the 10% condition must be met     * When sampling without replacement, the 10% condition must be met
  
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   * Individual observations must be independent   * Individual observations must be independent
-    * This allows us to calculate the standard deviation $\sqrt{ rac{p_o(1-p_0)}{n}}$+    * This allows us to calculate the standard deviation {{:pasted:20240409-223502.png}}
     * When sampling without replacement, the 10% condition must be met     * When sampling without replacement, the 10% condition must be met
  
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   * Individual observations must be independent   * Individual observations must be independent
  
-  * This allows us to calculate the standard deviation $ rac{s_x}{\sqrt{n}}$+  * This allows us to calculate the standard deviation {{:pasted:20240409-223604.png}}
  
   * When sampling without replacement, the 10% condition must be met   * When sampling without replacement, the 10% condition must be met
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   * The mean of the sampling distribution of p̂1 - p̂2 = p1 - p2   * The mean of the sampling distribution of p̂1 - p̂2 = p1 - p2
  
-  * The standard deviation of the sampling distribution of p̂1 - p̂2 = $\sqrt{ rac{p_1(1-p_1)}{n_1}+ rac{p_2(1+p_2)}{n_2}}$ +  * The standard deviation of the sampling distribution of p̂1 - p̂2 = {{:pasted:20240409-223727.png}} 
-    * The confidence interval is therefore $(\hat{p}_1-\hat{p}_2\pm z*)$ $\sqrt{ rac{\hat{p}_1(1-\hat{p}_1)}{n_1}}+ rac{\hat(p)_2(1-\hat{p}_2)}{n_2}$+    * The confidence interval is therefore {{:pasted:20240409-223915.png}}
       * We can do this on our calculator through Stat > Tests > 2-PropZInt       * We can do this on our calculator through Stat > Tests > 2-PropZInt
     * The 10% condition must be met for both samples     * The 10% condition must be met for both samples
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   * The **mean** of the sampling distribution of x̅1 - x̅2 = μ1 - μ2   * The **mean** of the sampling distribution of x̅1 - x̅2 = μ1 - μ2
  
-  * The **standard deviation** of the sampling distribution of x̅1 - x̅2 = $\sqrt{ rac{\sigma^2_1}{n_1}+ rac{o^2_2}{m^2}}$ +  * The **standard deviation** of the sampling distribution of x̅1 - x̅2 = {{:pasted:20240409-223929.png}} 
-    * The confidence interval is therefore (𝑥̅1 − 𝑥̅2) ± $z*\sqrt{ rac{\sigma^2_1}{n_1}+ rac{o^2_2}{m^2}}$+    * The confidence interval is therefore (𝑥̅1 − 𝑥̅2) ± {{:pasted:20240409-223944.png}}
       * We can use this through Stat > Tests > 2-SampTInt on the calculator       * We can use this through Stat > Tests > 2-SampTInt on the calculator
  
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