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Commit85e9df0

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8orrin9gitbook-bot
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GITBOOK-82: Image Resizing - Statistics
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‎docs/statistics/1.-prob.-distribution.md‎

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#1. Prob. Distribution
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<figure><imgsrc="../.gitbook/assets/image (267).png"alt=""width="477"><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (267).png"alt=""width="318"><figcaption></figcaption></figure>
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1. Click the_**Prob. Distribution**_ in the_**Statistics**_ category.
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####Statistics to P-Value
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<figure><imgsrc="../.gitbook/assets/image (272).png"alt=""><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (272).png"alt=""width="563"><figcaption></figcaption></figure>
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1._**Statistics**_: You will get the probability of getting a value from this distribution that is greater than or equal to the absolute value entered here.
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2._**Alternative**_:_**Two-sided** for a_ two-tailed test,_**One-sided**_ for_a_ one-tailed test.
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###Normal
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<figure><imgsrc="../.gitbook/assets/image (277).png"alt=""><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (277).png"alt=""width="563"><figcaption></figcaption></figure>
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1._**Loc**_: Set the mean of the normal distribution.
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2._**Scale**_: Set the standard deviation of the normal distribution.
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3._**User option**_: You can add options other than those provided in Visual Python.
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###Beta
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<figure><imgsrc="../.gitbook/assets/image (278).png"alt=""><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (278).png"alt=""width="563"><figcaption></figcaption></figure>
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1._**A**_: Set the shape parameter**A** for the beta distribution.
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2._**B**_: Set the shape parameter**B** for the beta distribution.
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###Student's t and Chi2
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<figure><imgsrc="../.gitbook/assets/image (280).png"alt=""><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (280).png"alt=""width="563"><figcaption></figcaption></figure>
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1._**Df**_: Set the degrees of freedom for the t-distribution or chi-squared distribution.
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2._**User Option**_: You can add options other than those provided in Visual Python.
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###F
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<figure><imgsrc="../.gitbook/assets/image (281).png"alt=""><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (281).png"alt=""width="563"><figcaption></figcaption></figure>
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1._**Dfn**_: Set the numerator degrees of freedom.
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2._**Dfd**_: Set the denominator degrees of freedom.

‎docs/statistics/10.-factor-analysis.md‎

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#10. Factor Analysis
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<figure><imgsrc="../.gitbook/assets/image (312).png"alt=""width="471"><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (312).png"alt=""width="314"><figcaption></figcaption></figure>
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1. Click on_**Factor Analysis**_ in the_**Statistics**_ category
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‎docs/statistics/11.-regression.md‎

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#11. Regression
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<figure><imgsrc="../.gitbook/assets/image (314).png"alt=""width="455"><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (314).png"alt=""width="303"><figcaption></figcaption></figure>
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1. Click on_**Regression**_ in the_**Statistics**_ category.
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‎docs/statistics/12.-logistic-regression.md‎

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#12. Logistic Regression
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<figure><imgsrc="../.gitbook/assets/image (316).png"alt=""width="464"><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (316).png"alt=""width="310"><figcaption></figcaption></figure>
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1. Click on_**Logistic Regression**_ in the_**Statistics**_ category.
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‎docs/statistics/2.-descriptive-statistics.md‎

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#2. Descriptive Statistics
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<figure><imgsrc="../.gitbook/assets/image (285).png"alt=""width="452"><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (285).png"alt=""width="302"><figcaption></figcaption></figure>
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1. Select_**Descriptive Statistics**_ in the_**Statistics category**_.
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<figure><imgsrc="../.gitbook/assets/image (286).png"alt=""width="563"><figcaption></figcaption></figure>
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2._**Data**:_ Choose a DataFrame. You can apply conditions or slice through the_**Subset**_.
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2._**Data**:_ Choose a DataFrame. You can apply conditions or slice through the[_**Subset**_](../data-analysis/5.-subset.md).
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3._**Variable**_: Select the column from the chosen DataFrame to be designated as the_**Variable**_.
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4._**Code View**_: Preview the code that will be displayed.
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5._**Data View**_: Preview the displayed results.

‎docs/statistics/3.-normality-test.md‎

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#3. Normality Test
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<figure><imgsrc="../.gitbook/assets/image (290).png"alt=""width="457"><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (290).png"alt=""width="305"><figcaption></figcaption></figure>
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1. Choose the_**Normality test**_ in the_**Statistics**_ category.
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<figure><imgsrc="../.gitbook/assets/image (291).png"alt=""width="563"><figcaption></figcaption></figure>
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2. On the top tab, select the test method you want to apply.
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3._**Data**_: Select the data you want to test. You can use_Subset to_ select values that meet certain conditions. Next, select the columns you want to test from the selected values.
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3._**Data**_: Select the data you want to test. You can use[_**Subset**_](../data-analysis/5.-subset.md)_to_ select values that meet certain conditions. Next, select the columns you want to test from the selected values.
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4._**Alternative hypothesis (Kolmogorov-Smirnov test)**_: Choose whether to perform a two-tailed or one-tailed test.
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5._**Display**_: Choose a plot to output with your results.
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6._**Code View**_: Preview the code that will be output.

‎docs/statistics/4.-equal-var.-test.md‎

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#4. Equal Var. Test
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<figure><imgsrc="../.gitbook/assets/image (292).png"alt=""width="462"><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (292).png"alt=""width="308"><figcaption></figcaption></figure>
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1. Select the_**Equal Var. Test**_ in the_**Statistics**_ category.
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###Long Data
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<figure><imgsrc="../.gitbook/assets/image (294).png"alt=""><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (294).png"alt=""width="563"><figcaption></figcaption></figure>
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1._**Input Type**_: Select the format of the data to be tested.
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2._**Data**_: Choose the data to be tested. You can use[_**Subset**_](../data-analysis/5.-subset.md) to select only the data that satisfies specific conditions.
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###Wide Data
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<figure><imgsrc="../.gitbook/assets/image (295).png"alt=""><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (295).png"alt=""width="563"><figcaption></figcaption></figure>
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1._**Input Type**_: Select the format of the data to be tested.
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2._**Data**_: Choose the data to be tested. You can use[_**Subset**_](../data-analysis/5.-subset.md) to select only the data that satisfies specific conditions. Select the columns for the data chosen above to test for equal variance.

‎docs/statistics/5.-correlation-analysis.md‎

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#5. Correlation Analysis
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<figure><imgsrc="../.gitbook/assets/image (296).png"alt=""width="473"><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (296).png"alt=""width="316"><figcaption></figcaption></figure>
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1. Click on_**Correlation Analysis**_ in the_**Statistics**_ category.
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‎docs/statistics/6.-reliability-analysis.md‎

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#6. Reliability Analysis
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<figure><imgsrc="../.gitbook/assets/image (298).png"alt=""width="455"><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (298).png"alt=""width="303"><figcaption></figcaption></figure>
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1. Click on_**Reliability Analysis**_ in the_**Statistics**_ category.
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###Example Output
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<figure><imgsrc="../.gitbook/assets/image (300).png"alt=""><figcaption></figcaption></figure>
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*_**Cronbach Alpha**_ used in Reliability Analysis is a statistical method measuring_**Consistency**_, and this consistency is an indicator of Reliability.
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***The Cronbach Alpha value** is output between 0 and 1, where closer to 1 indicates higher consistency. The_**N**_ value represents the number of columns selected.

‎docs/statistics/7.-chi-square-test.md‎

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#7. Chi-square Test
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<figure><imgsrc="../.gitbook/assets/image (301).png"alt=""width="463"><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (301).png"alt=""width="309"><figcaption></figcaption></figure>
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1. Click on the_**Chi-square test**_ in the_**Statistics**_ category.
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