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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><img src="../.gitbook/assets/image (267).png" alt="" width="477"><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.gitbook/assets/image (272).png" alt=""><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.gitbook/assets/image (277).png" alt=""><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.gitbook/assets/image (278).png" alt=""><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.gitbook/assets/image (280).png" alt=""><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.gitbook/assets/image (281).png" alt=""><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.gitbook/assets/image (312).png" alt="" width="471"><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.gitbook/assets/image (314).png" alt="" width="455"><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.gitbook/assets/image (316).png" alt="" width="464"><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.gitbook/assets/image (285).png" alt="" width="452"><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.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><img src="../.gitbook/assets/image (290).png" alt="" width="457"><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.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><img src="../.gitbook/assets/image (292).png" alt="" width="462"><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.gitbook/assets/image (294).png" alt=""><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.gitbook/assets/image (295).png" alt=""><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.gitbook/assets/image (296).png" alt="" width="473"><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.gitbook/assets/image (298).png" alt="" width="455"><figcaption></figcaption></figure>
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<figure><img src="../.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><img src="../.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><img src="../.gitbook/assets/image (301).png" alt="" width="463"><figcaption></figcaption></figure>
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<figure><img src="../.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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‎docs/statistics/8.-students-t-test.md

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# 8. Student's T-test
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<figure><img src="../.gitbook/assets/image (303).png" alt="" width="479"><figcaption></figcaption></figure>
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<figure><img src="../.gitbook/assets/image (303).png" alt="" width="319"><figcaption></figcaption></figure>
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1. Click on the _**Student's t-test**_ in the _**Statistics**_ category.
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<figure><img src="../.gitbook/assets/image (304).png" alt="" width="563"><figcaption></figcaption></figure>
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2. Choose the type of t-test you want to perform:
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‎docs/statistics/9.-anova.md

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# 9. ANOVA
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<figure><img src="../.gitbook/assets/image (308).png" alt="" width="479"><figcaption></figcaption></figure>
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<figure><img src="../.gitbook/assets/image (308).png" alt="" width="320"><figcaption></figcaption></figure>
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1. Click on _**ANOVA**_ in the _**Statistics**_ category.
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