Ward Clinker asked, updated on December 13th, 2021; Topic:
correlation

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A **positive correlation** exists when two variables move in the same direction as one another. A basic **example** of **positive correlation** is height and weight—taller people tend to be heavier, and vice versa. In some cases, **positive correlation** exists because one variable influences the other.

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That said, what is a strong correlation?

The relationship between two variables is generally considered **strong** when their r value is larger than 0.7. The **correlation** r measures the strength of the linear relationship between two quantitative variables. Pearson r: r is always a number between -1 and 1.

Even so, what is a weak positive correlation? A **weak positive correlation** would indicate that while both variables tend to go up in response to one another, the relationship is not very strong. A strong negative **correlation**, on the other hand, would indicate a strong connection between the two variables, but that one goes up whenever the other one goes down.

Anyhow, is 0.4 A strong correlation?

Generally, a value of r greater than 0.7 is considered a **strong correlation**. Anything between 0.5 and 0.7 is a **moderate correlation**, and anything less than **0.4** is considered a weak or no **correlation**.

What are the 5 types of correlation?

**Correlation**

- Pearson
**Correlation**Coefficient. - Linear
**Correlation**Coefficient. - Sample
**Correlation**Coefficient. - Population
**Correlation**Coefficient.

A zero correlation exists when there is no relationship between two **variables**. For example there is no relationship between the amount of tea drunk and level of intelligence.

How close is close enough to –1 or +1 to indicate a **strong** enough linear relationship? Most statisticians like to see **correlations** beyond at least +0.5 or –0.5 before getting too excited about them. Don't expect a **correlation** to always be 0.99 however; remember, these are real data, and real data aren't perfect.

The **correlation** coefficient is a statistical measure of the strength of the relationship between the relative movements of two variables. The values range between -1.0 and 1.0. A calculated number **greater than** 1.0 or less **than** -1.0 means that there was an error in the **correlation** measurement.

There is no rule for determining what size of **correlation** is considered **strong**, **moderate** or weak. ... For this kind of data, we generally consider **correlations** above 0.4 to be relatively **strong**; **correlations** between **0.2** and 0.4 are **moderate**, and those below **0.2** are considered weak.

To **determine whether** the **correlation** between variables is **significant**, compare the p-value to your **significance** level. Usually, a **significance** level (denoted as α or alpha) of 0.05 works well. ... **If** the p-value is less than or equal to the **significance** level, then you can conclude that the **correlation** is different from 0.

The strongest linear relationship is indicated by a **correlation coefficient** of -1 or 1. The weakest linear relationship is indicated by a **correlation coefficient** equal to 0. A positive correlation means that if one variable gets bigger, the other variable tends to get bigger.

When interpreting the value of the corrrelation coefficient, the same rules are valid for both Pearson's and Spearman's coefficient, and r values from 0 to **0.25** or from 0 to -**0.25** are commonly regarded to indicate the absence of **correlation**, whereas r values from **0.25** to 0.50 or from -**0.25** to -0.50 point to poor ...

A **weak correlation** means that as one variable increases or decreases, there is a lower likelihood of there being a relationship with the second variable. In a visualization with a **weak correlation**, the angle of the plotted point cloud is flatter. If the cloud is very flat or vertical, there is a **weak correlation**.

Usually, in statistics, we measure four types of correlations: Pearson correlation, **Kendall rank correlation**, Spearman correlation, and the **Point**-Biserial correlation.

The Pearson **correlation coefficient** is the most widely **used**. It measures the strength of the linear relationship between normally distributed variables.

The fundamental **difference between** the two **correlation** coefficients is that the **Pearson** coefficient works **with a** linear relationship **between** the two variables whereas the **Spearman** Coefficient works with monotonic relationships as well.

A value of zero indicates that there is no relationship between the two variables. ... **If the correlation** coefficient of two variables is zero, it signifies that there is no linear relationship between the variables.

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