- What does it mean when variables are independent?
- How do you determine if two variables are independent?
- When two variables are independent then the relationship is called as?
- What is an example of zero correlation?
- Can you have a correlation greater than 1?
- Is 0.4 A strong correlation?
- Is 0.6 A strong correlation?
- What does a correlation of 0.5 mean?
- Is .25 a weak correlation?
- What does an R2 value of 0.9 mean?
- What does an R2 value of 0.01 mean?
- What does an R2 value of 0.3 mean?

## What does it mean when variables are independent?

Answer: An independent variable is exactly what it sounds like. It is a variable that stands alone and isn’t changed by the other variables you are trying to measure. For example, someone’s age might be an independent variable.

## How do you determine if two variables are independent?

Independence two jointly continuous random variables X and Y are said to be independent if fX,Y (x,y) = fX(x)fY (y) for all x,y. It is easy to show that X and Y are independent iff any event for X and any event for Y are independent, i.e. for any measurable sets A and B P( X ∈ A ∩ Y ∈ B ) = P(X ∈ A)P(Y ∈ B).

## When two variables are independent then the relationship is called as?

Multiple correlation refers to the strength of the association between the independent variables and one dependent variable, i.e. relationship between more than two variables.

## What is an example of zero correlation?

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.

## Can you have a correlation greater than 1?

The possible range of values for the correlation coefficient is -1.0 to 1.0. In other words, the values cannot exceed 1.0 or be less than -1.0. A correlation of -1.0 indicates a perfect negative correlation, and a correlation of 1.0 indicates a perfect positive correlation.

## Is 0.4 A strong correlation?

The sign of the correlation coefficient indicates the direction of the relationship. 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.

## Is 0.6 A strong correlation?

Correlation Coefficient = +1: A perfect positive relationship. Correlation Coefficient = 0.8: A fairly strong positive relationship. Correlation Coefficient = 0.6: A moderate positive relationship.

## What does a correlation of 0.5 mean?

Correlation coefficients whose magnitude are between 0.5 and 0.7 indicate variables which can be considered moderately correlated. Correlation coefficients whose magnitude are between 0.3 and 0.5 indicate variables which have a low correlation.

## Is .25 a weak correlation?

25 or . 3 (weak correlations).

## What does an R2 value of 0.9 mean?

Some statisticians prefer to work with the value of R2, which is simply the correlation coefficient squared, or multiplied by itself, and is known as the coefficient of determination. An R2 value of 0.9, for example, means that 90 percent of the variation in the y data is due to variation in the x data.

## What does an R2 value of 0.01 mean?

R-square value tells you how much variation is explained by your model. So 0.1 R-square means that your model explains 10% of variation within the data. So if the p-value is less than the significance level (usually 0.05) then your model fits the data well.

## What does an R2 value of 0.3 mean?

– if R-squared value < 0.3 this value is generally considered a None or Very weak effect size, – if R-squared value 0.3 < r < 0.5 this value is generally considered a weak or low effect size, – if R-squared value r > 0.7 this value is generally considered strong effect size, Ref: Source: Moore, D. S., Notz, W.