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## What is the difference between observed value and predicted value?

In statistics, the **actual value** is the value that is obtained by observation or by measuring the available data. It is also called the observed value. The predicted value is the value of the variable predicted based on the regression analysis.

## What is the difference between the observed value of the dependent variable and the value predicted?

7. **The residual** is the difference between the actual value of a dependent variable and the value predicted by the estimated regression line.

## What is the mathematical difference between the estimated value of y and the observed value of y?

The difference between the observed value of y and the predicted value of y is **the error, or residual**. The observed drilling time is 6.93 seconds.

## Is a measure of the differences between the observed sample y values and the predicted values that are obtained using the regression equation?

The difference between the observed data value and the predicted value (the value on the straight line) is **the error or residual**. The criterion to determine the line that best describes the relation between two variables is based on the residuals.

## What is predicted value?

Predicted Values.

The value the **model predicts for the dependent variable**. Standardized . A transformation of each predicted value into its standardized form. That is, the mean predicted value is subtracted from the predicted value, and the difference is divided by the standard deviation of the predicted values.

## What is the predictor variable?

Predictor variable is the **name given to an independent variable used in regression analyses**. The predictor variable provides information on an associated dependent variable regarding a particular outcome. … At the most fundamental level, predictor variables are variables that are linked with particular outcomes.

## Is the value of any regression coefficient is zero then two variables are?

0008; this means that there is no correlation, or relationship, between the two variables. ∴ We can say that, if the value of any regression coefficient is zero, then two variables **are Independent**.

## Is there is a very strong correlation between two variables then the correlation coefficient must be?

If the value of the **correlation coefficient is closer to 1**, it indicates a very strong positive correlation between two variables and if the value of the correlation coefficient is closer to -1, it indicates a very strong negative correlation.

## How do you tell if there is a linear relationship between two variables?

The linear relationship between two variables is **positive when both increase together**; in other words, as values of get larger values of get larger. This is also known as a direct relationship. The linear relationship between two variables is negative when one increases as the other decreases.

## What is a correlation between two variables?

Correlation is a statistical term describing the **degree to which two variables move in coordination with one another**. If the two variables move in the same direction, then those variables are said to have a positive correlation. If they move in opposite directions, then they have a negative correlation.

## How is correlation and regression used in real life?

For example, in patients attending an accident and emergency unit (A&E), we could use correlation and regression to determine **whether there is a relationship between age and urea level**, and whether the level of urea can be predicted for a given age.

## What four assumptions must we meet for a linear regression model to be appropriate?

**There are four assumptions associated with a linear regression model:**

- Linearity: The relationship between X and the mean of Y is linear.
- Homoscedasticity: The variance of residual is the same for any value of X.
- Independence: Observations are independent of each other.

## When the values of two variables move in the same direction correlation is said to be?

When two related variables move in the same direction, their relationship is positive. This correlation is measured by the coefficient of correlation (r). When r is greater than 0, it is positive.