## What are assumptions of regression?

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.

**What is additivity in statistics?**

-My definition of statistical interaction: “Statistical interaction means the effect of one independent variable(s) on the dependent variable depends on the value of another independent variable(s).” Conversely, “Additivity means that the effect of one independent variable(s) on the dependent variable does NOT depend …

**What is p value in regression?**

P-Value is a statistical test that determines the probability of extreme results of the statistical hypothesis test,taking the Null Hypothesis to be correct. It is mostly used as an alternative to rejection points that provides the smallest level of significance at which the Null-Hypothesis would be rejected.

### How does heteroskedasticity affect regression?

Heteroskedasticity refers to situations where the variance of the residuals is unequal over a range of measured values. When running a regression analysis, heteroskedasticity results in an unequal scatter of the residuals (also known as the error term).

**Which is the best practice to deal with heteroskedasticity?**

The solution. The two most common strategies for dealing with the possibility of heteroskedasticity is heteroskedasticity-consistent standard errors (or robust errors) developed by White and Weighted Least Squares.

**Why normality assumption is important in regression?**

Making this assumption enables us to derive the probability distribution of OLS estimators since any linear function of a normally distributed variable is itself normally distributed. Thus, OLS estimators are also normally distributed. It further allows us to use t and F tests for hypothesis testing.

## Why do we use normality assumption in regression?

The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results.

**What are the four assumptions of linear regression?**

However, before we conduct linear regression, we must first make sure that four assumptions are met: 1. Linear relationship: There exists a linear relationship between the independent variable, x, and the dependent variable, y. 2. Independence: The residuals are independent.

**What to do if the assumption is violated in a regression?**

If the assumption is violated, consider the following options: For positive correlation, consider adding lags to the dependent or the independent or both variables. For negative correlation, check to see if none of the variables is over-differenced. For seasonal correlation, consider adding a few seasonal variables to the model.

### Why is the Theis equation used for single well analysis?

Due to the nature of the assumptions in the model for drawdown using the Theis equation, it is most commonly used for single well analysis. Next:Hydraulic ConductivityUp:Radial Flow to Previous:Radial Flow to

**Why are three of the assumptions in my model not satisfied?**

Three of the assumptions are not satisfied. This is probably because we have only 50 data points in the data and having even 2 or 3 outliers can impact the quality of the model. So the immediate approach to address this is to remove those outliers and re-build the model.