## What happens if ANOVA assumptions are violated?

If the assumption of normality is violated, or outliers are present, then the one-way ANOVA may not be the most powerful test available, and this could mean the difference between detecting a true difference among the population means or not.

## What if linearity is violated?

Violations of linearity or additivity are extremely serious: if you fit a linear model to data which are nonlinearly or nonadditively related, your predictions are likely to be seriously in error, especially when you extrapolate beyond the range of the sample data.

**What happens if linear regression assumptions are not met?**

Regression requires its dependent variable to be at least least interval or ratio data. If the dependent is not this level of data, the researcher violating this assumption would have spurious results.

**What is assumption violation?**

a situation in which the theoretical assumptions associated with a particular statistical or experimental procedure are not fulfilled.

### Does GLM assume constant variance?

In Generalized Linear Models, one expresses the variance in the data as a suitable function of the mean value. In the Linear regression model, we assume V(µ) = some constant, i.e. variance is constant.

### What is violation assumption?

**How could you check if the assumption of linearity is met?**

The linearity assumption can best be tested with scatter plots, the following two examples depict two cases, where no and little linearity is present. Secondly, the linear regression analysis requires all variables to be multivariate normal. This assumption can best be checked with a histogram or a Q-Q-Plot.

**What happens if the assumption of a normal distribution is not met?**

The hypothesis test would not be significant We need to be cautious in making inferences about the population from the statistical result It would be mathematically impossible to calculate Z statistic The effect size would be very small.

## What could cause model assumptions to be violated?

Potential assumption violations include: Implicit independent variables: X variables missing from the model. Lack of independence in Y: lack of independence in the Y variable. Outliers: apparent nonnormality by a few data points.

## What to do if the assumption of normality is violated?

Data transformation: A common issue that researchers face is a violation of the assumption of normality. Numerous statistics texts recommend data transformations, such as natural log or square root transformations, to address this violation (see Rummel, 1988).

**Is ANOVA a GLM?**

GLM generalizes the linear model used in ANOVA by allowing any other type of distribution of the residuals (and optimizes the likelihood function, which only allows a t-test based on an estimated error of the coefficients). So an anova is an Glm, but a Glm is not only anovas.

**What is the general linear model GLM Why does it matter?**

The general linear model and the generalized linear model (GLM) are two commonly used families of statistical methods to relate some number of continuous and/or categorical predictors to a single outcome variable.

### What is a violation of the independence assumption?

One of the assumptions of most tests is that the observations are independent of each other. This assumption is violated when the value of one observation tends to be too similar to the values of other observations.

### What are the assumptions for an Anova test?

There are three primary assumptions in ANOVA: The responses for each factor level have a normal population distribution. These distributions have the same variance. The data are independent.

**What is linearity assumption?**

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 a violation of the normality assumption?**

If the population from which data to be analyzed by a normality test were sampled violates one or more of the normality test assumptions, the results of the analysis may be incorrect or misleading.

## What are the assumptions of regression and ANOVA?

They state the assumptions in terms specific to that analysis, not the more general forms. For example, the assumptions of ANOVA are the same as those for regression, although they’re often written in a more specific form.

## How robust is a one-way ANOVA against the normality assumption?

This suggests that the samples do not come a normal distribution. In general, a one-way ANOVA is considered to be fairly robust against violations of the normality assumption as long as the sample sizes are sufficiently large.

**What are the explicit assumptions of regression model?**

The Explicit Assumptions. These assumptions are explicitly stated by the model: The residuals are independent. The residuals are normally distributed. The residuals have a mean of 0 at all values of X.

**How many assumptions are there in a model?**

There are four assumptions that are explicitly stated along with the model, and some authors stop there. 2. Some authors are writing for introductory classes, and rightfully so, don’t want to confuse students with too many abstract, and sometimes untestable, assumptions.

https://www.youtube.com/watch?v=BLhdkQCBia0