Table of Contents

## What are the usual assumptions for a factor model?

3 Assumptions of the common factor model Unique variances (disturbances) have a mean of zero: E(εi)=0. Latent factors have mean zero, E(ηi)=0. Latent factors have a variance of one, var(ηi)=1. (Standardized solution)

## What is a linear factor model?

A linear factor model relates the return on an asset (be it a stock, bond, mutual fund or something else) to the values of a limited number of factors, with the relationship described by a linear equation.

**What is the specific factor model?**

The specific factor (SF) model is designed to evaluate the real-world phenomenon that some factors of production are more mobile between industries than others. It does that by assuming that one factor (capital) cannot move between industries, while the other factor (labor) can freely move.

### What is the difference between the Ricardian model and specific factors model?

Unlike in the Ricardian model, labor is shared between the two industries. Thus, the specific factors model explains why a country produces a product and also imports it. For instance, the US produces but also imports oil from the Middle East. The exact output mix depends on the prices.

### Does factor analysis assume normality?

Normality assumption is necessary for some methods of factor extraction and for performin some statistical tests facultatively accompanying factor analysis. To your question: Yes, if factors are distributed normally and errors normally too, that will mean manifest variables are also normal.

**What are the assumptions of principal component analysis?**

The assumptions in PCA are: There must be linearity in the data set, i.e. the variables combine in a linear manner to form the dataset. The variables exhibit relationships among themselves.

## What is the difference between specific and non specific factors of production?

Nonspecific factor of production is a factor of production which has equal values in the production processes of more than one particular type of economic good or service and is thus capable of alternate uses, as opposed to specific factors of production.

## In what way does the specific factors model add to the conclusions of the Ricardian model?

In what way do the conclusions of the Ricardian and the specific-factors models differ? A. In the specific-factors model, all resources (labor, land, capital) are better off with free trade. In the Ricardian model, only labor is better off with free trade.

**What is the difference between specific and nonspecific factors of production?**

### What are the 3 assumptions of ANOVA?

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.

### Can you do factor analysis with non normal distribution?

For FA there is no requirement for normality, and you would lose some important information, not to mention the difficulty to explain and interpret your results on the transformed variables! You may run the FA as such and also do a cluster analysis to check the results.

**Is PCA a linear transformation?**

PCA is defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by some scalar projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on.

## What is the difference between PCA and factor analysis?

The mathematics of factor analysis and principal component analysis (PCA) are different. Factor analysis explicitly assumes the existence of latent factors underlying the observed data. PCA instead seeks to identify variables that are composites of the observed variables.

## What are non specific factors?

Non-specific factors refer to dimensions that are shared by most psychotherapies and include the therapeutic alliance, the therapist’s competence and adherence to the treatment protocols whereas specific factors refer to the specific techniques and interventions that characterize particular psychotherapies.

**What is specific and non specific factors of production?**

### Why do returns to specific factors change by more than returns to mobile factors when international trade changes relative prices?

The earnings of specific factors change the most from relative price changes due to international trade. This is because these factors (land and capital) cannot move between industries.

### What is specific factors and income distribution?

The specific factors model allows trade to affect income distribution. • Assumptions of the model: – Two goods, cloth and food. – Three factors of production: labor (L), capital (K) and land (T for terrain).

**Does ANOVA assume linearity?**

The fact that linearity is not included in the assumptions for ANOVA makes sense if we recall that in the regression example we used a quantitative predictor variable, and in Moriah’s example we use a categorical variable.

## What are the various assumptions for two ANOVA?

Assumptions of the Two-Way ANOVA The populations from which the samples are obtained must be normally distributed. Sampling is done correctly. Observations for within and between groups must be independent. The variances among populations must be equal (homoscedastic).

## Does data have to be normally distributed for factor analysis?

Yes your data should be normally distributed. In addition to that check crobach alpha and KMO to check the validity of data for factor analysis. After applying factor analysis, the communalities should be greater than 0.3.

**What are the assumptions of linear models?**

Chapter 7Assumptions of linear models 7.1Introduction Linear models are models. A model describes the relationship between two or more variables. A good model gives a valid summary of what the relationship between the variables looks like. Let’s look at a very simple example of two variables: height and weight.

### Is the specification of a model a set of assumptions?

These assumptions aren’t, but the specification of the model implies them. This is the way I’ve summarized them–they can be written with different terminology, of course. If there is an assumption you’ve heard not on this list, chances are it is a logical extension of one of these core assumptions.

### What are the assumptions of a 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. The residuals have constant variance.

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

1. 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.