Bivariate statistical measures

Mai multe ca aceasta

THE PROJECT APPROACH

după Javier Espinosa Gallardo

Computational Journalism

după Lluis Codina

Experiment vs Non-Experimental Methods

după Elizabeth Roraback

Umna mapa - Engleski jezik

după Petra Stručić

Bivariate statistical measures

multiple recreation

Recreation can be linear and curvilinear

regression coefficient

positive Negative Null

he egrection coefficient can be: the number of units in which the variable y is modified by the effect of the independent change x or vice versa in a unit of measurement.

regression analysis

statistical technique to derive an equation that relates a criterion variable to one or more predictor variables, when a predictor variable is used the regression analysis is multiple

linear regression coefficient

quantity that results from a multiple analysis that indicates the average change in a criterion variable per unit change in a predictive variable in equal circumstances in all as a criction variable

Determination coefficient R2

The determination coefficient, also called R square, determines the degree of correlation between the variables, which reflects the goodness of a model fit for the variable.

Recrecion lineal

regression analysis model

Standardized: the slope &1 indicates the relationship between the two variables, its sign indicates the positive or negative relationship, the reason is that its numerical value depends on the units of measurement of the two variables, a change of units is one of them can produce a drastic change in the value of the slope

statistical: allows the incorporation of a Random Component in the relationship, consequently, the predictions obtained through statistical models will have an associated prediction error

Deterministic: deterministic: assumes that under ideal conditions the behavior of the dependent variable can be fully described by a mathematical function of the independent variables, that is, under ideal conditions the model allows the value of the dependent variable to be predicted without error.

Simple linear regression

Linear function that satisfies the properties active property that is a polynomial function whose representation is in the Cartesian plane is a straight line

It tries to explain the behavior of a variable called dependent or endogenous as a function of another variable called dependent or exogenous.

Correlation Types

There is no correlation Positive correlation high positive correlation

weak correlation

It will be weaker the farther apart the points are on the line.

strong correlation

It will be stronger the closer it is to the points on the line.

null correlation

Null correlation occurs when the two assets have no dependence or relationship with each other.

inverse correlation

It occurs when increasing one of the variables decreases the other. The line corresponding to the cloud of points of the distribution is a decreasing line.

direct correlation

It occurs when increasing one of the variables increases the other.

Linear correlation

Stable is the relationship or dependence that exists between two variables that intervene in a two-dimensional distribution, that is, it determines it and if so we will say that the variables are correlated or that there is a correlation between them.