This article explains the different correlation and regression analysis values that are generated after conducting the tests. Their meaning, importance and how to interpret them are explained here.
correlation, regression analysis, spss intermediate
This article explores the concept of multivariate regression analysis along with discussing its assumptions and relevance.
correlation, data analysis and conclusion, empirical analysis with econometrics, regression analysis, regression with stata
A multicollinearity test helps to diagnose the presence of multicollinearity in a model. Multicollinearity refers to a state wherein there exists inter-association or inter-relation between two or more independent variables.
correlation, research methodology
When the linkage between 2 variables exists through a middle variable. This middle variable is referred to as a mediating variable.
correlation, empirical analysis with econometrics, regression analysis, regression with SPSS
Correlation refers to the extent of a relationship between the variables. In order to determine this relationship, it is essential to establish a correlation between the variables.
correlation, data testing with spss, empirical analysis with econometrics, research methodology, spss intermediate
Correlation is a statistical measure that helps in determining the extent of the relationship between two or more variables or factors.
correlation, data analysis and conclusion, data testing with spss, empirical analysis with econometrics, result interpretation, spss intermediate, spss procedure
This article shows a testing serial correlation of errors or time series autocorrelation in STATA. Autocorrelation problem arises when error terms in a regression model correlate over time or are dependent on each other.
assumption tests in STATA, correlation, empirical analysis with econometrics, STATA for data analysis, time series analysis, trend analysis
Until recently, Karl Pearson Correlation analysis was one of the most popular methods to measure linear association between two or more than two variables in a data set. For example, establishing the Karl Pearson Correlation between X variable and Y variable, where both variables belong to a single data set. Canonical Correlation Analysis (CCA), on the other hand, helps measure the correlation among variables which are in different datasets.
correlation, correlation in supervised learning, Supervised learning