If there is too much variance in the error term, the model isn't well-defined. This is known as heteroskedasticity. Too much variance indicates that there are other factors influencing the dependent variable. These factors need to be considered through further investigation or modeling. Related Te...
It is important to identify the presence of autocorrelation in the data and to appropriately account for it in your modeling. The test for autocorrelation can be done via the request of Durbin-Watson statistics from SAS regression procedures (both PROC REG and PROC AUTOREG), but the ...
Explorations in Spatial Demography The concept of spatial autocorrelation and ways to specify correctly multiple regression models in the presence of spatial autocorrelation are made more concrete through an illustration of spatial modeling of county-level growth in the U.S... PR Voss,KJC White,RB ...
After performing data cleaning and exploratory study, the data becomes ready for modeling. Statistics and artificial intelligence provide algorithms for model creation. Model creation is where machine learning comes into the picture. Machine learning is a branch of artificial intelligence that is utilis...
Modeling quarterly tourist flows to Australia using cointegration analysis. Tour. Econ. 1996, 2, 203–222. [Google Scholar] [CrossRef] Salman, A.K.; Shukurb, G.; von Bergmann-Winberg, M.-L. Comparison of Econometric Modelling of Demand for Domestic and International Tourism: Swedish Data. ...
Modeling linear regression in Excel is easier with the Data Analysis ToolPak. Regression output can be interpreted for both the size and strength of a correlation among one or more variables on the dependent variable. Linear Regression in Finance ...
We will first discuss some basic modeling choices that researchers often make—some trivial, some important. This section will focus primarily on regression-based estimation. While not necessary, it is nonetheless quite common for authors to transform the running variable \(X\) by recentering at \...
By analyzing the sedimentary response to different climatic variations, we argue that there is no direct indication of a major base-level drop in the Drava Basin, which was much larger than the seismic resolution, i.e. 40–50 m. Modeling also infers that autoretreat and autocyclic ...
(2003). Modeling and interpreting interactive hypotheses in regression analysis: A brief refresher and some practical advice. Unpublished, University of Michigan.Kam, Cindy, and Robert Franzese. 2003. "Modeling and Interpreting Interactive Hypotheses in Regression Analysis: A Brief Refresher and Some ...
Quantile information is important in time series applications. Quantile regression not only provides a method of estimating the conditional quantiles (thus the conditional distribution) of conventional time series models but also substantially expands the modeling options for time series analysis by allowing...