The last assumption of the linear regression analysis ishomoscedasticity. The scatter plot is good way to check whether the data are homoscedastic (meaning the residuals are equal across the regression line). Th
Most of regression analysis is based on least-squares estimates of the parameters of the linear regression equation. Although we have discussed some of the properties of the least-squares regression coefficient already, we have not presented any equations for computing this coefficient. It turns out...
Regression on component scoresCanonical correlation has been little used and little understood, even by otherwise sophisticated analysts. An alternative approach to canonical correlation, based on a general linear multivariate model, is presented. Properties of principal component analysis are used to help...
Linear regression analysis made it possible to find out which independent variables best explain the positive emotions dependent variable: the sentiment expressed in the text, number of hashtags included in the text, amount of written text, narcissism as the main reason for the photo, number of wo...
1. Some Regression Notions Let’s start by understanding the basics of regression. As Wikipedia says: regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the ‘outcome’ or ‘response’ variable) and one or more independen...
At each step, important aspects of application are detailed, such as how to choose a prior for logistic regression model, the Poisson regression model, and the proportional hazards model. A related Web site houses R functions and Minitab macros for Bayesian analysis and Monte Carlo simulations, ...
regression analysis of the agents’ locations from neural activations (Fig.4d). In the early stages of learning, the agents’ locations could not be accurately predicted from the neural activities of either process-1 or process-2, but as learning progressed, the locations of agent-1 and agent...
Sanchez, J.D. (2007). Analyzing Undergraduate Admissions Criteria (the SATs) and Understanding Students' Academic Growth Using Hierarchical Linear Models, Item Response Theory and Differential Item Functioning Analysis, Diss. University of California, Berkeley....
2.3. Statistical analysis This analysis used multinomial logistic regression, which simultaneously compares between exposure groups the odds of multiple, mutually-exclusive outcomes using a single chosen reference group. As per convention, we used BMI 18.5–24.99 kg/m2 as the reference group. All anal...
Our regression analysis establishes an accurate model to predict application contentiousness. The analysis also demonstrates that performance counters alone may not be sufficient to accurately predict application sensitivity to contention. In this chapter, we also present an evaluation using SPEC CPU2006 ...