Mobility transitionMobility transition is a set of social, technological and political processes of converting traffic (including freight transport) and mobility to sustainable transport with renewable energy resources, and an integration of several different modes of private transport and local public transport. It also includes social change, a redistribution of public spaces, and different ways of financing and spending money in urban planning.
Linear regressionIn statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.
Regression analysisIn statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features'). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion.
Interpersonal relationshipIn social psychology, an interpersonal relation (or interpersonal relationship) describes a social association, connection, or affiliation between two or more persons. It overlaps significantly with the concept of social relations, which are the fundamental unit of analysis within the social sciences. Relations vary in degrees of intimacy, self-disclosure, duration, reciprocity, and power distribution. The main themes or trends of the interpersonal relations are: family, kinship, friendship, love, marriage, business, employment, clubs, neighborhoods, ethical values, support and solidarity.
Logistic regressionIn statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients in the linear combination).
Intimate relationshipAn intimate relationship is an interpersonal relationship that involves physical or emotional intimacy. Although an intimate relationship is commonly a sexual relationship, it may also be a non-sexual relationship involving family or friends. Emotional intimacy is an essential aspect of a healthy . Feelings of liking or loving may prompt physical intimacy. However, emotional intimacy may or may not be present alongside physical intimacy depending on the relationship.
FamilyFamily (from familia) is a group of people related either by consanguinity (by recognized birth) or affinity (by marriage or other relationship). It forms the basis for social order. The purpose of the family is to maintain the well-being of its members and of society. Ideally, families offer predictability, structure, and safety as members mature and learn to participate in the community. Historically, most human societies use family as the primary locus of attachment, nurturance, and socialization.
Robust regressionIn robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship between one or more independent variables and a dependent variable. Standard types of regression, such as ordinary least squares, have favourable properties if their underlying assumptions are true, but can give misleading results otherwise (i.e. are not robust to assumption violations).
Multinomial logistic regressionIn statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes. That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc.).
Errors-in-variables modelsIn statistics, errors-in-variables models or measurement error models are regression models that account for measurement errors in the independent variables. In contrast, standard regression models assume that those regressors have been measured exactly, or observed without error; as such, those models account only for errors in the dependent variables, or responses. In the case when some regressors have been measured with errors, estimation based on the standard assumption leads to inconsistent estimates, meaning that the parameter estimates do not tend to the true values even in very large samples.
