Regression is an statistical technique that helps in exploring the relationship between a dependent variable (Y) and one or more independent or predictor variables (Xs). It can also be used to predict a continuous outcome variable based on one or more predictor variables. Essentially, regression analysis aims to identify the functional relationship between two or more variables in a dataset. There are different types of regression models, including linear regression, multiple regression, logistic regression, and others. Regression analysis is widely used in fields such as finance, economics, social sciences, and engineering, among others.
Probability refers to the chances or likelihood of a particular event or outcome occurring. It is expressed as a number between 0 and 1, with 0 meaning that the event is impossible, and 1 meaning that it is certain to occur. Probability is a fundamental concept in statistics and is used to describe the uncertainty that is involved in various events or experiments. Probabilities can be calculated using various methods, including counting techniques and mathematical formulas. Probability theory provides a framework to reason and draw conclusions about uncertainty, which is useful in many fields, including physics, finance, and engineering.
Parametric tests are a class of statistical tests that assume that the data follows a specific distribution, usually a normal distribution. The underlying assumption is that the population mean and variance are known, or that they can be estimated from the sample. Parametric tests are used when data is continuous, and the sample size is relatively large. Some examples of parametric tests include t-tests, ANOVA (analysis of variance), and regression analysis. Parametric tests are generally more powerful than non-parametric tests, which do not make any assumptions about the distribution of the data. However, the validity of parametric tests depends on the assumptions being met, which can be a challenge in practice.
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