선형 회귀

수학노트
둘러보기로 가기 검색하러 가기

노트[편집]

  1. You do not need to know any statistics or linear algebra to understand linear regression.[1]
  2. It is common to talk about the complexity of a regression model like linear regression.[1]
  3. Linear regression assumes that the relationship between your input and output is linear.[1]
  4. Linear regression assumes that your input and output variables are not noisy.[1]
  5. Linear regression is the most widely used statistical technique; it is a way to model a relationship between two sets of variables.[2]
  6. Most software packages and calculators can calculate linear regression.[2]
  7. A linear regression is where the relationships between your variables can be described with a straight line.[2]
  8. Non-linear regressions produce curved lines.[2]
  9. linear regression can be used to fit a predictive model to an observed data set of values of the response and explanatory variables.[3]
  10. Statistical estimation and inference in linear regression focuses on β .[3]
  11. Statistical estimation and inference in linear regression focuses on .[3]
  12. Linear regression can be used to estimate the values of β 1 and β 2 from the measured data.[3]
  13. Linear regression consists of finding the best-fitting straight line through the points.[4]
  14. Linear regression is used for finding linear relationship between target and one or more predictors.[5]
  15. Linear regression is a basic and commonly used type of predictive analysis.[6]
  16. Linear regression is still a good choice when you want a simple model for a basic predictive task.[7]
  17. Azure Machine Learning supports a variety of regression models, in addition to linear regression.[7]
  18. Multiple linear regression involves two or more independent variables that contribute to a single dependent variable.[7]
  19. Problems in which multiple inputs are used to predict a single numeric outcome are also called multivariate linear regression.[7]
  20. In linear regression, each observation consists of two values.[8]
  21. Linear regression can only be used when one has two continuous variables—an independent variable and a dependent variable.[9]
  22. Multiple linear regression (MLR) is used to determine a mathematical relationship among a number of random variables.[9]
  23. Linear Regression is a supervised machine learning algorithm where the predicted output is continuous and has a constant slope.[10]
  24. At the end of these four steps, we show you how to interpret the results from your linear regression.[11]
  25. which performs linear regression and, additionally, returns confidence estimates and an ANOVA table.[12]
  26. reg_multlin_stats which performs multiple linear regression ( v6.2.0 ) and , additionally, returns confidence estimates and an ANOVA table.[12]
  27. Read data from a table and perform a multiple linear regression using reg_multlin_stats .[12]
  28. Unless you specify otherwise, the test statistic used in linear regression is the t-value from a two-sided t-test.[13]
  29. Linear regression, alongside logistic regression, is one of the most widely used machine learning algorithms in real production settings.[14]
  30. This is because linear regression tries to find a straight line that best fits the data.[14]
  31. Unlike the deep learning models (neural networks), linear regression is straightforward to interpret.[14]
  32. The algorithm is not computationally heavy, which means that linear regression is perfect for use cases where scaling is expected.[14]
  33. The linear regression is typically estimated using OLS (ordinary least squares).[15]
  34. The first thing you ought to know about linear regression is how the strange term regression came to be applied to models like this.[16]
  35. It is sometimes known simply as multiple regression, and it is an extension of linear regression.[17]
  36. Both linear and non-linear regression track a particular response using two or more variables graphically.[17]
  37. Multiple linear regression assumes that the amount of error in the residuals is similar at each point of the linear model.[17]
  38. I offer it here on the chance that it might be of interest to those learning, or teaching, linear regression.[18]
  39. Linear regression is a technique used to model the relationships between observed variables.[19]
  40. The F-statistic becomes more important once we start using multiple predictors as in multiple linear regression.[20]
  41. Motivated by this phenomenon, we consider when a perfect fit to training data in linear regression is compatible with accurate prediction.[21]
  42. In this paper, we consider perhaps the simplest setting where we might hope to witness this phenomenon: linear regression.[21]
  43. Theorems 1 and 2 are steps toward understanding this phenomenon by characterizing when it occurs in the simple setting of linear regression.[21]

소스[편집]

메타데이터[편집]

위키데이터[편집]