# 선형 회귀

둘러보기로 가기 검색하러 가기

## 노트

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