File:Linear regression.svg
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Size of this PNG preview of this SVG file: 438 × 289 pixels. Other resolutions: 320 × 211 pixels | 640 × 422 pixels | 800 × 528 pixels | 1,024 × 676 pixels | 1,280 × 845 pixels.
Original file (SVG file, nominally 438 × 289 pixels, file size: 71 KB)
Summary[edit]
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This diagram was created with Sage. |
| Description |
English: Random data points and their linear regression. Created with the following Sage (http://sagemath.org) commands:
X = RealDistribution('uniform', [-20, 80
])
Y = RealDistribution('gaussian', 1.5)
f(x) = 3*x/20 + 5
xvals = [X.get_random_element() for _ in range(100)]
data = [(x, f(x) + Y.get_random_element()) for x in xvals]
m, b = var('m b')
g(x) = m*x + b
g(x) = g(x).subs(find_fit(data, g, solution_dict=True))
p = list_plot(data) + plot(g, (x, -20, 60), color='red')
p.save('linear_regression.svg')
|
| Date | |
| Source | Own work |
| Author | Sewaqu |
| Other versions |
Derivative works of this file: Sedm zakladnich nastroju zlepsovani kvality cz.svg |
Licensing[edit]
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I, the copyright holder of this work, release this work into the public domain. This applies worldwide. In some countries this may not be legally possible; if so: I grant anyone the right to use this work for any purpose, without any conditions, unless such conditions are required by law.
|
File history
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 10:42, 31 March 2016 | | 438 × 289 (71 KB) | RaphaelQS (talk | contribs) | transparent background |
| 03:18, 5 November 2010 |  | 438 × 289 (42 KB) | Sewaqu (talk | contribs) | {{Information |Description={{en|1=Random data points and their linear regression. Created with the following Sage (http://sagemath.org) commands: X = RealDistribution('uniform', [-20, 60]) Y = RealDistribution('gaussian', 1.5) f(x) = 3*x/20 + 5 xvals |
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- Least squares
- Gauss–Markov theorem
- Logistic regression
- Errors and residuals
- Linear least squares (mathematics)
- Studentized residual
- Generalized linear model
- Regression analysis
- General linear model
- Mathematical statistics
- Tikhonov regularization
- Total least squares
- Nonlinear regression
- Partial least squares regression
- Probit model
- Ordinary least squares
- Goodness of fit
- Simple linear regression
- Poisson regression
- Robust regression
- Isotonic regression
- Ordered logit
- Ordered probit
- Local regression
- Multinomial logistic regression
- Fixed effects model
- Generalized least squares
- Multilevel model
- Nonparametric regression
- Semiparametric regression
- Discrete choice
- Mixed model
- Random effects model
- Bayesian linear regression
- Bayesian multivariate linear regression
- Multivariate probit model
- Iteratively reweighted least squares
- Segmented regression
- Mixed logit
- Quantile regression
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