Difference between revisions of "Página de pruebas"

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[[File:Linear_regression2.png|600px|thumb|center|Takinf from https://www.youtube.com/watch?v=nk2CQITm_eo&t=267s]]
 
[[File:Linear_regression2.png|600px|thumb|center|Takinf from https://www.youtube.com/watch?v=nk2CQITm_eo&t=267s]]
 
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<!-- [[File:SimpleLinearRegression.png|350px|thumb|center|]] -->
 
  
 
[[File:SimpleLinearRegression2.png|600px|center|]]
 
[[File:SimpleLinearRegression2.png|600px|center|]]
 
 
'''The regression equation:'''
 
<math> y = a + bx </math>
 
 
* Dependent variable: <math> y </math>:
 
* Independent variable: <math> x </math>:
 
* Slope: <math> b = r \frac{S_y}{S_x}</math>
 
: The slope is the amount of change in units of <math>y</math> for each unitchange in <math>x</math>.
 
* <math> y </math> intercept: <math> a = \bar{y} - b\bar{x} </math>:
 
  
  
 
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Revision as of 21:10, 27 December 2020

Simple Linear Regression

https://www.youtube.com/watch?v=nk2CQITm_eo&t=267s


In general, there are 3 main stages in Linear regression:

1. Using Least-squares to fit a line to the data
2. Calculating
3. Calculating a Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle p-value} for Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle R^2}



1. Using Least-squares to fit a line to the data
  • First, draw a line through the data.
  • Second, calculate the Residual sum of squares: Measure the distance from the line to each data point (residual), square each distance, and then add them up.
The distance from a line to a data point is called a residual
  • Then, we rotate the line a little bit and calculate the RSS. We do this many times.
  • ...
  • Then, the line that represents the linear regression is the one corresponding to the rotation that has the least RSS. The regression equation:
The equation is composed of 2 parameters:
  • Slope: Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle b }
The slope is the amount of change in units of Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle y} for each unitchange in Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle x} .
  • The Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle y-axis} intercept: Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle a }



2. Calculating Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle R^2}





SimpleLinearRegression2.png