# Difference between revisions of "AP Statistics Curriculum 2007 GLM Regress"

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==[[AP_Statistics_Curriculum_2007 | General Advance-Placement (AP) Statistics Curriculum]] - Regression == | ==[[AP_Statistics_Curriculum_2007 | General Advance-Placement (AP) Statistics Curriculum]] - Regression == | ||

− | === | + | As we discussed in the [[AP_Statistics_Curriculum_2007_GLM_Corr |Correlation section]], many applications involve the analysis of relationships between two, or more, variables involved in the process of interest. Suppose we have bivariate data (''X'' and ''Y'') of a process and we are interested on determining the linear relation between X and Y (e.g., determining a straight line that best fits the pairs of data (''X,Y'')). A linear relationship between ''X'' and ''Y'' will give us the power to make predictions - i.e., given a value of ''X'' predict a corresponding ''Y'' response. Note that in this design, data consists of paired observations (''X,Y'') - for example, the [[SOCR_Data_Dinov_021708_Earthquakes | Longitude and Latitude of the SOCR Eathquake dataset]]. |

− | + | ||

− | < | + | ===Lines in 2D=== |

+ | There are 3 types of lines in 2D planes - Vertical Lines, Horizontal Lines and Oblique Lines. In general, the mathematical representation of lines in 2D is given by equations like <math>aX + bY=c</math>, most frequently expressed as <math>Y=aX + b</math>, provides the line is not vertical. | ||

− | === | + | Recall that there is a one-to-one correspondence between any line in 2D and (linear) equations of the form |

− | + | : If the line is '''vertical''' (<math>X_1 =X_2</math>): <math>X=X_1</math>; | |

+ | : If the line is '''horizontal''' (<math>Y_1 =Y_2</math>): <math>Y=Y_1</math>; | ||

+ | : Otherwise ('''oblique''' line): <math>{Y-Y_1 \over Y_2-Y_1}= {X-X_1 \over X_2-X_1}</math>, (for <math>X_1\not=X_2</math> and <math>Y_1\not=Y_2</math>) | ||

+ | where <math>(X_1,Y_1)</math> and <math>(X_2, Y_2)</math> are two points on the line of interest (2-distinct points in 2D determine a unique line). | ||

− | * | + | * Try drawing the following lines manually and [http://www.pserc.cornell.edu/pserc/java/graph/examples/parse1d.html using this applet]: |

+ | : Y=2X+1 | ||

+ | : Y=-3X-5 | ||

− | === | + | === Linear Modeling - Regression === |

− | + | There are two contexts for regression: | |

+ | * Y is an observed variable and X is specified by the researcher - e.g., Y is hair growth after X months, for individuals at certain dose levels of hair growth cream. | ||

− | * | + | * X and Y are both observed variables - e.g., [[SOCR_Data_Dinov_020108_HeightsWeights | Height (Y) and weight (X)]] for 20 randomly selected individuals from the population. |

− | + | Suppose we have ''n'' pairs ''(X,Y)'', {<math>X_1, X_2, X_3, \cdots, X_n</math>} and {<math>Y_1, Y_2, Y_3, \cdots, Y_n</math>}, of observations of the same process. If a [[SOCR_EduMaterials_Activities_ScatterChart |scatterplot]] of the data suggests a general linear trend, it would be reasonable to fit a line to the data. The main question is how to determine the best line? | |

− | |||

− | === | + | ====[[AP_Statistics_Curriculum_2007_GLM_Corr#Airfare_Example |Airfare Example]]==== |

− | + | We can see from the [[SOCR_EduMaterials_Activities_ScatterChart |scatterplot]] that greater distance is associated with higher airfare. In other words airports that tend to be further from Baltimore tend to be more expensive airfare. To decide on the best fitting line, we use the '''least-squares method''' to fit the least squares (regression) line. | |

− | + | <center>[[Image:SOCR_EBook_Dinov_GLM_Regr_021708_Fig1.jpg|500px]]</center> | |

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<hr> | <hr> | ||

===References=== | ===References=== | ||

− | |||

<hr> | <hr> |

## Revision as of 22:45, 17 February 2008

## Contents

## General Advance-Placement (AP) Statistics Curriculum - Regression

As we discussed in the Correlation section, many applications involve the analysis of relationships between two, or more, variables involved in the process of interest. Suppose we have bivariate data (*X* and *Y*) of a process and we are interested on determining the linear relation between X and Y (e.g., determining a straight line that best fits the pairs of data (*X,Y*)). A linear relationship between *X* and *Y* will give us the power to make predictions - i.e., given a value of *X* predict a corresponding *Y* response. Note that in this design, data consists of paired observations (*X,Y*) - for example, the Longitude and Latitude of the SOCR Eathquake dataset.

### Lines in 2D

There are 3 types of lines in 2D planes - Vertical Lines, Horizontal Lines and Oblique Lines. In general, the mathematical representation of lines in 2D is given by equations like \(aX + bY=c\), most frequently expressed as \(Y=aX + b\), provides the line is not vertical.

Recall that there is a one-to-one correspondence between any line in 2D and (linear) equations of the form

- If the line is
**vertical**(\(X_1 =X_2\))\[X=X_1\]; - If the line is
**horizontal**(\(Y_1 =Y_2\))\[Y=Y_1\]; - Otherwise (
**oblique**line)\[{Y-Y_1 \over Y_2-Y_1}= {X-X_1 \over X_2-X_1}\], (for \(X_1\not=X_2\) and \(Y_1\not=Y_2\))

where \((X_1,Y_1)\) and \((X_2, Y_2)\) are two points on the line of interest (2-distinct points in 2D determine a unique line).

- Try drawing the following lines manually and using this applet:

- Y=2X+1
- Y=-3X-5

### Linear Modeling - Regression

There are two contexts for regression:

- Y is an observed variable and X is specified by the researcher - e.g., Y is hair growth after X months, for individuals at certain dose levels of hair growth cream.

- X and Y are both observed variables - e.g., Height (Y) and weight (X) for 20 randomly selected individuals from the population.

Suppose we have *n* pairs *(X,Y)*, {\(X_1, X_2, X_3, \cdots, X_n\)} and {\(Y_1, Y_2, Y_3, \cdots, Y_n\)}, of observations of the same process. If a scatterplot of the data suggests a general linear trend, it would be reasonable to fit a line to the data. The main question is how to determine the best line?

#### Airfare Example

We can see from the scatterplot that greater distance is associated with higher airfare. In other words airports that tend to be further from Baltimore tend to be more expensive airfare. To decide on the best fitting line, we use the **least-squares method** to fit the least squares (regression) line.

### References

- SOCR Home page: http://www.socr.ucla.edu

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