AP Statistics Curriculum 2007 GLM Corr

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(Human weight and height)
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{| class="wikitable" style="text-align:center; width:75%" border="1"
{| class="wikitable" style="text-align:center; width:75%" border="1"
| Subject Index  || Height(<math>x_i</math>) || Weight (<math>y_i</math>) || <math>x_i-\bar{x}</math> ||  <math>y_i-\bar{y}</math> || <math>(x_i-\bar{x})^2</math> || <math>(y_i-\bar{y})^2</math> || <math>(x_i-\bar{x})(y_i-\bar{y})</math>
| Subject Index  || Height(<math>x_i</math>) in cm || Weight (<math>y_i</math>) in kg || <math>x_i-\bar{x}</math> ||  <math>y_i-\bar{y}</math> || <math>(x_i-\bar{x})^2</math> || <math>(y_i-\bar{y})^2</math> || <math>(x_i-\bar{x})(y_i-\bar{y})</math>
| 1 || 167 || 60 || 6 || 4.67 || 36 || 21,82 || 28.02
| 1 || 167 || 60 || 6 || 4.67 || 36 || 21,82 || 28.02
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We can easily now compute by hand <math>\bar{x}=966/6=161</math> (cm), <math>\bar{y}=332/6=55</math> (kg), <math>s_x=\sqrt{216/5}=6.57</math> and <math>s_y=\sqrt{215.3/5}=6.56</math>.
Therefore, <math> r_{xy}= {1 \over n-1} \sum_{i=1}^n { {x_i-\bar{x} \over s_x} {y_i-\bar{y}\over s_y}} = 0.904. </math>

Revision as of 05:14, 17 February 2008


General Advance-Placement (AP) Statistics Curriculum - Correlation

Many biomedical, social, engineering and science applications involve the analysis of relationships, if any, between two or more variables involved in the process of interest. We begin with the simplest of all situations where bivariate data (X and Y) are measured for a process and we are interested on determining the association, relation or an appropriate model for these observations (e.g., fitting a straight line to the pairs of (X,Y) data). If we are successful determining a relationship between X and Y, we can use this model 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 height and weight of individuals.

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 (X1 = X2): X = X1;
If the line is horizontal (Y1 = Y2): Y = Y1;
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 (X1,Y1) and (X2,Y2) are two points on the line of interest (2-distinct points in 2D determine a unique line).


The Correlation Coefficient

Correlation coefficient (-1 \leq \rho \leq 1) is a measure of linear association, or clustering around a line of multivariate data. The main relationship between two variables (X, Y) can be summarized by: XX), YY) and the correlation coefficient, denoted by ρ = ρ(X,Y) = R(X,Y).

  • If ρ = 1, we have a perfect positive correlation (straight line relationship between the two variables)
  • If ρ = 0, there is no correlation (random cloud scatter), i.e., no linear relation between X and Y.
  • If ρ = − 1, there is a perfect negative correlation between the variables.

Computing ρ = R(X,Y)

The protocol for computing the correlation involves standardizing, multiplication and averaging.

\rho_{X,Y}={\mathrm{COV}(X,Y) \over \sigma_X \sigma_Y} ={E((X-\mu_X)(Y-\mu_Y)) \over \sigma_X\sigma_Y},

where E is the expected value operator and COV means covariance. Since μX = E(X), σX2 = E(X2) − E2(X) and similarly for Y, we may also write

  • Sample correlation - we only have sampled data - we replace the (unknown) expectations and standard deviations by their sample analogues (sample-mean and sample-standard deviation) to compute the sample correlation correlation:
Suppose {X_1, X_2, X_3, \cdots, X_n} and {Y_1, Y_2, Y_3, \cdots, Y_n} are bivariate observations of the same process and XX) and YY) are the means and standard deviations for the X and Y measurements, respectively.
 r_{xy}=\frac{\sum x_iy_i-n \bar{x} \bar{y}}{(n-1) s_x s_y}=\frac{n\sum x_iy_i-\sum x_i\sum y_i} {\sqrt{n\sum x_i^2-(\sum x_i)^2}~\sqrt{n\sum y_i^2-(\sum y_i)^2}}.
 r_{xy}=\frac{\sum (x_i-\bar{x})(y_i-\bar{y})}{(n-1) s_x s_y}  = {1 \over n-1} \sum_{i=1}^n { {x_i-\bar{x} \over s_x} {y_i-\bar{y}\over s_y}},

where \bar{x} and \bar{y} are the sample means of X  and Y , sx  and sy  are the sample standard deviations of X  and Y  and the sum is from i = 1 to n. We may rewrite this as

 r_{xy}=\frac{\sum x_iy_i-n \bar{x} \bar{y}}{(n-1) s_x s_y}=\frac{n\sum x_iy_i-\sum x_i\sum y_i} {\sqrt{n\sum x_i^2-(\sum x_i)^2}~\sqrt{n\sum y_i^2-(\sum y_i)^2}}.
  • Note: The correlation is defined only if both of the standard deviations are finite and both of them are nonzero. It is a corollary of the Cauchy-Schwarz inequality that the correlation is always bound -1 \leq \rho \leq 1.


Human weight and height

Suppose we took only 6 of the over 2,000 observations of human weight and height included in this SOCR Dataset.

Subject Index Height(xi) in cm Weight (yi) in kg x_i-\bar{x} y_i-\bar{y} (x_i-\bar{x})^2 (y_i-\bar{y})^2 (x_i-\bar{x})(y_i-\bar{y})
1 167 60 6 4.67 36 21,82 28.02
2 170 64 9 8.67 81 75.17 78.03
3 160 57 -1 1.67 1 2.79 -1.67
4 152 46 -9 -9.33 81 87.05 83.97
5 157 55 -4 -0.33 16 0.11 1.32
6 160 50 -1 -5.33 1 28.41 5.33
Total 966 332 0 0 216 215.33 195.0

We can easily now compute by hand \bar{x}=966/6=161 (cm), \bar{y}=332/6=55 (kg), s_x=\sqrt{216/5}=6.57 and s_y=\sqrt{215.3/5}=6.56.

Therefore,  r_{xy}= {1 \over n-1} \sum_{i=1}^n { {x_i-\bar{x} \over s_x} {y_i-\bar{y}\over s_y}} = 0.904.


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