REGRESSION

Regression



Regression

This report studies a statistical skin-color model and its adaptation. By Regression analysis, we reveal that (1) skin-color differences among people can be reduced by intensity normalization, and (2) under a certain lighting condition, a skin-color distribution can be characterized by a multivariate normal distribution in the normalized color space. We then propose an adaptive model to characterize human skin-color distributions for locating human faces under different lighting conditions. The parameters of the model are adapted by a linear combination of the known parameters. The maximum likelihood criterion has been used to obtain the optimal estimation of the coefficients. The model has been successfully applied to a real-time face tracker and other applications.

Human face perception is currently an active research area in the computer vision community. Locating and tracking human faces is a prerequisite for face recognition and/or facial expressions analysis, although it is often assumed that a normalized face image is available. In order to locate a human face, the system needs to capture an image using a camera and a frame grabber, to process the image, to search the image for important features, and then to use these features to determine the location of the face. In order to track a human face, the system not only needs to locate a face, but also needs to find the same face in a sequence of images.

Variables Entered/Removed

Model

Variables Entered

Variables Removed

Method

1

priceX2a

.

Enter

a. All requested variables entered.

b. Dependent Variable: sales

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.137a

.019

-.036

230.09712

a. Predictors: (Constant), priceX2

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

18120.198

1

18120.198

.342

.566a

Residual

953004.352

18

52944.686

Total

971124.550

19

a. Predictors: (Constant), priceX2

b. Dependent Variable: salesY

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

965.405

433.724

2.226

.039

priceX2

104.542

178.698

.137

.585

.566

a. Dependent Variable: salesY

y=965.405+104.542 x

x

y

1.8

1153.581

1.9

1164.035

2.1

1184.943

2.2

1195.397

2.2

1195.397

2.3

1205.852

2.4

1216.306

2.2

1195.397

2.3

1205.852

2.5

1226.76

2.5

1226.76

2.6

1237.214

2.7

1247.668

2.8

1258.123

2.9

1268.577

2.4

1216.306

2.4

1216.306

2.5

1226.76

2.7

1247.668

2.8

1258.123

Task II: Comments

From the above scatter plot and estimations in previous section, we can say see that the productions from our equation will not give a good forecast. In the scatter plot, the line of best fit cannot be said as the best estimator of the future trends.

Elasticity is the responsiveness to which one variable responds to a change in another variable Price elasticity of demand (PED) measures the responsiveness of quantity demanded of a product to a change in its price. If a relatively small change in price leads to a relatively large change in demand, the product is said to be 'elastic'.

Whereas if quantity demanded is relatively unresponsive to a change in price the product is said to be 'inelastic'.

The final result is when a change in price causes no change to the ...