SSR 1 in .NET

Paint QR Code in .NET SSR 1
SSR 1
Visual Studio .NET qr code 2d barcode creation with .net
using barcode generating for .net control to generate, create qr barcode image in .net applications.
1, 11 0
.net Framework qr-code reader on .net
Using Barcode recognizer for .net vs 2010 Control to read, scan read, scan image in .net vs 2010 applications.
SSR 1
Barcode generator on .net
using barcode printer for visual studio .net control to generate, create bar code image in visual studio .net applications.
we fit a
Barcode scanner on .net
Using Barcode scanner for visual .net Control to read, scan read, scan image in visual .net applications.
0.00910056x
Control qr size with .net c#
to insert qr codes and qr code iso/iec18004 data, size, image with c#.net barcode sdk
It can be easily verified that the regression sum of squares for this model is SSR 1 SSR 1
Control qr-codes data with .net
to build qr-code and qr bidimensional barcode data, size, image with .net barcode sdk
11 0 1, 0 2 10 02
Control qrcode image with vb
use .net framework qr code 2d barcode maker todraw qr-code on vb.net
Therefore, the extra sum of the squares due to SSR 1 1, 0.5254 0.0312
Bar Code generation on .net
generate, create barcode none for .net projects
given that SSR 1 02 0.4942
2d Matrix Barcode printer on .net
generate, create matrix barcode none in .net projects
10 02
Code 128C maker on .net
using barcode integrating for visual studio .net control to generate, create code 128 image in visual studio .net applications.
are in the model, is
Print matrix barcode in .net
using visual studio .net crystal togenerate matrix barcode for asp.net web,windows application
11 0
Make international standard serial number in .net
use .net vs 2010 issn development todraw issn on .net
The analysis of variance, with the test of H0: 11 0 incorporated into the procedure, is displayed in Table 12-13. Note that the quadratic term contributes significantly to the model.
Visual Studio .NET linear barcode printer in .net c#
use .net vs 2010 1d integrated topaint 1d barcode for c#.net
Table 12-13 Analysis of Variance for Example 12-11, Showing the Test for H0: Source of Variation Regression Linear Quadratic Error Total Sum of Squares Degrees of Freedom 2 1 1 9 11 Mean Square 0.262700 0.494200 0.031200 0.00121
Assign barcode pdf417 in .net
use .net for windows forms pdf417 2d barcode generating toinsert pdf417 2d barcode on .net
0 P-value 5.18E-15 1.17E-15 5.51E-9
Deploy qr codes on .net
use windows forms qr barcode implement tocompose qr in .net
SSR 1 1, 11 0 0 2 0.5254 SSR 1 1 0 0 2 0.4942 SSR 1 11 0 0, 1 2 0.0312 0.0011 0.5265
Control code 128 code set a size in vb
to draw code 128 and code128 data, size, image with vb barcode sdk
f0 2171.07 4084.30 258.18
Control 3 of 9 data with .net c#
to attach 3 of 9 and code 3/9 data, size, image with .net c# barcode sdk
450 12-6.2
Sql Database data matrix creator in .net
use ms reporting service data matrix generator toconnect ecc200 with .net
CHAPTER 12 MULTIPLE LINEAR REGRESSION
Control qr-code size on word
to integrate qrcode and qr bidimensional barcode data, size, image with office word barcode sdk
Categorical Regressors and Indicator Variables
Control qr code image on vb.net
use .net framework qr code iso/iec18004 generating toconnect qr code jis x 0510 with visual basic
The regression models presented in previous sections have been based on quantitative variables, that is, variables that are measured on a numerical scale. For example, variables such as temperature, pressure, distance, and voltage are quantitative variables. Occasionally, we need to incorporate categorical, or qualitative, variables in a regression model. For example, suppose that one of the variables in a regression model is the operator who is associated with each observation yi. Assume that only two operators are involved. We may wish to assign different levels to the two operators to account for the possibility that each operator may have a different effect on the response. The usual method of accounting for the different levels of a qualitative variable is to use indicator variables. For example, to introduce the effect of two different operators into a regression model, we could define an indicator variable as follows: x e 0 if the observation is from operator 1 1 if the observation is from operator 2
In general, a qualitative variable with r-levels can be modeled by r 1 indicator variables, which are assigned the value of either zero or one. Thus, if there are three operators, the different levels will be accounted for by the two indicator variables defined as follows: x1 0 1 0 x2 0 0 1 if the observation is from operator 1 if the observation is from operator 2 if the observation is from operator 3
Indicator variables are also referred to as dummy variables. The following example [from Montgomery, Peck, and Vining (2001)] illustrates some of the uses of indicator variables; for other applications, see Montgomery, Peck, and Vining (2001). EXAMPLE 12-12 A mechanical engineer is investigating the surface finish of metal parts produced on a lathe and its relationship to the speed (in revolutions per minute) of the lathe. The data are shown in Table 12-14. Note that the data have been collected using two different types of cutting tools. Since the type of cutting tool likely affects the surface finish, we will fit the model Y
0 1x1 2x2
where Y is the surface finish, x1 is the lathe speed in revolutions per minute, and x2 is an indicator variable denoting the type of cutting tool used; that is, x2 e 0, for tool type 302 1, for tool type 416 0, the model becomes
The parameters in this model may be easily interpreted. If x2 Y which is a straight-line model with slope becomes Y
0 1x1 2 112 0
and intercept 1
However, if x2
1, the model
12-6 ASPECTS OF MULTIPLE REGRESSION MODELING
Table 12-14 Surface Finish Data for Example 12-13 Observation Number, i 1 2 3 4 5 6 7 8 9 10 Surface Finish yi 45.44 42.03 50.10 48.75 47.92 47.79 52.26 50.52 45.58 44.78 RPM 225 200 250 245 235 237 265 259 221 218 Type of Cutting Tool 302 302 302 302 302 302 302 302 302 302 Observation Number, i 11 12 13 14 15 16 17 18 19 20 Surface Finish yi 33.50 31.23 37.52 37.13 34.70 33.92 32.13 35.47 33.49 32.29 RPM 224 212 248 260 243 238 224 251 232 216 Type of Cutting Tool 416 416 416 416 416 416 416 416 416 416
which is a straight-line model with slope 1 and intercept 0 2 . Thus, the model implies that surface finish is linearly related to lathe speed and Y x x2 0 1 2 that the slope 1 does not depend on the type of cutting tool used. However, the type of cutting tool does affect the intercept, and 2 indicates the change in the intercept associated with a change in tool type from 302 to 416. The X matrix and y vector for this problem are as follows: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 225 200 250 245 235 237 265 259 221 218 224 212 248 260 243 238 224 251 232 216 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 45.44 42.03 50.10 48.75 47.92 47.79 52.26 50.52 45.58 44.78 33.50 31.23 37.52 37.13 34.70 33.92 32.13 35.47 33.49 32.29