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6-1 Introduction To Empirical Models

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6-1 Introduction To Empirical Models

6-1 Introduction To Empirical Models

Based on the scatter diagram, it is probably reasonable to assume that the mean of the random variable Y is related to x by the following straight-line relationship:

where the slope and intercept of the line are called regression coefficients.

The simple linear regression model is given by

where is the random error term.

6-1 Introduction To Empirical Models

6-1 Introduction To Empirical Models

We think of the regression model as an empirical model.

Suppose that the mean and variance of are 0 and 2, respectively, then

The variance of Y given x is

6-1 Introduction To Empirical Models

- The true regression model is a line of mean values:

- where 1 can be interpreted as the change in the mean of Y for a unit change in x.
- Also, the variability of Y at a particular value of x is determined by the error variance, 2.
- This implies there is a distribution of Y-values at each x and that the variance of this distribution is the same at each x.

6-1 Introduction To Empirical Models

6-1 Introduction To Empirical Models

A Multiple Linear Regression Model:

where

= the intercept of the plane

, = partial regression coefficients

6-2 Simple Linear Regression

6-2.1 Least Squares Estimation

- The case of simple linear regression considers a single regressoror predictorx and a dependent or response variableY.
- The expected value of Y at each level of x is a random variable:

- We assume that each observation, Y, can be described by the model

6-2 Simple Linear Regression

6-2.1 Least Squares Estimation

- Suppose that we have n pairs of observations (x1, y1), (x2, y2), …, (xn, yn).
- The method of least squares is used to estimate the parameters, 0 and 1 by minimizing the sum of the squares of the vertical deviations in Figure 6-6.

6-2 Simple Linear Regression

6-2.1 Least Squares Estimation

- Using Equation 6-8, the n observations in the sample can be expressed as

- The sum of the squares of the deviations of the observations from the true regression line is

6-2 Simple Linear Regression

6-2.1 Least Squares Estimation

6-2 Simple Linear Regression

6-2.1 Least Squares Estimation

6-2 Simple Linear Regression

6-2.1 Least Squares Estimation

6-2 Simple Linear Regression

6-2.1 Least Squares Estimation

6-2 Simple Linear Regression

6-2.1 Least Squares Estimation

6-2 Simple Linear Regression

6-2.1 Least Squares Estimation

6-2 Simple Linear Regression

6-2.1 Least Squares Estimation

6-2 Simple Linear Regression

6-2.1 Least Squares Estimation

6-2 Simple Linear Regression

Sums of Squares and

Cross-products Matrix

The Sums of squares and cross-products matrix is a convenient way to summarize the quantities needed to do the hand calculations in regression. It also plays a key role in the internal calculations of the computer. It is outputed from PROC REG and PROC GLM if the XPX option is included on the model statement. The elements are

X’X

6-2 Simple Linear Regression

6-2.1 Least Squares Estimation

6-2 Simple Linear Regression

Regression Assumptions and Model Properties

6-2 Simple Linear Regression

Regression Assumptions and Model Properties

6-2 Simple Linear Regression

Regression and Analysis of Variance

6-2 Simple Linear Regression

Regression and Analysis of Variance

6-2 Simple Linear Regression

Example 6-1

OPTIONS NOOVP NODATE NONUMBER LS=80;

DATA ex61;

INPUT salt area @@;

LABEL salt='Salt Conc' area='Roadway area';

CARDS;

3.80.19 5.90.15 14.10.57 10.40.4

14.60.7 14.50.67 15.10.63 11.90.47

15.50.75 9.30.6 15.60.78 20.80.81

14.60.78 16.60.69 25.61.3 20.91.05

29.91.52 19.61.06 31.31.74 32.71.62

PROC REG;

MODEL salt=area/xpx r;

PLOT salt*area; /* Scatter Plot*/

RUN; QUIT;

6-2 Simple Linear Regression

6-2 Simple Linear Regression

Linear Regression of SALT vs AREA

The REG Procedure

Model: MODEL1

Model Crossproducts X'X X'Y Y'Y

Variable Label Intercept area salt

Intercept Intercept 20 16.48 342.7

area Roadway area 16.48 17.2502 346.793

salt Salt Conc 342.7 346.793 7060.03

-----------------------------------------------------------------------------------------

Linear Regression of SALT vs AREA

The REG Procedure

Model: MODEL1

Dependent Variable: salt SaltConc

Number of Observations Read 20

Number of Observations Used 20

-----------------------------------------------------------------------------------------

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr > F

Model 1 1130.14924 1130.14924 352.46 <.0001

Error 18 57.71626 3.20646

Corrected Total 19 1187.86550

Root MSE 1.79066 R-Square 0.9514

Dependent Mean 17.13500 Adj R-Sq 0.9487

CoeffVar 10.45030

Parameter Estimates

Parameter Standard

Variable Label DF Estimate Error t Value Pr> |t|

Intercept Intercept1 2.67655 0.86800 3.08 0.0064

area Roadway area 1 17.54667 0.93463 18.77 <.0001

6-2 Simple Linear Regression

SAS 시스템

The REG Procedure

Model: MODEL1

Dependent Variable: salt SaltConc

Output Statistics

Dependent Predicted Std Error Std Error Student Cook's

Obs Variable Value Mean Predict Residual ResidualResidual -2-1 0 1 2 D

1 3.8000 6.0104 0.7152 -2.2104 1.642 -1.346 | **| | 0.172

2 5.9000 5.3085 0.7464 0.5915 1.628 0.363 | | | 0.014

3 14.1000 12.6781 0.4655 1.4219 1.729 0.822 | |* | 0.025

4 10.4000 9.6952 0.5633 0.7048 1.700 0.415 | | | 0.009

5 14.6000 14.9592 0.4168 -0.3592 1.741 -0.206 | | | 0.001

6 14.5000 14.4328 0.4255 0.0672 1.739 0.0386 | | | 0.000

7 15.1000 13.7309 0.4395 1.3691 1.736 0.789 | |* | 0.020

8 11.9000 10.9235 0.5194 0.9765 1.714 0.570 | |* | 0.015

9 15.5000 15.8365 0.4063 -0.3365 1.744 -0.193 | | | 0.001

10 9.3000 13.2045 0.4518 -3.9045 1.733 -2.253 | ****| | 0.173

11 15.6000 16.3629 0.4025 -0.7629 1.745 -0.437 | | | 0.005

12 20.8000 16.8893 0.4006 3.9107 1.745 2.241 | |**** | 0.132

13 14.6000 16.3629 0.4025 -1.7629 1.745 -1.010 | **| | 0.027

14 16.6000 14.7837 0.4195 1.8163 1.741 1.043 | |** | 0.032

15 25.6000 25.4872 0.5985 0.1128 1.688 0.0668 | | | 0.000

16 20.9000 21.1005 0.4527 -0.2005 1.732 -0.116 | | | 0.000

17 29.9000 29.3475 0.7639 0.5525 1.620 0.341 | | | 0.013

18 19.6000 21.2760 0.4571 -1.6760 1.731 -0.968 | *| | 0.033

19 31.3000 33.2077 0.9451 -1.9077 1.521 -1.254 | **| | 0.304

20 32.7000 31.1021 0.8449 1.5979 1.579 1.012 | |** | 0.147

Sum of Residuals 0

Sum of Squared Residuals 57.71626

Predicted Residual SS (PRESS) 70.97373

6-2 Simple Linear Regression

6-2.2 Testing Hypothesis in Simple Linear Regression

Use of t-Tests

Suppose we wish to test

An appropriate test statistic would be

6-2 Simple Linear Regression

6-2.2 Testing Hypothesis in Simple Linear Regression

Use of t-Tests

We would reject the null hypothesis if

6-2 Simple Linear Regression

6-2.2 Testing Hypothesis in Simple Linear Regression

Use of t-Tests

Suppose we wish to test

An appropriate test statistic would be

6-2 Simple Linear Regression

6-2.2 Testing Hypothesis in Simple Linear Regression

Use of t-Tests

We would reject the null hypothesis if

6-2 Simple Linear Regression

6-2.2 Testing Hypothesis in Simple Linear Regression

Use of t-Tests

An important special case of the hypotheses of Equation 6-23 is

These hypotheses relate to the significance of regression.

Failure to reject H0 is equivalent to concluding that there is no linear relationship between x and Y.

6-2 Simple Linear Regression

6-2.2 Testing Hypothesis in Simple Linear Regression

Use of t-Tests

6-2 Simple Linear Regression

6-2.2 Testing Hypothesis in Simple Linear Regression

Use of t-Tests

6-2 Simple Linear Regression

The Analysis of Variance Approach

6-2 Simple Linear Regression

6-2.2 Testing Hypothesis in Simple Linear Regression

The Analysis of Variance Approach

6-2 Simple Linear Regression

6-2.3 Confidence Intervals in Simple Linear Regression

6-2 Simple Linear Regression

6-2.3 Confidence Intervals in Simple Linear Regression

6-2 Simple Linear Regression

6-2 Simple Linear Regression

6-2.4 Prediction of Future Observations

6-2 Simple Linear Regression

6-2.4 Prediction of Future Observations

6-2 Simple Linear Regression

6-2.5 Checking Model Adequacy

- Fitting a regression model requires several assumptions.
- Errors are uncorrelated random variables with mean zero;
- Errors have constant variance; and,
- Errors be normally distributed.

- The analyst should always consider the validity of these assumptions to be doubtful and conduct analyses to examine the adequacy of the model

6-2 Simple Linear Regression

6-2.5 Checking Model Adequacy

- The residuals from a regression model are ei = yi - ŷi , where yiis an actual observation and ŷi is the corresponding fitted value from the regression model.
- Analysis of the residuals is frequently helpful in checking the assumption that the errors are approximately normally distributed with constant variance, and in determining whether additional terms in the model would be useful.

6-2 Simple Linear Regression

6-2.5 Checking Model Adequacy

- As an approximate check of normality, construct a frequency histogram or anormal probability plot of residuals.
- Standardize the residuals by computing , i = 1, 2, …, n. If the errors are normally distributed, approximately 95% of the standardized residuals should fall in the interval (.

6-2 Simple Linear Regression

6-2.5 Checking Model Adequacy

- Plot the residuals (1) in time sequence (if known), (2) against the , and (3) against the independent variable .

,

6-2 Simple Linear Regression

6-2.5 Checking Model Adequacy

6-2 Simple Linear Regression

6-2.5 Checking Model Adequacy

6-2 Simple Linear Regression

6-2.5 Checking Model Adequacy

6-2 Simple Linear Regression

Example 6-1 (Continued)

OPTIONS NOOVP NODATE NONUMBER LS=120;

DATA ex61;

INPUT salt area @@;

LABEL salt='Salt Conc' area='Roadway area';

CARDS;

3.80.19 5.90.15 14.10.57 10.40.4

14.60.7 14.50.67 15.10.63 11.90.47

15.50.75 9.30.6 15.60.78 20.80.81

14.60.78 16.60.69 25.61.3 20.91.05

29.91.52 19.61.06 31.31.74 32.71.62

PROCREG;

MODEL salt=area/CLB CLM CLI;

/* CLB for (100-) % confidence limits for the parameter estimates,

CLM for (100-) % confidence limits for the expected value of the dependent variable,

CLI for (100-)% confidence limits for an individual predicted value */

PLOT salt*area/predconf;

/* predfor (100-) % prediction intervals for individual responses,

conf for (100-) % confidence intervals for the mean */

PLOT npp.* residual.; /* Normal probability-probability plot */

PLOT residual.*pred.; /* RESIDUAL PLOT */

RUN; QUIT;

RUN; QUIT;

6-2 Simple Linear Regression

Linear Regression of SALT vs AREA

The REG Procedure

Model: MODEL1

Dependent Variable: salt SaltConc

Number of Observations Read 20

Number of Observations Used 20

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr > F

Model 1 1130.14924 1130.14924 352.46 <.0001

Error 18 57.71626 3.20646

Corrected Total 19 1187.86550

Root MSE 1.79066 R-Square 0.9514

Dependent Mean 17.13500 Adj R-Sq 0.9487

CoeffVar 10.45030

Parameter Estimates

Parameter Standard

Variable Label DF Estimate Error t Value Pr > |t|

Intercept Intercept 1 2.67655 0.86800 3.08 0.0064

area Roadway area 1 17.54667 0.93463 18.77 <.0001

Parameter Estimates

Variable Label DF 95% Confidence Limits

Intercept Intercept 1 0.85294 4.50015

area Roadway area 1 15.58308 19.51025

6-2 Simple Linear Regression

SAS 시스템

The REG Procedure

Model: MODEL1

Dependent Variable: salt SaltConc

Output Statistics

Dependent Predicted Std Error Std Error Student Cook's

Obs Variable Value Mean Predict 95% CL Mean 95% CL Predict Residual ResidualResidual -2-1 0 1 2 D

1 3.8000 6.0104 0.7152 4.5079 7.5129 1.9594 10.0614 -2.2104 1.642 -1.346 | **| | 0.172

2 5.9000 5.3085 0.7464 3.7404 6.8767 1.2328 9.3843 0.5915 1.628 0.363 | | | 0.014

3 14.1000 12.6781 0.4655 11.7002 13.6561 8.7911 16.5652 1.4219 1.729 0.822 | |* | 0.025

4 10.4000 9.6952 0.5633 8.5117 10.8788 5.7514 13.6390 0.7048 1.700 0.415 | | | 0.009

5 14.6000 14.9592 0.4168 14.0835 15.8350 11.0966 18.8218 -0.3592 1.741 -0.206 | | | 0.001

6 14.5000 14.4328 0.4255 13.5389 15.3267 10.5660 18.2996 0.0672 1.739 0.0386 | | | 0.000

7 15.1000 13.7309 0.4395 12.8075 14.6544 9.8572 17.6047 1.3691 1.736 0.789 | |* | 0.020

8 11.9000 10.9235 0.5194 9.8322 12.0147 7.0064 14.8406 0.9765 1.714 0.570 | |* | 0.015

9 15.5000 15.8365 0.4063 14.9829 16.6902 11.9789 19.6942 -0.3365 1.744 -0.193 | | | 0.001

10 9.3000 13.2045 0.4518 12.2553 14.1538 9.3246 17.0845 -3.9045 1.733 -2.253 | ****| | 0.173

11 15.6000 16.3629 0.4025 15.5173 17.2086 12.5070 20.2189 -0.7629 1.745 -0.437 | | | 0.005

12 20.8000 16.8893 0.4006 16.0477 17.7310 13.0343 20.7444 3.9107 1.745 2.241 | |**** | 0.132

13 14.6000 16.3629 0.4025 15.5173 17.2086 12.5070 20.2189 -1.7629 1.745 -1.010 | **| | 0.027

14 16.6000 14.7837 0.4195 13.9023 15.6652 10.9198 18.6477 1.8163 1.741 1.043 | |** | 0.032

15 25.6000 25.4872 0.5985 24.2297 26.7447 21.5206 29.4538 0.1128 1.688 0.0668 | | | 0.000

16 20.9000 21.1005 0.4527 20.1495 22.0516 17.2201 24.9809 -0.2005 1.732 -0.116 | | | 0.000

17 29.9000 29.3475 0.7639 27.7427 30.9523 25.2575 33.4375 0.5525 1.620 0.341 | | | 0.013

18 19.6000 21.2760 0.4571 20.3156 22.2364 17.3933 25.1587 -1.6760 1.731 -0.968 | *| | 0.033

19 31.3000 33.2077 0.9451 31.2221 35.1934 28.9538 37.4616 -1.9077 1.521 -1.254 | **| | 0.304

20 32.7000 31.1021 0.8449 29.3271 32.8772 26.9424 35.2619 1.5979 1.579 1.012 | |** | 0.147

Sum of Residuals 0

Sum of Squared Residuals 57.71626

Predicted Residual SS (PRESS) 70.97373

6-2 Simple Linear Regression

Scatter Diagram

6-2 Simple Linear Regression

Normal Probability plot

6-2 Simple Linear Regression

Residual Plot

6-2 Simple Linear Regression

6-2.6 Correlation and Regression

- as the population correlation coefficient, which is a measure of the strength of the linear relationship between Y and X in the population or joint distribution.

The sample correlation coefficient between X and Y is

6-2 Simple Linear Regression

6-2.6 Correlation and Regression

The sample correlation coefficient is also closely related to the slope in a linear regression model

6-2 Simple Linear Regression

Correlation Analysis

Correlation is a measure of the linear relationship between two random variables, X and Y. It is a parameter of the bivariate distribution (joint distribution) of X & Y and will be denoted by the Greek letter rho, .

= Corr(X, Y)

If =1 then X and Y have a perfect direct linear relationship.

If =1 then X and Y have a perfect inverse linear relationship.

If =0 then X and Y have no linear relationship.

If then X and Y are directly related. The closer to 1 the more perfect the linear relationship.

If then X and Y are inversely related. The closer to the more perfect the linear relationship.

6-2 Simple Linear Regression

6-2 Simple Linear Regression

6-2.6 Correlation and Regression

It is often useful to test the hypotheses

The appropriate test statistic for these hypotheses is

Reject H0 if |t0| > t/2,n-2.

6-2 Simple Linear Regression

Example 6-1 (Continued)

OPTIONS NOOVP NODATE NONUMBER LS=80;

DATA ex61;

INPUT salt area @@;

LABEL salt='Salt Conc' area='Roadway area';

CARDS;

3.80.19 5.90.15 14.10.57 10.40.4

14.60.7 14.50.67 15.10.63 11.90.47

15.50.75 9.30.6 15.60.78 20.80.81

14.60.78 16.60.69 25.61.3 20.91.05

29.91.52 19.61.06 31.31.74 32.71.62

PROCCORR DATA=EX61;

VAR SALT AREA;

TITLE 'Correlation between SALT and AREA';

PROCREG;

MODEL salt=area/XPX;

TITLE 'Linear Regression of SALT vs AREA';

RUN; QUIT;

6-2 Simple Linear Regression

Linear Regression of SALT vs AREA

The REG Procedure

Model: MODEL1

Dependent Variable: salt SaltConc

Number of Observations Read 20

Number of Observations Used 20

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr > F

Model 1 1130.14924 1130.14924 352.46 <.0001

Error 18 57.71626 3.20646

Corrected Total 19 1187.86550

Root MSE 1.79066 R-Square 0.9514

Dependent Mean 17.13500 Adj R-Sq 0.9487

CoeffVar 10.45030

Parameter Estimates

Parameter Standard

Variable Label DF Estimate Error t Value Pr > |t|

Intercept Intercept 1 2.67655 0.86800 3.08 0.0064

area Roadway area 1 17.54667 0.93463 18.77 <.0001

Correlation between SALT and AREA

CORR 프로시저

2 Variables: salt area

단순 통계량

변수 N 평균 표준편차 합 최소값 최대값

salt 20 17.13500 7.90691 342.70000 3.80000 32.70000

area 20 0.82400 0.43954 16.48000 0.15000 1.74000

단순 통계량

변수 레이블

salt SaltConc

area Roadway area

피어슨 상관 계수, N = 20

H0: Rho=0 가정하에서 Prob > |r|

salt area

salt 1.00000 0.97540

Salt Conc <.0001

area 0.97540 1.00000

Roadway area <.0001

6-3 Multiple Regression

6-3.1 Estimation of Parameters in Multiple Regression

6-3 Multiple Regression

6-3.1 Estimation of Parameters in Multiple Regression

- The least squares function is given by

- The least squares estimates must satisfy

6-3 Multiple Regression

6-3.1 Estimation of Parameters in Multiple Regression

- The least squares normal equations are

- The solution to the normal equations are the least squares estimators of the regression coefficients.

6-3 Multiple Regression

6-3 Multiple Regression

6-3 Multiple Regression

6-3 Multiple Regression

6-3 Multiple Regression

6-2 Simple Linear Regression

Example 6-7

OPTIONS NOOVP NODATE NONUMBER LS=80;

DATA ex67;

INPUT strength length height @@;

CARDS;

9.95 2 50 24.45 8 110 31.75 11 120 35 10 550

25.02 8 295 16.86 4 200 14.382 375 9.6 2 52

24.35 9 100 27.5 8 300 17.084 412 37 11 400

41.95 12 500 11.66 2 360 21.654 205 17.89 4 400

69 20 600 10.3 1 585 34.93 10 540 46.59 15 250

44.88 15 290 54.12 16 510 56.63 17 590 22.13 6100

21.15 5 400

PROCSGSCATTER data=ex67;

MATRIX STRENGTH LENGTH HEIGHT;

TITLE 'Scatter Plot Matrix for Wire Bond Data';

PROCREG data=ex67;

MODEL strength=length height/xpx r;

TITLE 'Multiple Regression';

RUN; QUIT;

6-2 Simple Linear Regression

6-2 Simple Linear Regression

Multiple Regression

The REG Procedure

Model: MODEL1

Model Crossproducts X'X X'Y Y'Y

Variable Intercept length height strength

Intercept 25 206 8294 725.82

length 206 2396 77177 8008.47

height 8294 77177 3531848 274816.71

strength 725.82 8008.47 274816.71 27178.5316

-----------------------------------------------------------------------------------------

Multiple Regression

The REG Procedure

Model: MODEL1

Dependent Variable: strength

Number of Observations Read 25

Number of Observations Used 25

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr > F

Model 2 5990.77122 2995.38561 572.17 <.0001

Error 22 115.17348 5.23516

Corrected Total 24 6105.94470

Root MSE 2.28805 R-Square 0.9811

Dependent Mean 29.03280 Adj R-Sq 0.9794

CoeffVar 7.88090

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr > |t|

Intercept 1 2.26379 1.06007 2.14 0.0441

length 1 2.74427 0.09352 29.34 <.0001

height 1 0.01253 0.00280 4.48 0.0002

6-2 Simple Linear Regression

Multiple Regression

The REG Procedure

Model: MODEL1

Dependent Variable: strength

Output Statistics

Dependent Predicted Std Error Std Error Student Cook's

Obs Variable Value Mean Predict Residual ResidualResidual -2-1 0 1 2 D

1 9.9500 8.3787 0.9074 1.5713 2.100 0.748 | |* | 0.035

2 24.4500 25.5960 0.7645 -1.1460 2.157 -0.531 | *| | 0.012

3 31.7500 33.9541 0.8620 -2.2041 2.119 -1.040 | **| | 0.060

4 35.0000 36.5968 0.7303 -1.5968 2.168 -0.736 | *| | 0.021

5 25.0200 27.9137 0.4677 -2.8937 2.240 -1.292 | **| | 0.024

6 16.8600 15.7464 0.6261 1.1136 2.201 0.506 | |* | 0.007

7 14.3800 12.4503 0.7862 1.9297 2.149 0.898 | |* | 0.036

8 9.6000 8.4038 0.9039 1.1962 2.102 0.569 | |* | 0.020

9 24.3500 28.2150 0.8185 -3.8650 2.137 -1.809 | ***| | 0.160

10 27.5000 27.9763 0.4651 -0.4763 2.240 -0.213 | | | 0.001

11 17.0800 18.4023 0.6960 -1.3223 2.180 -0.607 | *| | 0.013

12 37.0000 37.4619 0.5246 -0.4619 2.227 -0.207 | | | 0.001

13 41.9500 41.4589 0.6553 0.4911 2.192 0.224 | | | 0.001

14 11.6600 12.2623 0.7689 -0.6023 2.155 -0.280 | | | 0.003

15 21.6500 15.8091 0.6213 5.8409 2.202 2.652 | |***** | 0.187

16 17.8900 18.2520 0.6785 -0.3620 2.185 -0.166 | | | 0.001

17 69.0000 64.6659 1.1652 4.3341 1.969 2.201 | |**** | 0.565

18 10.3000 12.3368 1.2383 -2.0368 1.924 -1.059 | **| | 0.155

19 34.9300 36.4715 0.7096 -1.5415 2.175 -0.709 | *| | 0.018

20 46.5900 46.5598 0.8780 0.0302 2.113 0.0143 | | | 0.000

21 44.8800 47.0609 0.8238 -2.1809 2.135 -1.022 | **| | 0.052

22 54.1200 52.5613 0.8432 1.5587 2.127 0.733 | |* | 0.028

23 56.6300 56.3078 0.9771 0.3222 2.069 0.156 | | | 0.002

24 22.1300 19.9822 0.7557 2.1478 2.160 0.995 | |* | 0.040

25 21.1500 20.9963 0.6176 0.1537 2.203 0.0698 | | | 0.000

Sum of Residuals 0

Sum of Squared Residuals 115.17348

Predicted Residual SS (PRESS) 156.16295

6-3 Multiple Regression

6-3.1 Estimation of Parameters in Multiple Regression

6-3 Multiple Regression

6-3.2 Inferences in Multiple Regression

Test for Significance of Regression

6-3 Multiple Regression

6-3.2 Inferences in Multiple Regression

Inference on Individual Regression Coefficients

- This is called a partial or marginal test

6-3 Multiple Regression

6-3.2 Inferences in Multiple Regression

Confidence Intervals on the Mean Response and Prediction Intervals

6-3 Multiple Regression

6-3.2 Inferences in Multiple Regression

Confidence Intervals on the Mean Response and Prediction Intervals

6-3 Multiple Regression

6-3.2 Inferences in Multiple Regression

Confidence Intervals on the Mean Response and Prediction Intervals

6-3 Multiple Regression

6-3.2 Inferences in Multiple Regression

A Test for the Significance of a Group of Regressors

6-3 Multiple Regression

6-3.3 Checking Model Adequacy

Residual Analysis

6-3 Multiple Regression

6-3.3 Checking Model Adequacy

Residual Analysis

6-3 Multiple Regression

6-3.3 Checking Model Adequacy

Residual Analysis

6-3 Multiple Regression

6-3.3 Checking Model Adequacy

Residual Analysis

6-3 Multiple Regression

6-3.3 Checking Model Adequacy

Residual Analysis

6-3 Multiple Regression

6-3.3 Checking Model Adequacy

Influential Observations

6-3 Multiple Regression

6-3.3 Checking Model Adequacy

Influential Observations

6-3 Multiple Regression

6-3.3 Checking Model Adequacy

6-3 Multiple Regression

6-3.3 Checking Model Adequacy

Multicollinearity

6-4 Other Aspects of Regression

6-4.1 Polynomial Models

6-4 Other Aspects of Regression

6-4.1 Polynomial Models

6-4 Other Aspects of Regression

6-4.1 Polynomial Models

6-4 Other Aspects of Regression

6-4.1 Polynomial Models

6-4 Other Aspects of Regression

6-4.1 Polynomial Models

6-4 Other Aspects of Regression

6-4.2 Categorical Regressors

- Many problems may involve qualitative or categorical variables.
- The usual method 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:

6-4 Other Aspects of Regression

6-4.2 Categorical Regressors

6-4 Other Aspects of Regression

6-4.2 Categorical Regressors

6-4 Other Aspects of Regression

6-4.3 Variable Selection Procedures

Best Subsets Regressions

6-4 Other Aspects of Regression

6-4.3 Variable Selection Procedures

Backward Elimination

6-4 Other Aspects of Regression

6-4.3 Variable Selection Procedures

Forward Selection

6-4 Other Aspects of Regression

6-4.3 Variable Selection Procedures

Stepwise Regression