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Estimation of Regression Function

创建日期：2024-05-22 16:23:55

所属分类：统计学statistics

标签： Function

Estimation of Regression Function

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Regression Modelling

Estimation of Regression Function
(Ch 1.6)
Estimated Regression Function
Regression Model: Y = β0 + β1X + ε.
Regression Function: E (Y ) = β0 + β1X .
Use least squares estimation to estimate β0 and β1.
Estimated regression function:
Yˆ = b0 + b1X ,
where
b1 =
∑n
i=1(Xi − X¯ )(Yi − Y¯ )∑n
i=1(Xi − X¯ )2
= SxySxx
, b0 = Y¯ − b1X¯ ,
and we call Yˆ the value of the estimated regression function at the
level X of the predictor variable.
Estimated Regression Function
• We call the value of Y a response and E (Y ) the mean
response. So Yˆ is a point estimator of the mean response
E (Y ) at the level X of the predictor variable.
• It can be shown that Yˆ is an unbiased point estimator of the
mean response E (Y ).
• Given a new level X , we can use Yˆ as a (point) prediction of
the response.
• For the i-th observation in the data, Yi is the observed value,
and we call
Yˆi = b0 + b1Xi ,
the fitted value for the i-th observation.
Residuals
• The i-th residual is the difference between the observed value
Yi and the corresponding fitted value Yˆi , denoted as ei :
ei = Yi − Yˆi .
• For our model, the i-th residual becomes
ei = Yi − (b0 + b1Xi).
Residuals
• Do not confuse
• εi = Yi − E (Yi) "Model error"
• ei = Yi − Yˆi "Residual"
• εi : deviation from the unknown true regression line,
unobservable.
• ei : deviation from the estimated regression line, known.
• Residuals are highly useful for studying whether a given
regression model is appropriate for the data at hand.
Residuals
2 4 6 8 10
5
10
15
X
Y
Residuals
2 4 6 8 10
5
10
15
X
Y
Toluca Company Example
The Toluca Company manufactures refrigeration equipment as well
as many replacement parts. In the past, one of the replacement
parts has been produced periodically in lots of varying sizes. When a
cost improvement program was undertaken, company officials
wished to determine the optimum lot size for producing this part.
The production of this part involves setting up the production
process (which must be done no matter what is the lot size) and
machining and assembly operations. One key input for the model to
ascertain the optimum lot size was the relationship between lot size
and labor hours required to produce the lot. To determine this
relationship, data on lot size and work hours for 25 recent
production runs were utilized.
• Page 19, Chapter 1 of the textbook.
Load Data
• Use dataset from the R package “ALSM”.
# install.packages("ALSM")
library("ALSM")
mydata <- TolucaCompany
dim(mydata)
[1] 25 2
head(mydata, 2)
x y
1 80 399
2 30 121
X <- mydata[,1] # or X <- mydata$x
Y <- mydata[,2] # or Y <- mydata$y
• X = “Lot Size” and Y = “Work Hours”
Load Data
• Or download “Kutner Textbook Datasets” from Wattle, file
named “CH01TA01.txt”.
mydata <- read.table("CH01TA01.txt")
head(mydata, 2)
V1 V2
1 80 399
2 30 121
names(mydata) <- c("x","y")
head(mydata, 2)
x y
1 80 399
2 30 121
Summary Statistics
summary(mydata)
x y
Min. : 20 Min. :113.0
1st Qu.: 50 1st Qu.:224.0
Median : 70 Median :342.0
Mean : 70 Mean :312.3
3rd Qu.: 90 3rd Qu.:389.0
Max. :120 Max. :546.0
Summary Statistics
boxplot(mydata) # or boxplot(X); boxplot(Y)
x y
0
10
0
20
0
30
0
40
0
50
0
Scatter Plot
plot(X, Y, pch = 16, xlab = "Lot size", ylab = "Work hours",
main = "Toluca Company")
20 40 60 80 100 120
10
0
20
0
30
0
40
0
50
0
Toluca Company
Lot size
W
o
rk
h
ou
rs
Fitting Model Manually
• Recall we have
b1 =
∑n
i=1(Xi − X¯ )(Yi − Y¯ )∑n
i=1(Xi − X¯ )2
b0 = Y¯ − b1X¯
Xbar <- mean(X)
Ybar <- mean(Y)
Xcenter <- X - Xbar
Ycenter <- Y - Ybar
Sxy <- sum(Xcenter*Ycenter) # or sum(X*Y)-length(X)*mean(X)*mean(Y)
Sxx<- sum(Xcenterˆ2) # or sum(Xˆ2)-length(X)*mean(X)*mean(X)
Sxy
[1] 70690
Sxx
[1] 19800
Fitting Model Manually
b1 <- Sxy/Sxx
b1
[1] 3.570202
b0 <- Ybar - b1*Xbar
b0
[1] 62.36586
Fitting Model Manually
• Another way to calculate b1,
b1 =
∑n
i=1(Xi − X¯ )(Yi − Y¯ )∑n
i=1(Xi − X¯ )2
= rxy sy sxs2x
= rxy sysx
,
where rxy is the sample correlation between X and Y , sy and
sx are the standard deviations of X and Y .
Fitting Model Manually
b1 <- cov(X, Y)/var(X)
b1
[1] 3.570202
# or
b1 <- cor(X, Y)*sd(Y)/sd(X)
b1
[1] 3.570202
Fitting Model with “lm” Function
mymodel <- lm(Y ~ X)
class(mymodel)
[1] "lm"
# or
# mymodel <- lm(y ~ x, data = mydata)
View(mymodel)
Fitting Model with “lm” Function
mymodel$coefficients # or use coef(mymodel)
(Intercept) X
62.365859 3.570202
Fitting Model with “lm” Function
summary(mymodel)
Call:
lm(formula = Y ~ X)
Residuals:
Min 1Q Median 3Q Max
-83.876 -34.088 -5.982 38.826 103.528
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 62.366 26.177 2.382 0.0259 *
X 3.570 0.347 10.290 4.45e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 48.82 on 23 degrees of freedom
Multiple R-squared: 0.8215, Adjusted R-squared: 0.8138
F-statistic: 105.9 on 1 and 23 DF, p-value: 4.449e-10
Estimated Regression Line
• Yˆ = 62.366+ 3.570X
plot(X, Y, pch = 16)
abline(mymodel, col = "purple", lty = 2, lwd = 2)
20 40 60 80 100 120
10
0
20
0
30
0
40
0
50
0
X
Y
Fitted Values
Yhat <- b0 + b1*X # manually compute
Yfit <- mymodel$fitted.values # get it from model output
# or Yfit <- fitted(mymodel)
Yhat
[1] 347.9820 169.4719 240.8760 383.6840 312.2800 276.5780 490.7901 347.9820
[9] 419.3861 240.8760 205.1739 312.2800 383.6840 133.7699 455.0881 419.3861
[17] 169.4719 240.8760 383.6840 455.0881 169.4719 383.6840 205.1739 347.9820
[25] 312.2800
Yfit
1 2 3 4 5 6 7 8
347.9820 169.4719 240.8760 383.6840 312.2800 276.5780 490.7901 347.9820
9 10 11 12 13 14 15 16
419.3861 240.8760 205.1739 312.2800 383.6840 133.7699 455.0881 419.3861
17 18 19 20 21 22 23 24
169.4719 240.8760 383.6840 455.0881 169.4719 383.6840 205.1739 347.9820
25
312.2800
Predict Y for a New Observation
# Predict Y as the new level of X = 85
Xnew <- 85
Ypredict <- b0 + b1*Xnew
# or Ypredict <- mymodel$coefficients[1] + mymodel$coefficients[2]*Xnew
Ypredict
[1] 365.833
# Or use matrix multiplication
Xnew <- c(1, 85)
Ypredict <- Xnew %*% mymodel$coefficients
Ypredict
[,1]
[1,] 365.833
Residuals
Res <- Y - Yhat # manually compute
Res
[1] 51.0179798 -48.4719192 -19.8759596 -7.6840404 48.7200000 -52.5779798
[7] 55.2098990 4.0179798 -66.3860606 -83.8759596 -45.1739394 -60.2800000
[13] 5.3159596 -20.7698990 -20.0880808 0.6139394 42.5280808 27.1240404
[19] -6.6840404 -34.0880808 103.5280808 84.3159596 38.8260606 -5.9820202
[25] 10.7200000
Res <- mymodel$residuals # get it from model output
# or Res <- residuals(mymodel)
Res
1 2 3 4 5 6
51.0179798 -48.4719192 -19.8759596 -7.6840404 48.7200000 -52.5779798
7 8 9 10 11 12
55.2098990 4.0179798 -66.3860606 -83.8759596 -45.1739394 -60.2800000
13 14 15 16 17 18
5.3159596 -20.7698990 -20.0880808 0.6139394 42.5280808 27.1240404
19 20 21 22 23 24
-6.6840404 -34.0880808 103.5280808 84.3159596 38.8260606 -5.9820202
25
10.7200000
Properties of Fitted Regression Line
(Ch 1.6)
Properties of Fitted Regression Line
1. The sum of residuals is zero: ∑ni=1 ei = 0.
Follows from one of the normal equation.
sum(Res)
[1] 1.29674e-13
Properties of Fitted Regression Line
2. The sum of squared residuals is a minimum: ∑ni=1 e2i .
Properties of Fitted Regression Line
3. The sum of the observed values Yi equals the sum of the fitted
values Yˆi :
n∑
i=1
Yi =
n∑
i=1
Yˆi .
It follows that the mean of the fitted values Yˆi is the same as
the mean of the observed values Yi , namely, Y¯ .
Properties of Fitted Regression Line
4. The sum of the weighted residuals is zero when the residual in
the ith trial is weighted by the level of the predictor variable in
the ith trial:
n∑
i=1
Xiei = 0.
Follows from the second normal equation.
sum(X*Res)
[1] 5.570655e-12
Properties of Fitted Regression Line
5. The sum of the weighted residuals is zero when the residual in
the ith trial is weighted by the fitted value of the response
variable for the ith trial:
n∑
i=1
Yˆiei = 0.
sum(Yhat*Res)
[1] 4.638423e-11
Properties of Fitted Regression Line
6. The fitted regression line always goes through the point (X¯ , Y¯ ).
Estimation of Error Terms Variance
σ2 (Ch 1.7)
Estimation of Error Terms Variance σ2
• The variance σ2 of the error terms εi needs to be estimated as
it measures the variability of the probability distribution of Y .
• In addition, many statistical inferences of the regression model
and getting a prediction interval of Y require an estimator of
σ2.
Estimation of Error Terms Variance σ2
Recall: Estimate σ2 for a single population
s2 =
∑n
i=1(Yi − Y¯ )2
n − 1 .
• ∑ni=1(Yi − Y¯ )2 is called a sum of squares, where Yi − Y¯ is
the deviation of Yi from the estimated mean Y¯ .
• The degrees of freedom (DF) is n − 1 since we lost one
degree of freedom by using Y¯ as an estimator of the unknown
population mean.
• The estimator s2 can be regarded as a mean square (sum of
squares/DF).
• It is unbiased, E (s2) = σ2.
Estimation of Error Terms Variance σ2
For the regression model, estimating σ2 (i.e., Var(Y )), we also need
to calculate a sum of squared deviations, but the deviation of an
observation Yi must be calculated around its own estimated mean
Yˆi , i.e., ei = Yi − Yˆi .
• The appropriate sum of squares, denoted by SSE, is
SSE =
n∑
i=1
e2i =
n∑
i=1
(Yi − Yˆi)2.
• SSE stands for error sum of squares or residual sum of
squares. It has n − 2 degrees of freedom as two degrees of
freedom are lost because we need to estimate two parameters
to obtain Yˆi .
Estimation of Error Terms Variance σ2
Our estimator of σ2 is a mean square, i.e.,
s2e = MSE =
SSE
DF =
∑n
i=1(Yi − Yˆi)2
n − 2 .
• MSE stands for error mean square or residual mean square.
• An estimator of the standard deviation σ is se =
√
MSE .
• It can be shown that MSE is an unbiased point estimator of
σ2, i.e.,
E (MSE ) = σ2.
Estimation of Error Terms Variance σ2
SSE <- sum(Resˆ2)
SSE
[1] 54825.46
n <- length(Y) # or n <- dim(mydata)[1]
MSE <- SSE/(n-2)
MSE
[1] 2383.716
# Estimator of the standard deviation sigma
sigma_hat = sqrt(MSE)
sigma_hat
[1] 48.82331
# This is also called "residual standard error"
Estimation of Error Terms Variance σ2
summary(mymodel)
Call:
lm(formula = Y ~ X)
Residuals:
Min 1Q Median 3Q Max
-83.876 -34.088 -5.982 38.826 103.528
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 62.366 26.177 2.382 0.0259 *
X 3.570 0.347 10.290 4.45e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 48.82 on 23 degrees of freedom
Multiple R-squared: 0.8215, Adjusted R-squared: 0.8138
F-statistic: 105.9 on 1 and 23 DF, p-value: 4.449e-10
Normal Error Regression Model (Ch
1.8)
Normal Error Regression Model
The normal error regression model is
Yi = β0 + β1Xi + εi ,
where the error εi ∼iid N (0, σ2).
• This model implies that the Yi ∼ N (β0 + β1Xi , σ2)
independently.
• Throughout this course unless otherwise stated, we assume the
normal error regression model is applicable. This assumption
simplifies theory and helps us setting up the procedures of
statistical inferences.
No matter what form of the distribution of εi , the least squares
method provides unbiased estimators of β0 and β1 that have
minimum variance among all unbiased linear estimators.
• Read Ch 1.6-1.8 of the textbook.

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1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A PHIL2617 PHYS3116 MIE250 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