Statistics

1. Dr. Pritpal Singh
Sr. Statistician
Department of Plant Breeding and Genetics
Regression Analysis

2. Regression analysis is used for modeling the relationship between a single variable
Y, called the response, output or dependent variable, (Effect) and one or more
predictor, input, independent or explanatory variables, X1, X2,…., Xp. (Cause)
When p=1, it is called simple regression
Y = β 0 + β1 X 1 + e
and when p> 1 it is called multiple regression.
The functional form of the multiple linear regression model is
Y = β 0 + β1 X 1 + β 2 X 2 +.. + β p X p + e
where p is the number of the so-called "independent" variables, or "regressors“ and
is the random error.
The statistical technique of estimating or predicting the unknown value of a
dependent variable from the known value of the independent variable is called
regression analysis.
Regression Analysis

3. Assumptions of linear regression
 Normality: the values of the dependent variable are normally distributed for any
value of the independent variable. Normality of errors following normal
distribution with mean zero and variance σ 2
 Linearity: a linear relationship between the dependent and independent variable.
 Independence: the observations are randomly and independently selected.
 Homoscedasticity: the variation in the values of the dependent variable is the
same (equal) for any value of the independent variable
 There is no multicollinearity between the independent variables or no exact
correlation between the independent variable.

4. Linear Regression
Linear regression line is one which gives the best estimate or predict dependent variable (Y)
for any given value of the independent variable (X).
Regression Line of Y on X1
βo is the intercept which the regression line makes with the Y-axis,
β1is the slope of the line i.e. regression coefficient Y on X1
(represents the increase or decrease in the value of Y variable corresponding to
the unit increase in the value of X-variable)
and ei are the random error i.e. the effect of some unknown factors.
Least Square Estimates
The values of βo and β1 are estimated by the method of least squares such that the sum of squares
of the deviation of observed value of the dependent variable from the corresponding estimated
value based on regression function is minimum.
1
1 0 1 1
2
1
ˆ ˆ ˆ
,
yx
Y X
x
  
  


0 1 1
Y X e
 
  
imum
e
Y
Y i
i
i min
2
2
^








 


5. Least Square Estimates
The values of βo and β1 are estimated by the method of
least squares such that the residual/errors sum of squares
is minimum
0 1 1
Y X e
 
  

6.  







 1
1
^
2
^
yx
Y
Y i 

 






 2
2
^
i
i
i e
Y
Y
  
 
 2
2
y
Y
Yi

7. Testing the Significance of Overall Regression
Sources of
variation
df SS MS F-ratio
Regression 1 SSReg(1) MSR=SSReg/1 F=MSR/MSE
~F1,(n-2)
Error n-2 SSE = MSE=SSE/(n-2)
Total n-1 TSS =
1 1
ˆ yx

 
2
y

2
e

 There are two alternative method to test this hypothesis:
ANOVA
0
1
: Overall Regression is not significant
: Overall Regression is significant
H
H
^
2
u


8. Testing the Significance of Overall Regression
Coefficient of Determination (R2)
 
1
1 1
2
. 2
ˆ
Re 1
Y X
yx
SS g
R
TSS y

 


0
1
: Overall Regression is not significant
: Overall Regression is significant
H
H
 
2
, 1
2
1 1
p n p
R p
F F
R n p
 

  
 
2
1, 2
2
1
1 2
n
R
F F
R n


 

9. Test of Significance of Regression Coefficients
.
 
   
 
 
1
2
1
1 1
2
1 2
1
2 2
1 1
2
ˆ 0
ˆ
ˆ ˆ
Where
1
ˆ ˆ
ˆ Re 1
ˆ
2 2 2
n
u
u
t t
SE
SE V
V
x
e y yx TSS SS g
n n n


 
 





 
 
  
 
 
 
  
  

  
0
:
0
:
1
1
1
0




H
H Regression coefficient. is not significant i.e. No linear relationship
Regression coefficient. is significant i.e. linear relationship exist

11. S.No.
Hours
spent
outdoors
(X1) Y
y
=Y-Y
x1=
X1-X1 x12 y
2 yx1
Predicted
value of Y
ei
Residual
/Error
ei2
Residual
SS/ESS
1 5 59 -4 -5 25 16 20 50.07 8.93 79.74
2 9 53 -10 -1 1 100 10 60.41 -7.41 54.97
3 6 58 -5 -4 16 25 20 52.66 5.34 28.56
4 14 70 7 4 16 49 28 73.34 -3.34 11.18
5 10 66 3 0 0 9 0 63.00 3.00 9.00
6 8 53 -10 -2 4 100 20 57.83 -4.83 23.31
7 13 56 -7 3 9 49 -21 70.76 -14.76 217.80
8 12 71 8 2 4 64 16 68.17 2.83 8.00
9 6 50 -13 -4 16 169 52 52.66 -2.66 7.05
10 17 94 31 7 49 961 217 81.10 12.90 166.36
Sum 100 630 0 0 140 1542 362 0 605.97
Avera
ge 10 63
1
1 0 1 1
2
1
ˆ ˆ ˆ
,
yx
Y X
x
  
  


14
.
37
10
586
.
2
63
586
.
2
140
362
0
1








^
12
^
1
0
1
.
68
12
58
.
2
14
.
37
586
.
2
14
.
37









X
Y
X
Y
X
Y 


14. Example: Personal exposure to pollutants is influenced by various outdoor and indoor sources.
The aim of this study was to evaluate the exposure of the citizens to toluene. This variation
among monitoring campaigns might largely be explained by differences in climate
parameters, namely wind speed, humidity and amount of sunlight. Passive air samplers were
used to monitor volunteers, their homes and various urban sites for ten days, excluding
exposure from active smoking. For selected three variables i.e. Y = toluene personal
exposure concentration- widespread aromatic hydrocarbon (µg/m3); X1 = hours spent
outdoors; X2 = toluene home levels (µg/m3) the data are given below:
(a) Fit the model Y  0  1X1  2 X 2  e ?
(b) Test H0  i  0 vs H1  i  0 for i  1,2
(c) Measure of the overall strength of the linear relationship and tests its significance.
(d) Compare the explained variability of the full model with that of the reduced model ie.when
X1 is only in the regression.
S. No 1 2 3 4 5 6 7 8 9 10
(Y) 59 53 58 70 66 53 56 71 50 91
(X1) 5 9 6 14 9 7 13 11 5 17
(X2) 31 35 35 34 40 50 36 34 45 42

15. Fitting of Multiple Regression
.
     
    
     
    
2
1 2 2 1 2
1 2
2 2
1 2 1 2
2
2 1 1 1 2
2 2
2 2
1 2 1 2
0 1 1 2 2
ˆ ,
ˆ
ˆ ˆ ˆ
yx x yx x x
x x x x
yx x yx x x
x x x x
Y X X


  






  
   
  
   
  
0 1 1 2 2
Y X X e
  
   

16. Test of Significance of Regression Coefficients
. 0 1
1 1
: 0
: 0
H
H




 
   
 
    
 
1
1 3
1
1 1
2
2
2
1 2
2 2
1 2 1 2
2 2
1 1 2 2
2
ˆ 0
ˆ
ˆ ˆ
Where
ˆ ˆ
ˆ ˆ Re 2
ˆ
3 3 3
n
u
u
t t
SE
SE V
x
V
x x x x
e y yx yx TSS SS g
n n n


 
 
 




 
 
 

 

 
  
  
  

  
   
Regression coefficient. is not significant i.e. No linear relationship
Regression coefficient. is significant i.e. linear relationship exist

17. Test of Significance of Regression Coefficients
0 2
1 2
: 0
: 0
H
H




 
   
 
    
 
2
2 3
2
2 2
2
1
2
2 2
2 2
1 2 1 2
2 2
1 1 2 2
2
ˆ 0
ˆ
ˆ ˆ
Where
ˆ ˆ
ˆ ˆ Re 2
ˆ
3 3 3
n
u
u
t t
SE
SE V
x
V
x x x x
e y yx yx TSS SS g
n n n


 
 
 




 
 
 

 

 
  
  
  

  
   
Regression coefficient. is significant i.e. linear relationship exist
Regression coefficient. is not significant i.e. No linear relationship

18. Sources of
variation
df SS MS F-ratio
Regression 2 SSReg(2)= MSR=SSReg/2 F=MSR/MSE
~F2,(n-3)
Error n-3 SSE = MSE=SSE/(n-3)
Total n-1 TSS =
1 1 2 2
ˆ ˆ
yx yx
 

 
2
y

2
e

0
1
:Overall Regression is not significant
:Overall Regression is significant
H
H
Testing the Significance of Overall Regression
There are two alternative method to test this hypothesis:
ANOVA
2
^
u


19. Testing the Significance of Overall Regression
• There are two alternative method to test this hypothesis:
2.
 
1 2
1 1 2 2
2
. 2
ˆ ˆ
Re 2
Y X X
yx yx
SS g
R
TSS y
 

 
 

0
1
: Overall Regression is not significant
: Overall Regression is significant
H
H
 
2
, 1
2
1 1
p n p
R p
F F
R n p
 

  
 
2
2, 3
2
2
1 3
n
R
F F
R n


 

20. Improvement With the additional variable
0 2
1 2
: New variable has not improved the Regression
: New variable has improved the Regression
H X
H X
Sources of
variation
df SS MS F-ratio
m=1 SSReg(1)
p=2 SSReg(2)
p-m=1 SSReg(2)-SSReg(1) MSReg F=MSR/MSE(2)
~F1,(n-3)
Error n-3 SSE(2) MSE(2)
Total n-1 TSS
1
X
1 2
,
X X
2 1
/
X X

23. Residuals
A linear regression model is not always appropriate for the data. You can assess the
appropriateness of the model by examining residuals and outliers.
Residuals
The difference between the observed value of the dependent variable (y) and the predicted
value (ŷ) is called the residual (e). Each data point has one residual.
Residual = Observed value - Predicted value
e = y - ŷ
Both the sum and the mean of the residuals are equal to zero. That is, Σ e = 0 and e = 0.
Residual Plots
A residual plot is a graph that shows the residuals on the vertical axis and the predicted
value of Y on the horizontal axis. If the points in a residual plot are randomly dispersed
around the horizontal axis, a linear regression model is appropriate for the data; otherwise, a
non-linear model is more appropriate.
The residual plots show three typical patterns. The first plot shows a random pattern,
indicating a good fit for a linear model. The other plot patterns are non-random (U-shaped
and inverted U), suggesting a better fit for a non-linear model.
Random Pattern

24. Quadratic Regression
i
i
i X
X
Y e


 


 2
1
2
1
1
0
β0= Y intercept
β1= linear effect on Y
β0= curvilinear effect on Y
εi= random error in Y for ith obsevation
The maximum value of quadratic curve occurs at the function ^
2
^
1
)
2
( 



X
2
1
2 X
X 

25. Quadratic Regression
^
2
^
1
)
2
( 



X

26. Fertilizer
(X) Yield (Y)
20 60
25 100
30 128
35 145
45 160
55 170
60 160
65 140
70 150
y = 1.4x + 71.77
R² = 0.544
0
20
40
60
80
100
120
140
160
180
0 20 40 60 80
Yield (Y)
Yield (Y)
Linear (Yield (Y))
y = -0.097x2 + 10.20x - 96.95
R² = 0.950
0
20
40
60
80
100
120
140
160
180
0 20 40 60 80
Yield (Y)
Yield (Y)
Poly. (Yield (Y))
Practical 9(a)
58
.
52
)
097
.
0
2
(
20
.
10
)
2
(
^
2
^
1 








X