Multi

2. Presentation Topic
Multivariate
Regression Analysis

3. Learning Objectives
– Introduction To Multivariate Regression Analysis.
– Multivariate Least Squares Estimation.
– Test of Overall Regression

4. Multivariate Analysis
Multivariate analysis (MVA) is based on the
statistical principle of multivariate statistics,
which involves observation and analysis of
more than one variable at the same time.

5. Regression
A statistical process for estimating the
relationship among dependent and independent
variables.

6. Univariate Simple Linear Regression
In statistics, linear regression is an approach for
modeling the relationship between a scalar
dependent variable y and one independent.
Y = βo + β1X +e
(Simple Linear Regression)

7. Multiple Linear Regression
Multiple linear regression is an approach for
modeling the relationship between 1 dependent
variable y and 2 or more explanatory variables.
Y = βo + β1X1 +β2X2+β3X3+e
(Multiple Regression)

8. Multivariate Multiple Regression
Multivariate regression is an approach for
modeling the relationship between several
dependent variables y’s and several independent
variables x’s.
𝑦 = 𝛽 𝑜 + 𝛽1 𝑋 + 𝜀
(Multivariate multiple Regression)

9. Multivariate Multiple Regression
Where
𝑦 =
𝑦1
𝑦2
𝑦𝑛
, 𝑋 =
𝑥11 𝑥12 … … . 𝑥1𝑞
𝑥21 𝑥22 … … . 𝑥2𝑞
𝑥 𝑛1 𝑥 𝑛2 … … . 𝑥 𝑛𝑞
,
and 𝜀 =
𝜀1
𝜀2
𝜀 𝑛

10. Types Of Independent Variables
There are two basic types of independent
variables.
1. MULTIPLE REGRESSION: FIXED x’s
2. MULTIPLE REGRESSION: RANDOM x’s

11. 1-MULTIPLE REGRESSION: FIXED x’s
In some experimental situations, the x ’s are
fixed, that is, under the control of the
experimenter. For example, a researcher may
wish to relate yield per acre and nutritional
value to level of application of various chemical
fertilizers. The experimenter can choose the
amount of chemicals to be applied and then
observe the changes in the yield and nutritional
responses.

12. 2-MULTIPLE REGRESSION: RANDOM x’s
In some cases all x ’s are random variables and
are therefore not under the control of the
researcher. A person is chosen at random, and
all the y ’s and x ’s are measured, or observed,
for that person.

13. Model for Fixed x’s
In the fixed-x regression model, we express
each y in a sample of n observations as a linear
function of the x’s plus a random error, ε

14. The number of x’s is denoted by q .The β’s are
called regression coefficients. Additional
assumptions that accompany the equations of
the model are as follows:
E (εi) = 0 for all i = 1, 2,... ,n .
var(εi) = σ2 for all i = 1, 2,... ,n .
cov(εi,εj) = 0 for all i ≠ j .
Model for Fixed x’s

15. Matrix Notation
Using matrix notation, the models for the n
observations

16. Least Squares Estimation in the
Fixed- x Model
If 1st assumption hold,
E (εi) = 0 for all i = 1, 2,... ,n
We seek to estimate the β ’s

17. Least Squares Estimation in the
Fixed- x Model
The product X’y can be used to compute the
co-variances of the x ’s with y .
 The product X’X can be used to obtain the
covariance matrix of the x ’s, which includes
the variances and co-variances of the x ’s.

18. Test of Overall Regression
The overall regression hypothesis that none of
the x ’s predict y can be expressed as
We do not include βo = 0 in the hypothesis so as
not to restrictly to have an intercept of zero.

19. Test of Overall Regression
We can write SSE as,
To correctly for its mean and thereby avoid
inclusion of βo = 0, we subtract from both
sides of to obtain

20. Test of Overall Regression
Where
is the total sum of squares adjusted for the mean
and
is the overall regression sum of squares adjusted
for the intercept.

21. Test of Overall Regression
We can test by means of
Which is distributed as
We reject Ho if F >