Transcript Slide 1

Srinivasulu Rajendran
Centre for the Study of Regional Development (CSRD)
Jawaharlal Nehru University (JNU)
New Delhi
India
[email protected]
Objective of the session
To understand How
HHsize influences the
monthly per capita
total expenditure of the
households based OLS
1. What is the procedure to
perform Regression?
2. How do we interpret results?
4. What are procedure available
for estimating poverty line and
Poverty rate and how to do with
Econometric software
Identify the relationship between variables that
we want to perform Scatter plot for outliers and
type of relationship
 Monthly HH food Expenditure and HHSIZE
Linear Regression Analysis using
SPSS
Objectives
 Regression analysis is
the next step up after
correlation; it is used when we want to predict the
value of a variable based on the value of another
variable. In this case, the variable we are using to
predict the other variable's value is called the
independent variable or sometimes the predictor
variable. The variable we are wishing to predict is
called the dependent variable or sometimes the
outcome variable.
Assumption
 Variables are approximately normally distributed
(see Testing for Normality guide).
 There is a linear relationship between the two
variables.
 There are classical assumption ……..
Step 1
Procedure
1.Click Analyze > Regression > Linear... on the top menu.
You will be presented with the following dialog box:
Step 2
 Transfer
the
independent
(predictor) variable,
hhsize, into the
"Independent(s):"
box
and
the
dependent
(outcome) variable,
mfx,
into
the
"Dependent:" box.
You can do this by
either
drag-anddropping or by
using
the
buttons.
 Click
button.
the
Dependent
Variable
Independent Vari
Step 2
Extra options
 Click
“Statistics”
and it provides
Regression
coefficients,
depends on your
analysis you may
select your relevant
test
 Finally
click
“Continue”
Plot - Options
 Click “Plot” and it
provides option to
plot
histogram,
normal probability,
etc, depends on
your analysis you
may select your
relevant plot
 Finally
click
“Continue”
Click
“OK”
to get results
in the output
viewer
Output of Linear Regression
Analysis
 SPSS will generate quite a
few tables in its results
section
for
a
linear
regression.
 In this session, we are going
to look at the important
tables Model Summary
table.
 This table provides the R
and R2 value. The R value is
0.608, which represents the
simple correlation and,
therefore, indicates a high
degree of correlation. The
R2 value indicates how
much of the dependent
variable, monthly HH food
exp, can be explained by the
independent
variable,
hhsize. In this case, 37.0%
can be explained.
Model Summary
Model
1
R
a
.608
Adjusted R Std. Error of
R Square
Square
the Estimate
.370
.370
2157.08
 The next table is the
ANOVA table.
 This table indicates that
the regression model
predicts the outcome
variable
significantly
well. How do we know
this? Look at the
"Regression" row and go
to the Sig. column.
 This
indicates
the
statistical significance of
the regression model
that was applied. Here,
P < 0.0005 which is less
than 0.05 and indicates
that, overall, the model
applied is significantly
good
enough
in
predicting the outcome
variable.
ANOVAb
Model
1
Mean
Sum of Squares df
Square
F
Regressio
3378640742.5 1.0 3378640742 726.116
n
.5
Residual
5746495913.9 123
5.0
Total
9125136656.4 123
6.0
4653033.1
Sig.
a
.000
 The
table below, Coefficients, provides us with
information on each predictor variable.
 This provides us with the information necessary to predict
monthly food exp from hhsize. We can see that both the
constant and hhsize contribute significantly to the model
(by looking at the Sig. column). By looking at the B
column under the Unstandardized Coefficients column
we can present the regression equation as
 mfx = 669.3+ 861.7(hhsize)
Coefficientsa
Standardiz
ed
Coefficient
Unstandardized Coefficients
s
Model
1
Std. Error
151.807
Beta
(Constant)
B
669.294
t
4.409
Sig.
.000
Household size
861.655
31.976
.608
26.947 .000
Interpretation
 If HHSIZE goes up by a member or individual, the average
monthly HH food expenditure (mfx) goes up by about 862
taka. The intercept value of about 669 taka tells us that if
hhsize were zero, mfx would be about 669 taka. The r 2
value of 0.37 means approximately 37 percent
 of the variation in the mfx is explained by variation
 in the hhsize.
Coefficientsa
Standardiz
ed
Coefficient
Unstandardized Coefficients
s
Model
1
Std. Error
151.807
Beta
(Constant)
B
669.294
t
4.409
Sig.
.000
Household size
861.655
31.976
.608
26.947 .000