Transcript Advantages of Multivariate Analysis

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Advantages of
Multivariate
Analysis
Close resemblance to how the researcher thinks.
Easy visualisation and interpretation of data.
More information is analysed simultaneously,
giving greater power.
Relationship between variables is understood
better.
Focus shifts from individual factors taken singly
to relationship among variables.
Definitions - I
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Independent (or Explanatory or Predictor) variable
always on the X axis.
Dependent (or Outcome or Response) variable always
on the Y axis.
In OBSERVATIONAL studies researcher observes the
effects of explanatory variables on outcome.
In INTERVENTION studies researcher manipulates
explanatory variable (e.g. dose of drug) to influence
outcome
Definitions - II
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Scatter plot helps to
visualise the relationship
between two variables.
The figure shows a
scatter plot with a
regression line. For a
given value of X there is
a spread of Y values.
The regression line
represents the mean
values of Y.
Scatterplot of deuterium against testweighing
Deuterium = -67.3413 + 1.16186 Test weigh
S = 234.234
R-Sq = 59.3 %
R-Sq(adj) = 56.0 %
2500
Deuterium
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2000
1500
Regression
95% CI
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Test weigh
2000
Definitions - III
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INTERCEPT is the
value of Y for X = 0. It
denotes the point where
the regression line meets
the Y axis
SLOPE is a measure of
the change in the value
of Y for a unit change in
X.
Y axis
Slope
Intercept
X axis
Basic Assumptions
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Y increases or decreases linearly with increase
or decrease in X.
For any given value of X the values of Y are
distributed Normally.
Variance of Y at any given value of X is the
same for all value of X.
The deviations in any one value of Y has no
effect on other values of Y for any given X
The Residuals
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The difference between the
observed value of Y and the
value on the regression line
(Fitted value) is the residual.
The statistical programme
minimizes the sum of the
squares of the residuals. In a
Good Fit the data points are
all crowded around the
regression line.
Residual
Analysis of Variance - I
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The variation of Y values around the regression
line is a measure of how X and Y relate to each
other.
Method of quantifying the variation is by
Analysis of variance presented as Analysis of
Variance table
Total sum of squares represents total variation
of Y values around their mean - Syy
Analysis of Variance - II
Total Sum of Squares ( Syy ) is made up of two parts:
(i). Explained by the regression
(ii). Residual Sum of Squares
Sum of Squares ÷ its degree of freedom = Mean Sum of Squares
(MSS)
The ratio MSS due to regression ÷ MSS Residual = F ratio
Reading the output
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Regression Equation
Residual Sum of Squares (RSS)
Values of α and β.
R2
S (standard deviation)
Testing for β ≠ 0
Analysis of Variance Table
F test
Outliers
Remote from the rest