Statistics
Correlation and regression
Introduction
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Some methods involve one variable
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is Treatment A as effective in relieving arthritic
pain as Treatment B?
Correlation and regression used to
investigate relationships between variables
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most commonly linear relationships
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between two variables
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is BMD related to dietary calcium level?
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Contents
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Coefficients of correlation
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meaning
values
role
significance
Regression
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line of best fit
prediction
significance
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Introduction
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Correlation
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Regression analysis
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the strength of the linear relationship between two
variables
determines the nature of the relationship
Is there a relationship between the number of
units of alcohol consumed and the likelihood of
developing cirrhosis of the liver?
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Pearson’s coefficient of correlation
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r
Measures the strength of the linear relationship
between one dependent and one independent
variable
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curvilinear relationships need other techniques
Values lie between +1 and -1
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perfect positive correlation r = +1
perfect negative correlation r = -1
no linear relationship r = 0
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r = +1
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Pearson’s coefficient of correlation
r = -1
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r=0
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r = 0.6
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Scatter plot
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BMD
dependent variable
make inferences about
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Calcium intake
independent variable
make inferences from
controlled in some cases
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Non-Normal data
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Normalised
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Calculating r
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The value and significance of r are calculated by
SPSS
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SPSS output: scatter plot
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SPSS output: correlations
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Interpreting correlation
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Large r does not necessarily imply:
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strong correlation
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r increases with sample size
cause and effect
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strong correlation between the number of
televisions sold and the number of cases of
paranoid schizophrenia
watching TV causes paranoid schizophrenia
may be due to indirect relationship
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Interpreting correlation
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Variation in dependent variable due to:
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relationship with independent variable: r2
random factors: 1 - r2
r2 is the Coefficient of Determination
e.g. r = 0.661
r2 = = 0.44
less than half of the variation in the dependent
variable due to independent variable
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Agreement
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Correlation should never be used to determine
the level of agreement between repeated
measures:
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measuring devices
users
techniques
It measures the degree of linear relationship
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1, 2, 3 and 2, 4, 6 are perfectly positively correlated
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Assumptions
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Errors are differences of predicted values of Y
from actual values
To ascribe significance to r:
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distribution of errors is Normal
variance is same for all values of independent
variable X
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Non-parametric correlation
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Make no assumptions
Carried out on ranks
Spearman’s r
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Kendall’s t
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easy to calculate
has some advantages over r
distribution has better statistical properties
easier to identify concordant / discordant pairs
Usually both lead to same conclusions
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Calculation of value and significance
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Computer does it!
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Role of regression
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Shows how one variable changes with another
By determining the line of best fit
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linear
curvilinear
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Line of best fit
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Simplest case linear
Line of best fit between:
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dependent variable Y
 BMD
independent variable X
 dietary intake of Calcium
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Y = a + bX
value of Y when X=0 change in Y when X increases by 1
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Role of regression
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Used to predict
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the value of the dependent variable
when value of independent variable(s) known
within the range of the known data
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extrapolation risky!
relation between age and bone age
Does not imply causality
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SPSS output: regression
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Assumptions
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Only if statistical inferences are to be made
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significance of regression
values of slope and intercept
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Assumptions
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If values of independent variable are randomly
chosen then no further assumptions necessary
Otherwise
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as in correlation, assumptions based on errors
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balance out (mean=0)
variances equal for all values of independent variable
not related to magnitude of independent variable
seek advice / help
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Multivariate regression
 More than one independent variable
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BMD dependent on:
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age
gender
calorific intake
etc
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Logistic regression
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The dependent variable is binary
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yes / no
predict whether a patient with Type 1 diabetes
will undergo limb amputation given history of
prior ulcer, time diabetic etc
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result is a probability
Can be extended to more than two
categories
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Outcome after treatment
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recovered, in remission, died
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Summary
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Correlation
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strength of linear relationship between two variables
Pearson’s - parametric
Spearman’s / Kendalls non-parametric
Interpret with care!
Regression
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line of best fit
prediction
multivariate
logistic
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