Statistics Correlation and regression. 2 Introduction Some methods involve one variable is...
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Transcript of Statistics Correlation and regression. 2 Introduction Some methods involve one variable is...
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Statistics
Correlation and regression
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Introduction Some methods involve one variable
is Treatment A as effective in relieving arthritic pain as Treatment B?
Correlation and regression used to investigate relationships between variables most commonly linear relationships between two variables
is BMD related to dietary calcium level?
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Contents
Coefficients of correlation meaning values role significance
Regression line of best fit prediction significance
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Introduction Correlation
the strength of the linear relationship between two variables
Regression analysis 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 r Measures the strength of the linear
relationship between one dependent and one independent variable curvilinear relationships need other techniques
Values lie between +1 and -1 perfect positive correlation r = +1 perfect negative correlation r = -1 no linear relationship r = 0
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Pearson’s coefficient of correlation
r = +1
r = -1
r = 0.6
r = 0
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Scatter plot
dependent variablemake inferences
about
independent variable
controlled in some cases
Calcium intake
BMD
make inferences from
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Non-Normal data
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Normalised
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Calculating r 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 correlationLarge r does not necessarily imply:
strong correlation r increases with sample size
cause and effectstrong 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 Variation in dependent variable due
to: 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 Correlation should never be used to
determine the level of agreement between repeated measures: measuring devices users techniques
It measures the degree of linear relationship 1, 2, 3 and 2, 4, 6 are perfectly positively
correlated
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Assumptions Errors are differences of predicted values of Y
from actual values To ascribe significance to r:
distribution of errors is Normal variance is same for all values of independent
variable X
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Non-parametric correlation Make no assumptions Carried out on ranks Spearman’s
easy to calculate Kendall’s
has some advantages over 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
Computer does it!
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Role of regression Shows how one variable changes with
another By determining the line of best fit
linear curvilinear
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Line of best fit Simplest case linear Line of best fit between:
dependent variable Y BMD
independent variable X dietary intake of Calcium
value of Y when X=0
Y = a + bX
change in Y when X increases by 1
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Role of regression Used to predict
the value of the dependent variable when value of independent variable(s)
known within the range of the known data
extrapolation risky! relation between age and bone age
Does not imply causality
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SPSS output: regression
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Assumptions
Only if statistical inferences are to be made significance of regression values of slope and intercept
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Assumptions If values of independent variable are randomly
chosen then no further assumptions necessary Otherwise
as in correlation, assumptions based on errors 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 regressionMore than one independent
variable BMD dependent on:
agegendercalorific intakeetc
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Logistic regression The dependent variable is binary
yes / no predict whether a patient with Type 1
diabetes will undergo limb amputation given history of prior ulcer, time diabetic etc result is a probability
Can be extended to more than two categories Outcome after treatment
recovered, in remission, died
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Summary Correlation
strength of linear relationship between two variables
Pearson’s - parametric Spearman’s / Kendalls non-parametric Interpret with care!
Regression line of best fit prediction multivariate logistic