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## Correlation - California State University, Northridge

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Cal State Northridge427Ainsworth
Correlation and Regression
Major Points - Correlation
Questions answered by correlationScatterplotsAn exampleThe correlation coefficientOther kinds of correlationsFactors affecting correlationsTesting for significance
The Question
Are two variables related?Does one increase as the other increases?e. g. skills and incomeDoes one decrease as the other increases?e. g. health problems and nutritionHow can we get a numerical measure of the degree of relationship?
Scatterplots
AKA scatter diagram or scattergram.Graphically depicts the relationship between two variables in two dimensional space.
Direct Relationship
Inverse Relationship
An Example
Does smoking cigarettes increase systolic blood pressure?Plotting number of cigarettes smoked per day against systolic blood pressureFairly moderate relationshipRelationship is positive
Trend?
Smoking and BP
Note relationship is moderate, but real.Why do we care about relationship?What would conclude if there were no relationship?What if the relationship were near perfect?What if the relationship were negative?
Heart Disease and Cigarettes
Data on heart disease and cigarette smoking in 21 developed countries (Landwehr and Watkins, 1987)Data have been rounded for computational convenience.The results were not affected.
The Data
Surprisingly, the U.S. is the first country on the list--the countrywith the highest consumption and highest mortality.
Scatterplot of Heart Disease
CHD Mortality goes on ordinate (Y axis)Why?Cigarette consumption on abscissa (X axis)Why?What does each dot represent?Best fitting line included for clarity
{X=6, Y= 11}
What Does the Scatterplot Show?
As smoking increases, so does coronary heart disease mortality.Relationship looks strongNot all data points on line.This gives us “residuals” or “errors of prediction”To be discussed later
Correlation
Co-relationThe relationship between two variablesMeasured with a correlation coefficientMost popularly seen correlation coefficient: Pearson Product-Moment Correlation
Types of Correlation
Positive correlationHigh values of X tend to be associated with high values of Y.As X increases, Y increasesNegative correlationHigh values of X tend to be associated with low values of Y.As X increases, Y decreasesNo correlationNo consistent tendency for values on Y to increase or decrease as X increases
Correlation Coefficient
A measure of degree of relationship.Between 1 and -1Sign refers to direction.Based on covarianceMeasure of degree to which large scores on X go with large scores on Y, and small scores on X go with small scores on YThink of it as variance, but with 2 variables instead of 1 (What does that mean??)
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Covariance
Remember that variance is:The formula for co-variance is:How this works, and why?When wouldcovXYbe large and positive? Large and negative?
Example
Example
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What the heck is a covariance?I thought we were talking about correlation?
Correlation Coefficient
Pearson’s Product Moment CorrelationSymbolized byrCovariance ÷ (product of the 2 SDs)Correlation is a standardized covariance
Calculation for Example
CovXY= 11.12sX= 2.33sY= 6.69
Example
Correlation = .713Sign is positiveWhy?If sign were negativeWhat would it mean?Would not alter thedegreeof relationship.
Other calculations
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Z-score methodComputational (Raw Score) Method
Other Kinds of Correlation
Spearman Rank-Order Correlation Coefficient (rsp)used with 2 ranked/ordinal variablesuses the same Pearson formula
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Other Kinds of Correlation
Point biserial correlation coefficient (rpb)used with one continuous scale and one nominal or ordinal or dichotomous scale.uses the same Pearson formula
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Other Kinds of Correlation
Phi coefficient ()used with two dichotomous scales.uses the same Pearson formula
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Factors Affectingr
Range restrictionsLooking at only a small portion of the total scatter plot (looking at a smaller portion of the scores’ variability)decreasesr.Reducing variability reducesrNonlinearityThe Pearson r (and its relatives) measure the degree oflinearrelationship between two variablesIf a strong non-linear relationship exists, r will provide a low, or at least inaccurate measure of the true relationship.
Factors Affectingr
Heterogeneous subsamplesEveryday examples (e.g. height and weight using both men and women)OutliersOverestimate CorrelationUnderestimate Correlation
Countries With Low Consumptions
Data With Restricted Range
Truncated at 5 Cigarettes Per Day
Cigarette Consumption per Adult per Day
5.5
5.0
4.5
4.0
3.5
3.0
2.5
CHD Mortality per 10,000
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14
12
10
8
6
4
2
Truncation
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Non-linearity
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Heterogenous samples
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Outliers
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Testing Correlations
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So you have a correlation. Now what?In terms of magnitude, how big is big?Small correlations in large samples are “big.”Large correlations in small samples aren’t always “big.”Depends upon the magnitude of the correlation coefficientANDThe size of your sample.
Testingr
Population parameter =Null hypothesisH0:  = 0Test of linear independenceWhat would a true null mean here?What would a false null mean here?Alternative hypothesis (H1)   0Two-tailed
Tables of Significance
We can convert r to t and test for significance:Where DF = N-2
Tables of Significance
In our examplerwas .71N-2 = 21 – 2 = 19T-crit(19) = 2.09Since 6.90 is larger than 2.09 rejectr= 0.
Computer Printout
Printout gives test of significance.
Regression
What is regression?
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How do we predict one variable from another?How does one variable change as the other changes?Influence
Linear Regression
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A technique we use to predict the most likely score on one variable from those on another variableUses thenature of the relationship(i.e. correlation)between two variables toenhanceyour prediction
Linear Regression: Parts
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Y- the variables you are predictingi.e. dependent variableX- the variables you are using to predicti.e. independent variable- your predictions (also known asY’)
Why Do We Care?
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We may want to make a prediction.More likely, we want to understand the relationship.How fast does CHD mortality rise with a one unit increase in smoking?Note: we speak about predicting, but often don’t actually predict.
An Example
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Cigarettes and CHD Mortality againData repeated on next slideWe want to predict level of CHD mortality in a country averaging 10 cigarettes per day.
The Data
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Based on the data we have what would we predict the rate of CHD be in a country that smoked 10 cigarettes on average?First, we need to establish a prediction of CHD from smoking…
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For a country that smokes 6 C/A/D…
We predict a CHD rate of about 14
Regression Line
Regression Line
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Formula= the predicted value ofY(e.g. CHD mortality)X= the predictor variable (e.g. average cig./adult/country)
Regression Coefficients
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“Coefficients” areaandbb= slopeChange in predictedYfor one unit change inXa= interceptvalue of whenX= 0
Calculation
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SlopeIntercept
For Our Data
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CovXY= 11.12s2X= 2.332= 5.447b= 11.12/5.447 = 2.042a= 14.524 - 2.042*5.952 = 2.32See SPSS printout on next slide
Answers are not exact due to rounding error and desire to match SPSS.
SPSS Printout
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Note:
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The values we obtained are shown on printout.The intercept is the value in theBcolumn labeled “constant”The slope is the value in theBcolumn labeled by name of predictor variable.
Making a Prediction
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Second, once we know the relationship we can predictWe predict 22.77 people/10,000 in a country with an average of 10 C/A/D will die of CHD
Accuracy of Prediction
Finnish smokers smoke 6 C/A/DWe predict:They actually have 23 deaths/10,000Our error (“residual”) =23 - 14.619 = 8.38a large error
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Cigarette Consumption per Adult per Day
12
10
8
6
4
2
CHD Mortality per 10,000
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20
10
0
Residuals
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When we predict Ŷ for a given X, we will sometimes be in error.Y – Ŷ for any X is a anerror of estimateAlso known as: aresidualWe want to Σ(Y- Ŷ) as small as possible.BUT, there are infinitely many lines that can do this.Just draw ANY line that goes through the mean of the X and Y values.Minimize Errors of Estimate… How?
Minimizing Residuals
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Again, the problem lies with this definition of the mean:So, how do we get rid of the 0’s?Square them.
Regression Line:A Mathematical Definition
The regression line is the line which when drawn through your data set produces the smallest value of:Called the Sum of Squared Residual or SSresidualRegression line is also called a “least squares line.”
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Summarizing Errors of Prediction
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Residual varianceThe variability of predicted values
Standard Error of Estimate
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Standard error of estimateThe standard deviation of predicted valuesA common measure of the accuracy of our predictionsWe want it to be as small as possible.
Example
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Regression and Z Scores
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When your data are standardized (linearly transformed to z-scores), the slope of the regression line is called βDO NOT confuse this β with the β associated with type II errors. They’re different.When we have one predictor, r = βZy= βZx, since A now equals 0
Sums of square deviationsTotalRegressionResidual we already coveredSStotal=SSregression+SSresidual
Partitioning Variability
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Partitioning Variability
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Degrees of freedomTotaldftotal= N - 1Regressiondfregression= number of predictorsResidualdfresidual=dftotal–dfregressiondftotal=dfregression+dfresidual
Partitioning Variability
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Variance (or Mean Square)Total Variances2total=SStotal/ dftotalRegression Variances2regression=SSregression/ dfregressionResidual Variances2residual= SSresidual/ dfresidual
Example
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Example
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Coefficient of Determination
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It is a measure of the percent of predictable variabilityThe percentage of the total variability in Y explained by X
r= .713r2= .7132=.508orApproximately 50% in variability of incidence of CHD mortality is associated with variability in smoking.
r2for our example
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Coefficient of Alienation
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It is defined as 1 -r2orExample1 - .508 = .492
r2, SS and sY-Y’
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r2* SStotal= SSregression(1 - r2) * SStotal= SSresidualWe can also use r2to calculate the standard error of estimate as:
Testing Overall Model
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We can test for the overall prediction of the model by forming the ratio:If the calculated F value is larger than a tabled value (F-Table) we have a significant prediction
Testing Overall Model
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ExampleF-Table – F critical is found using 2 thingsdfregression(numerator) anddfresidual.(demoninator)F-Table ourFcrit(1,19) = 4.3819.594 > 4.38, significant overallShould all sound familiar…
SPSS output
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Testing Slope and Intercept
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The regression coefficients can be tested for significanceEach coefficient divided by it’s standard error equals a t value that can also be looked up in a t-tableEach coefficient is tested against 0
Testing the Slope
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With only 1 predictor, the standard error for the slope is:For our Example:
Testing Slope and Intercept
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These are given in computer printout as attest.
Testing
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Thetvalues in the second from right column are tests on slope and intercept.The associatedpvalues are next to them.The slope is significantly different from zero, but not the intercept.Why do we care?
Testing
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What does it mean if slope is not significant?How does that relate to test onr?What if the intercept is not significant?Does significant slope mean we predict quite well?

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Correlation - California State University, Northridge