T-tests, ANOVA and regression Methods for Dummies February 1 st 2006 Jon Roiser and Predrag...

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t-tests, ANOVA and regressiont-tests, ANOVA and regression

Methods for Dummies February 1Methods for Dummies February 1stst 2006 2006

Jon Roiser and Predrag PetrovicJon Roiser and Predrag Petrovic

OverviewOverview

Simple hypothesis testingSimple hypothesis testing Z-tests & t-testsZ-tests & t-tests F-tests and ANOVAF-tests and ANOVA Correlation/regressionCorrelation/regression

Central aim of statistical tests:Central aim of statistical tests: Determining the likelihood of a value in a Determining the likelihood of a value in a

sample, given that the Null Hypothesis is sample, given that the Null Hypothesis is truetrue: P(value|H: P(value|H00)) HH00: no statistically significant difference between : no statistically significant difference between

sample & population (or between samples)sample & population (or between samples) HH11: statistically significant difference between : statistically significant difference between

sample & population (or between samples)sample & population (or between samples)

Significance level: P(value|HSignificance level: P(value|H00) < 0.05) < 0.05

Starting pointStarting point

Types of errorTypes of error

True state of the worldTrue state of the world

HH0 0 truetrue HH0 0 falsefalse

DecisionDecision

Accept Accept HH00

Correct Correct acceptanceacceptance

p=1-p=1-

-error-error(Type (Type IIII error) error)False negativeFalse negative

Reject Reject HH00

-error-error(Type (Type II error) error)False positiveFalse positive

Correct rejection Correct rejection p=1-p=1-Power)Power)

TotalTotal

populationpopulation

PopulationPopulation studiedstudied

Distribution & probabilityDistribution & probability

If we know something about the distribution of events in a population, we know something about the probability of these events

Populationmean

Population standard deviation

Standardised normal distributionStandardised normal distribution

The z-The z-score representsscore represents a value on the x-axis for a value on the x-axis for which we know the p-valuewhich we know the p-value

2-tailed: z = 1.96 is equivalent to p=0.05 (rule 2-tailed: z = 1.96 is equivalent to p=0.05 (rule of thumb ~2SD)of thumb ~2SD)

1-tailed: z = 1.65 is equivalent to p=0.05 (area 1-tailed: z = 1.65 is equivalent to p=0.05 (area between infinity and 1.65=5% on one of the between infinity and 1.65=5% on one of the tails)tails)

1 point compared to population

Group compared to populationStandardised

Assumptions of parametric testsAssumptions of parametric tests

Variables are:Variables are: Normally distributedNormally distributed N>10 (or 12, or 15…)N>10 (or 12, or 15…) On an interval or ideally ratio scale (e.g. 2 On an interval or ideally ratio scale (e.g. 2

metres=2x1 metre)metres=2x1 metre)

……but parametric tests are (fairly) robust to but parametric tests are (fairly) robust to violations of these assumptionsviolations of these assumptions

Z- versus t- statistic?Z- versus t- statistic? Z is used when we know the Z is used when we know the

variance variance in the general in the general population population e.g. IQ. This is not e.g. IQ. This is not normally true!normally true!

t is used when we do not know t is used when we do not know the variance of the underlying the variance of the underlying population for sure, and is population for sure, and is dependent on Ndependent on N

The t distribution is similar to The t distribution is similar to the Z (but flatter)the Z (but flatter)

For N>30, t≈Z For N>30, t≈Z

Large N

Small N

Two-sample t-testTwo-sample t-test

ss xx Group 1 Group 2

Difference between the means divided by the pooled standard error of the mean

Different types of t-testDifferent types of t-test One-sampleOne-sample

Tests whether the mean of a population is different to a Tests whether the mean of a population is different to a given value (e.g. if chance performance=50% in 2 given value (e.g. if chance performance=50% in 2 alternative forced choice)alternative forced choice)

Paired t-test (within subjects)Paired t-test (within subjects) Tests whether a group of individuals tested under Tests whether a group of individuals tested under

condition A is different to tested under condition Bcondition A is different to tested under condition B Must have 2 values for each subjectMust have 2 values for each subject Basically the same as a one-sample t-test on the Basically the same as a one-sample t-test on the

difference scores, comparing the difference scores to 0difference scores, comparing the difference scores to 0

Another approach to group differencesAnother approach to group differences Instead of thinking about the group means, we can Instead of thinking about the group means, we can

instead think about variancesinstead think about variances Recall sample variance:Recall sample variance:

F=Variance 1/Variance 2F=Variance 1/Variance 2 ANOVA = ANANOVA = ANALYSISALYSIS O OFF VA VARIANCERIANCE

Total variance=model variance + error varianceTotal variance=model variance + error variance

Partitioning the variancePartitioning the variance

Group 1

Group 2

Group 1

Group 2

Group 1

Group 2

Total = Model +

(Between groups)

(Within groups)

ANOVAANOVA At its simplest, one-way ANOVA is the same as the two-At its simplest, one-way ANOVA is the same as the two-

sample t-testsample t-test Recall t=difference between means/spread around means Recall t=difference between means/spread around means

(pooled standard error of the mean)(pooled standard error of the mean)

Group 1

Group 2

Group 1

Group 2

Model (difference between means)

Between groups

Error (spread around means)

Within groups

A quick proof from SPSSA quick proof from SPSSGroup Statistics

15 3.1352 1.45306 .37518

11 1.7157 1.03059 .31073

group2Ecstasy

Control

DepressionN Mean Std. Deviation

Std. ErrorMean

Independent Samples Test

2.105 .160 2.764 24 .011 1.41951 .51363 .35943 2.47958

2.914 23.991 .008 1.41951 .48715 .41406 2.42496

Equal variancesassumed

Equal variancesnot assumed

DepressionF Sig.

Levene's Test forEquality of Variances

t df Sig. (2-tailed)Mean

DifferenceStd. ErrorDifference Lower Upper

95% ConfidenceInterval of the

Difference

t-test for Equality of Means

Depression

12.788 1 12.788 7.638 .011

40.181 24 1.674

52.968 25

Between Groups

Within Groups

Sum ofSquares df Mean Square F Sig. ANOVA

t-test

In fact, t=SQRT(F) => 2.764=SQRT(7.638) (for 1 degree of freedom)

ANOVA is useful for more complex designsANOVA is useful for more complex designs

Group 1 Group 2 Group 3

More than 1 group

Male Female

More than 1 effect (interaction)

● Drug

● Placebo

…but we need to use post-hoc tests (t-tests corrected for multiple comparisons) to interpret the results

Differences between t-tests and F-tests (especially in SPM)Differences between t-tests and F-tests (especially in SPM) t-tests can only be used to compare 2 t-tests can only be used to compare 2

groups/effects, while ANOVA can handle groups/effects, while ANOVA can handle more sophisticated designs (several more sophisticated designs (several groups/several effects/interactions)groups/several effects/interactions)

In SPM In SPM t-tests are one-tailed t-tests are one-tailed (i.e. for (i.e. for contrast X-Y, significant voxels are only contrast X-Y, significant voxels are only reported where X>Y)reported where X>Y)

In SPM In SPM F-tests are two-tailedF-tests are two-tailed (i.e. for (i.e. for contrast X-Y, significant voxels are contrast X-Y, significant voxels are reported for both X>Y and Y>X)reported for both X>Y and Y>X)

Correlation and RegressionCorrelation and Regression Is there a relationship between Is there a relationship between xx and and yy?? What is the strength of this relationshipWhat is the strength of this relationship

Pearson’s rPearson’s r Can we describe this relationship and use it to predict Can we describe this relationship and use it to predict yy

from from xx?? RegressionRegression

Fitting a line using the Least Squares solutionFitting a line using the Least Squares solution

Is the relationship we have described statistically Is the relationship we have described statistically significant? significant? Significance testsSignificance tests

Relevance to SPMRelevance to SPM GLMGLM

Is there a relationship between Is there a relationship between xx and and yy?? What is the strength of this relationshipWhat is the strength of this relationship

Correlation and RegressionCorrelation and Regression

Correlation: predictability about the relationship between Correlation: predictability about the relationship between two variablestwo variables

Covariance: measurement of this predictabilityCovariance: measurement of this predictability Regression: description about the relationship between two Regression: description about the relationship between two

variables where one is dependent and the other is variables where one is dependent and the other is independentindependent

No causality in any of these modelsNo causality in any of these models

When X and Y : cov (x,y) = pos.When X and Y : cov (x,y) = pos. When X and Y : cov (x,y) = neg.When X and Y : cov (x,y) = neg. When no consistent relationship: cov (x,y) = 0When no consistent relationship: cov (x,y) = 0 Dependent on size of the data’s standard deviations (Dependent on size of the data’s standard deviations (!!)) We need to standardize the data (We need to standardize the data (Pearson’s rPearson’s r))

))((),cov( 1

yyxxyx

CovarianceCovariance

Pearson’s rPearson’s r

yxxy ssn

))((),cov( 1

Pearson’s rPearson’s r

Covariance does not really tell us anythingCovariance does not really tell us anything

– Solution: standardise this measureSolution: standardise this measure

Pearson’s r: standardises the covariance valuePearson’s r: standardises the covariance value Divides the covariance by the multiplied standard Divides the covariance by the multiplied standard

deviations of X and Y: deviations of X and Y:

Best-fit lineBest-fit line

= ŷ, predicted value

Aim of linear regression is to fit a straight line, Aim of linear regression is to fit a straight line, ŷŷ = ax + b to data = ax + b to data that gives best prediction of y for any value of xthat gives best prediction of y for any value of x

This will be the line that minimises distance betweenThis will be the line that minimises distance between data and fitted line, i.e. the residualsdata and fitted line, i.e. the residuals

intercept

ŷ = ax + b

ε = residual error

= y i , true value

Least Squares RegressionLeast Squares Regression

To find the best line we must minimise the sum To find the best line we must minimise the sum of the squares of the residuals (the vertical of the squares of the residuals (the vertical distances from the data points to our line)distances from the data points to our line)

Residual (ε) = y - ŷ

Sum of squares (SS) of residuals = Σ (y – ŷ)2

Model line: ŷ = ax + b

We must find values of We must find values of a a and and bb that minimise that minimise

ΣΣ (y – (y – ŷŷ))22

a = slope, b = intercept

Finding bFinding b First we find the value of b that gives the least First we find the value of b that gives the least

sum of squaressum of squares

Trying different values of b is equivalent to Trying different values of b is equivalent to shifting the line up and down the scatter plotshifting the line up and down the scatter plot

Finding aFinding a Now we find the value of a that gives the least Now we find the value of a that gives the least

sum of squaressum of squares

Trying out different values of a is equivalent to Trying out different values of a is equivalent to changing the slope of the line, while b stays constantchanging the slope of the line, while b stays constant

Minimising the sum of squaresMinimising the sum of squares

Need to minimise Need to minimise ΣΣ(y–(y–ŷŷ))22

ŷŷ = ax + b = ax + b So need to minimise:So need to minimise:

ΣΣ(y - ax - b)(y - ax - b)22

If we plot the sums of If we plot the sums of squares for all different squares for all different values of a and b we get a values of a and b we get a parabola, because it is a parabola, because it is a squared termsquared term

So the minimum sum of So the minimum sum of squares is at the bottom of squares is at the bottom of the curve, where the the curve, where the gradient is zerogradient is zero

Values of a and b

Gradient = 0min S

The solutionThe solution

Doing this gives the following equation for a:Doing this gives the following equation for a:

a =r sy

r = correlation coefficient of x and ysy = standard deviation of ysx = standard deviation of x

From this we can see that: From this we can see that: A low correlation coefficient gives a flatter slope (low A low correlation coefficient gives a flatter slope (low

value of a)value of a) Large spread of y, i.e. high standard deviation, results in a Large spread of y, i.e. high standard deviation, results in a

steeper slope (high value of a)steeper slope (high value of a) Large spread of x, i.e. high standard deviation, results in a Large spread of x, i.e. high standard deviation, results in a

flatter slope (low value of a)flatter slope (low value of a)

The solution continuedThe solution continued

Our model equation is Our model equation is ŷ ŷ = ax + b= ax + b This line must pass through (x, y) so:This line must pass through (x, y) so:

y = ax + b b = y – ax

We can put our equation for a into this giving: We can put our equation for a into this giving:

b = y – ax

b = y - r sy

r = correlation coefficient of x and ysy = standard deviation of ysx = standard deviation of x

The smaller the correlation, the closer the intercept is to the mean of The smaller the correlation, the closer the intercept is to the mean of yy

Back to the modelBack to the model

If the correlation is zero, we will simply predict the mean of y for every If the correlation is zero, we will simply predict the mean of y for every value of x, and our regression line is just a flat straight line crossing the value of x, and our regression line is just a flat straight line crossing the x-axis at yx-axis at y

But this isn’t very usefulBut this isn’t very useful

We can calculate the regression line for any data, but the important We can calculate the regression line for any data, but the important question is how well does this line fit the data, or how good is it at question is how well does this line fit the data, or how good is it at predicting y from xpredicting y from x

ŷ = ax + b = r sy

x + y - x

ŷ = (x – x) + yRearranges to:

We’ve determined the form of the relationship We’ve determined the form of the relationship

(y = ax + b)(y = ax + b)

Does a prediction based on this model do a Does a prediction based on this model do a better job than just predicting the mean?better job than just predicting the mean?

How can we determine the significance of the model?How can we determine the significance of the model?

We can solve this using ANOVAWe can solve this using ANOVA In general: In general:

Total variance = predicted (or model) variance + error Total variance = predicted (or model) variance + error variancevariance

In a one-way ANOVA, we have:In a one-way ANOVA, we have:

VarianceVarianceTotal Total = = MSMSModelModel + + MSMSErrorError

MSError

MSModelF (df model, dferror)

MS=SS/dfMS=SS/df

Total = Model + Error(Between) (Within)

Partitioning the variance for linear regression (using ANOVA)Partitioning the variance for linear regression (using ANOVA)

So linear regression and ANOVA are doing the same thing statistically, and are the same as correlation…

ANOVAb

10.843 1 10.843 7.531 .017a

18.717 13 1.440

29.560 14

Regression

Residual

Model1

Sum ofSquares df Mean Square F Sig.

Predictors: (Constant), Ecstasy_frequencya.

Dependent Variable: Depressionb.

Another quick proof from SPSSAnother quick proof from SPSSCorrelations

1 .606*

.606* 1

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Depression

Ecstasy_frequency

DepressionEcstasy_frequency

Correlation is significant at the 0.05 level (2-tailed).*.

Regression

Correlation (Pearson’s r)

Relating the F and t statisticsRelating the F and t statistics

Alternatively (as F is the square of t):

2)2(ˆ1

So all we need toknow is N and r!!

NrF (df model, dferror)

MSError

MSModel

Basic assumptionsBasic assumptions

Variables: ratio or interval with > 10 (or 12, or Variables: ratio or interval with > 10 (or 12, or 15…) different pairs of values15…) different pairs of values

Variables normally distributed in the populationVariables normally distributed in the population Linear relationshipLinear relationship Residuals (errors) should be normally distributedResiduals (errors) should be normally distributed Independent samplingIndependent sampling

Warning 1: Outliers

Regression health warning!Regression health warning!

Warning 2: More than 1 different population or contrast(aka the “Ecological Fallacy”)

Regression health warning!Regression health warning!

(519 citations)

Science (1997) 277:968-71

Multiple regressionMultiple regression

Multiple regression is used to determine the effect of a Multiple regression is used to determine the effect of a number of independent variables, xnumber of independent variables, x11, x, x22, x, x33 etc, on a etc, on a single dependent variable, ysingle dependent variable, y

The different x variables are combined in a linear way The different x variables are combined in a linear way and each has its own regression coefficient:and each has its own regression coefficient:

y = ay = a11xx11+ a+ a22xx22 +…..+ a +…..+ annxxnn + b + + b + εε

The a parameters reflect the independent contribution of The a parameters reflect the independent contribution of each independent variable, x, to the value of the each independent variable, x, to the value of the dependent variable, ydependent variable, y

i.e. the amount of variance in y that is accounted for by i.e. the amount of variance in y that is accounted for by each x variable after all the other x variables have been each x variable after all the other x variables have been accounted foraccounted for

SPMSPM

Linear regression is a GLM that models the effect of one Linear regression is a GLM that models the effect of one independent variable, x, on ONE dependent variable, y independent variable, x, on ONE dependent variable, y

Multiple regression models the effect of several Multiple regression models the effect of several independent variables, xindependent variables, x11, x, x22 etc, on ONE dependent etc, on ONE dependent

variable, yvariable, y Both are types of General Linear ModelBoth are types of General Linear Model GLM can also allow you to analyse the effects of several GLM can also allow you to analyse the effects of several

independent x variables on several dependent variables, independent x variables on several dependent variables, yy11, y, y22, y, y33 etc, in a linear combination etc, in a linear combination

This is what SPM does!This is what SPM does!

AcknowledgementsAcknowledgements

Previous year’s slidesPrevious year’s slides David Howell’s excellent book Statistical David Howell’s excellent book Statistical

Methods for Psychology (2002)Methods for Psychology (2002) And David Howell’s websiteAnd David Howell’s website

http://www.uvm.edu/~dhowell/StatPages/Sthttp://www.uvm.edu/~dhowell/StatPages/StatHomePage.htmlatHomePage.html

The lecturers declare that they do not own stocks, shares or capital investments in David Howell’s The lecturers declare that they do not own stocks, shares or capital investments in David Howell’s book, they are not employed by the Duxbury group and do not consult for them, nor are they book, they are not employed by the Duxbury group and do not consult for them, nor are they associated with David Howell, or his friends, or his family, or his catassociated with David Howell, or his friends, or his family, or his cat

T-tests, ANOVA and regression Methods for Dummies February 1 st 2006 Jon Roiser and Predrag...

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