- Home
- Documents
*Multivariate Analysis Many statistical techniques focus on just one or two variables Multivariate...*

prev

next

of 57

View

216Download

0

Tags:

Embed Size (px)

- Slide 1
- Multivariate Analysis Many statistical techniques focus on just one or two variables Multivariate analysis (MVA) techniques allow more than two variables to be analysed at once Multiple regression is not typically included under this heading, but can be thought of as a multivariate analysis
- Slide 2
- Outline of Lectures We will cover Why MVA is useful and important Simpsons Paradox Some commonly used techniques Principal components Cluster analysis Correspondence analysis Others if time permits Market segmentation methods An overview of MVA methods and their niches
- Slide 3
- Simpsons Paradox Example: 44% of male applicants are admitted by a university, but only 33% of female applicants Does this mean there is unfair discrimination? University investigates and breaks down figures for Engineering and English programmes MaleFemale Accept3520 Refuse entry 4540 Total8060
- Slide 4
- Simpsons Paradox No relationship between sex and acceptance for either programme So no evidence of discrimination Why? More females apply for the English programme, but it it hard to get into More males applied to Engineering, which has a higher acceptance rate than English Must look deeper than single cross-tab to find this out Engineer- ing MaleFemale Accept3010 Refuse entry 3010 Total6020 EnglishMaleFemale Accept510 Refuse entry 1530 Total2040
- Slide 5
- Another Example A study of graduates salaries showed negative association between economists starting salary and the level of the degree i.e. PhDs earned less than Masters degree holders, who in turn earned less than those with just a Bachelors degree Why? The data was split into three employment sectors Teaching, government and private industry Each sector showed a positive relationship Employer type was confounded with degree level
- Slide 6
- Slide 7
- Simpsons Paradox In each of these examples, the bivariate analysis (cross-tabulation or correlation) gave misleading results Introducing another variable gave a better understanding of the data It even reversed the initial conclusions
- Slide 8
- Many Variables Commonly have many relevant variables in market research surveys E.g. one not atypical survey had ~2000 variables Typically researchers pore over many crosstabs However it can be difficult to make sense of these, and the crosstabs may be misleading MVA can help summarise the data E.g. factor analysis and segmentation based on agreement ratings on 20 attitude statements MVA can also reduce the chance of obtaining spurious results
- Slide 9
- Multivariate Analysis Methods Two general types of MVA technique Analysis of dependence Where one (or more) variables are dependent variables, to be explained or predicted by others E.g. Multiple regression, PLS, MDA Analysis of interdependence No variables thought of as dependent Look at the relationships among variables, objects or cases E.g. cluster analysis, factor analysis
- Slide 10
- Principal Components Identify underlying dimensions or principal components of a distribution Helps understand the joint or common variation among a set of variables Probably the most commonly used method of deriving factors in factor analysis (before rotation)
- Slide 11
- Principal Components The first principal component is identified as the vector (or equivalently the linear combination of variables) on which the most data variation can be projected The 2 nd principal component is a vector perpendicular to the first, chosen so that it contains as much of the remaining variation as possible And so on for the 3 rd principal component, the 4 th, the 5 th etc.
- Slide 12
- Principal Components - Examples Ellipse, ellipsoid, sphere Rugby ball Pen Frying pan Banana CD Book
- Slide 13
- Multivariate Normal Distribution Generalisation of the univariate normal Determined by the mean (vector) and covariance matrix E.g. Standard bivariate normal
- Slide 14
- Example Crime Rates by State Crime Rates per 100,000 Population by State The PRINCOMP Procedure Observations 50 Variables 7 Simple Statistics MurderRapeRobberyAssaultBurglaryLarcenyAuto_Theft Mean 7.44400000025.73400000124.0920000211.30000001291.9040002671.288000377.5260000 StD 3.86676894110.7596299588.3485672100.2530492432.455711725.908707193.3944175 Crime Rates per 100,000 Population by State ObsStateMurderRapeRobberyAssaultBurglaryLarcenyAuto_Theft 1 Alabama14.225.296.8278.31135.51881.9280.7 2 Alaska10.851.696.8284.01331.73369.8753.3 3 Arizona9.534.2138.2312.32346.14467.4439.5 4 Arkansas8.827.683.2203.4972.61862.1183.4 5 California11.549.4287.0358.02139.43499.8663.5 ...
- Slide 15
- Correlation Matrix MurderRapeRobberyAssaultBurglaryLarcenyAuto_Theft Murder 1.00000.60120.48370.64860.38580.10190.0688 Rape 0.60121.00000.59190.74030.71210.61400.3489 Robbery 0.48370.59191.00000.55710.63720.44670.5907 Assault 0.64860.74030.55711.00000.62290.40440.2758 Burglary 0.38580.71210.63720.62291.00000.79210.5580 Larceny 0.10190.61400.44670.40440.79211.00000.4442 Auto_Theft 0.06880.34890.59070.27580.55800.44421.0000 Eigenvalues of the Correlation Matrix EigenvalueDifferenceProportionCumulative 1 4.114959512.876237680.5879 2 1.238721830.512905210.17700.7648 3 0.725816630.409384580.10370.8685 4 0.316432050.058457590.04520.9137 5 0.257974460.035934990.03690.9506 6 0.222039470.097983420.03170.9823 7 0.12405606 0.01771.0000
- Slide 16
- Eigenvectors Prin1Prin2Prin3Prin4Prin5Prin6Prin7 Murder 0.300279-.6291740.178245-.2321140.5381230.2591170.267593 Rape 0.431759-.169435-.2441980.0622160.188471-.773271-.296485 Robbery 0.3968750.0422470.495861-.557989-.519977-.114385-.003903 Assault 0.396652-.343528-.0695100.629804-.5066510.1723630.191745 Burglary 0.4401570.203341-.209895-.0575550.1010330.535987-.648117 Larceny 0.3573600.402319-.539231-.2348900.0300990.0394060.601690 Auto_Theft 0.2951770.5024210.5683840.4192380.369753-.0572980.147046 2-3 components explain 76%-87% of the variance First principal component has uniform variable weights, so is a general crime level indicator Second principal component appears to contrast violent versus property crimes Third component is harder to interpret
- Slide 17
- Cluster Analysis Techniques for identifying separate groups of similar cases Similarity of cases is either specified directly in a distance matrix, or defined in terms of some distance function Also used to summarise data by defining segments of similar cases in the data This use of cluster analysis is known as dissection
- Slide 18
- Clustering Techniques Two main types of cluster analysis methods Hierarchical cluster analysis Each cluster (starting with the whole dataset) is divided into two, then divided again, and so on Iterative methods k-means clustering (PROC FASTCLUS) Analogous non-parametric density estimation method Also other methods Overlapping clusters Fuzzy clusters
- Slide 19
- Applications Market segmentation is usually conducted using some form of cluster analysis to divide people into segments Other methods such as latent class models or archetypal analysis are sometimes used instead It is also possible to cluster other items such as products/SKUs, image attributes, brands
- Slide 20
- Tandem Segmentation One general method is to conduct a factor analysis, followed by a cluster analysis This approach has been criticised for losing information and not yielding as much discrimination as cluster analysis alone However it can make it easier to design the distance function, and to interpret the results
- Slide 21
- Tandem k-means Example proc factor data=datafile n=6 rotate=varimax round reorder flag=.54 scree out=scores; var reasons1-reasons15 usage1-usage10; run; proc fastclus data=scores maxc=4 seed=109162319 maxiter=50; var factor1-factor6; run; Have used the default unweighted Euclidean distance function, which is not sensible in every context Also note that k-means results depend on the initial cluster centroids (determined here by the seed) Typically k-means is very prone to local maxima Run at least 20 times to ensure reasonable maximum
- Slide 22
- Selected Outputs 19th run of 5 segments Cluster Summary Maximum Distance RMS Std from Seed Nearest Distance Between Cluster Frequency Deviation to Observation Cluster Cluster Centroids 1 433 0.9010 4.5524 4 2.0325 2 471 0.8487 4.5902 4 1.8959 3 505 0.9080 5.3159 4 2.0486 4 870 0.6982 4.2724 2 1.8959 5 433 0.9300 4.9425 4 2.0308
- Slide 23
- Selected Outputs 19th run of 5 segments FASTCLUS Procedure: Replace=RANDOM Radius=0 Maxclusters=5 Maxiter=100 Converge=0.02 Statistics for Variables Variable Total STD Within STD R-Squared RSQ/(1-RSQ) FACTOR1 1.000000 0.788183 0.379684 0.612082 FACTOR2 1.000000 0.893187 0.203395 0.255327 FACTOR3 1.000000 0.809710 0.345337 0.527503 FACTOR4 1.000000 0.733956 0.462104 0.859095 FACTOR5 1.000000 0.948424 0.101820 0.113363 FACTOR6 1.000000 0.838418 0.298092 0.424689 OVER-ALL 1.000000 0.838231 0.298405 0.425324 Pseudo F Statistic = 287.84 Approximate Expected Over-All R-Squared = 0.37027 Cubic Clustering Criterion = -26.135 WARNING: The two above values are invalid for correlated variables.
- Slide 24
- Selected Outputs 19th run of 5 segments Cluster Means Cluster FACTOR1 FACTOR2 FACTOR3 FACTOR4 FACTOR5 FACTOR6 1 -0.17151 0.86945 -0.06349 0.08168 0.14407 1.17640 2 -0.96441 -0.62497 -0.02967 0.6708