You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information. |
The following list contains all exercises which are available within
Teach/Me Data Analysis. All exercises are based on data supplied with Teach/Me.
Most of them can be carried out by using the built-in data laboratory.
Topic | Reference | Remarks |
Artificial neural network | NO_{x} Exhausts | Modeling the NO_{x} exhaust of an experimental motor |
Autocorrelation | Weather Station Data | Determining the time shift between the sun and the warming up of air and sea water |
Chance Correlation | Artifical Data | See the effect of correlation by chance when the number of observations is too low. |
Characterizing Distributions | Artificial Data | Design a data set showing a bimodal probability density function, and calculate the most important measures of location and variation. |
Cluster Analysis | Similar Mineral Waters | Try to find the two most similar mineral waters |
Correlation | Artificial Correlated Data | Design an artificial data set with predefined correlations |
Distribution | Delayed Trains | What is the probability that a given measurement falls within a given range |
Distribution | Alcohol Content | What is the probability that a given measurement falls within a given range |
LDA | Counterfeit Money | Use linear discriminant analysis to discriminate between genuine and counterfeit banknotes. |
Missing Values | Mineral Waters | Estimate missing values for moderately correlated data by multiple regression |
MLR | Polynomial Fit | Calculate a polynomial fit by means of MLR |
MLR | Collinearity | The effect of collinear variables on MLR models |
MLR | Boiling Points | Estimation of boiling points from chemical structures |
Multivariate Modeling | Henry Constants | Use several multivariate modeling methods to estimate log(H) from structural descriptors. |
Normal Distribution | Artificial Data | Design a data set showing a normal probability density function, shift and scale it, and calculate the most important descriptive parameters of it. |
Outliers | Creating outliers | Create two different data sets and experiment with outliers |
PCA | Wine Blending | Detecting mixtures in PCA plots |
PCA | Artificial Data | Dependence of principal component score on scaling of data |
PCA | Classification of Wine | Classify two unknown samples of wine |
PCR | Octane Numbers | Apply principal component regression as a combination of PCA and MLR |
Simple Regression | Weight of Perch | Try to Estimate the weight of perch from their body length |
Simple regression | Solid Residues | Establish the relationship between bicarbonate concentration and solid residues of mineral waters. |
Simple regression | Intestine Cancer | Try to set up a model for the relationship between male and female cancer data. |
t-Test | Humidity of US cities | Compare the relative humidities of 264 US cities |
t-Test | Coins | Use t-Test to decide whether coins lose weight during usage over the years |
t-Test | Reaction Times | Test whether the reaction time of a person is above a certain threshold. |
t-Test | Strontium concentration in drinking water | Compare the strontium concentration of several water wells in different areas. |
Variability | Reaction Times | Measure your reaction time and calculate mean and standard deviation |
Last Update: 2005-Jän-25