AM_1 Eri IC_4499 Lil_1 NGC_104 NGC_1261 NGC_1841 NGC_1851 1 1 1 1 1 1 1 1 NGC_1904 NGC_2298 NGC_2419 NGC_2808 NGC_288 NGC_3201 NGC_362 NGC_4147 1 1 1 1 1 1 1 1 NGC_4372 NGC_4590 NGC_4833 NGC_5024 NGC_5053 NGC_5139 NGC_5272 NGC_5286 1 1 1 1 1 1 1 1 NGC_5466 NGC_5634 NGC_5694 NGC_5824 NGC_5897 NGC_5904 NGC_5927 NGC_5946 1 1 1 1 1 1 1 1 NGC_5986 NGC_6093 NGC_6101 NGC_6121 NGC_6139 NGC_6144 NGC_6171 NGC_6205 1 1 1 1 1 1 1 1 NGC_6218 NGC_6229 NGC_6235 NGC_6254 NGC_6256 NGC_6266 NGC_6273 NGC_6284 1 1 1 1 1 1 1 1 NGC_6287 NGC_6293 NGC_6304 NGC_6316 NGC_6325 NGC_6333 NGC_6341 NGC_6342 1 1 1 1 1 1 1 1 NGC_6352 NGC_6355 NGC_6356 NGC_6362 NGC_6366 NGC_6380 NGC_6388 NGC_6397 1 1 1 1 1 1 1 1 NGC_6401 NGC_6402 NGC_6440 NGC_6441 NGC_6453 NGC_6496 NGC_6517 NGC_6522 1 1 1 1 1 1 1 1 NGC_6528 NGC_6535 NGC_6539 NGC_6541 NGC_6544 NGC_6553 NGC_6558 NGC_6569 1 1 1 1 1 1 1 1 NGC_6584 NGC_6624 NGC_6626 NGC_6637 NGC_6638 NGC_6642 NGC_6652 NGC_6656 1 1 1 1 1 1 1 1 NGC_6681 NGC_6712 NGC_6715 NGC_6717 NGC_6723 NGC_6749 NGC_6752 NGC_6760 1 1 1 1 1 1 1 1 NGC_6779 NGC_6809 NGC_6838 (Other) 1 1 1 14

The following row(s) of the original data set have/has been deleted, because of missing value(s):

Transforming and subsetting the data

Plots and summaries

Boxplot: A univariate plot giving a rough idea of the distributions and outliers.
Dotplot: Plotting values of the same variable for different objects (e.g., densities of different asteroids).
Histogram: Univariate histogram.
Averaged Shifted Histogram (ASH): Univariate ASH.
Scatterplot: A bivariate plot allowing error bars.
Pairs plot. This is sometimes also called a ScatterPLOt Matrix or SPLOM.
3D scatterplot
Summary statistics

Estimating distribution from data

Histogram
Averaged Shifted Histogram (ASH): Univariate ASH.
Empirical distributon function (ECDF)
Kernel density estimation:
Fiting parametric distribution

Comparing data with distributions

Normal plot
Tests for normality
1-sample KS test. This is useful for checking if a variable in your data set is distributed according to some standard distribution.
Anderson-Darling test. This is another test for checking if a variable in your data set is distributed according to some standard distribution.
2-sample KS test to compare the distributions of two continuous variables in a data set.
Chi-square 2 sample test to compare the distributions of two discrete variables in a data set.

Some standard tests

One sample z-test to compare the mean of a variable with some given number. This test is applicable only if you know (or have a very good approximation for) the standard deviation of your variable. (Requires Gaussianity assumption.)
One sample t-test. Similar to above, except that you do not need to know the standard deviation. (Requires Gaussianity assumption.)
Paired t-test for comparing the means of two variables. (Requires Gaussianity assumption.)
Wilcoxon's signed rank test (1 sample). Much like one sample t-test, but without the Gaussianity assumption.
Wilcoxon's signed rank test (2 sample). Much like paired t-test, but without the Gaussianity assumption.

Regression

Multivariate methods

Make a pairs plot. This is sometimes also called a ScatterPLOt Matrix or SPLOM.
Linear regression
Principal component analysis
Canonical correlation analysis
Model based clustering
Hierarchical clustering

Spatial methods

Directional statistics

Survival analysis

Maximum likelihood Kaplan-Meier estimate of the survival curve for censored data
Tests for censored data: Log rank, Peto-Peto, Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling tests
Maximum likelihood Lynden-Bell-Woodroofe estimator

Step 2: Choose an analysis