100% of variance explained by one principal component I am new to PCA and trying to do some analysis on my data set. When I apply PCA to my set of data I get all 100% variance on only one principal component. Does this make any sense?
Any explanation would be appreciated. 
 A: This means all your variables can be written as a linear transformation of a single one of them, which is a pretty extreme case of linear dependence.
(Why? Because all the principal components of a dataset together are always a basis for the vector space spanned by the original variables.)
A: To elaborate on @Kodiologist's answer (+1): let's say your data matrix is $X \in \mathbb R^{n \times p}$, and let $X = UDV^T$ be the SVD of $X$. Let's further assume that $X$ has been normalized so that $X^T X = V D^2 V^T$ is the covariance matrix. It is well known that the variance explained by the first $k$ principal components is $\sum_{i=1}^k d_i^2 / \sum_{i=1}^p d_i^2$ (feel free to ask for more details here). If $d_1^2 / \sum_{i=1}^p d_i^2 = 1$ then $d_2 = \dots = d_p = 0$ so this tells us that $X = d_1u_1 v_1^T$, i.e. $X$ is a rank 1 matrix, which is what it means to have all $p$ variables be perfectly collinear. We also have that the covariance matrix is $d_1^2 v_1 v_1^T$ which is also rank 1
A: One feature with large numeric range might dominate the others. 
Try to apply standardization on your dataset before PCA, by removing the mean and scaling to unit variance. 
