This book will hold a collection of problems, and my solutions to them, in applied statistics with R. These come from my courses STAC32, STAC33 and STAD29 at the University of Toronto Scarborough.
The problems were originally written in Sweave (that is, LaTeX with R
code chunks), using the exam
document class, using data sets stolen
from numerous places (textbooks, websites etc). I wrote a Perl
program
to strip out the LaTeX and turn each problem into R Markdown for this
book. You will undoubtedly see bits of LaTeX still embedded in the
text. I am trying to update my program to catch them, but I am sure to
miss some.
I just figured out that you can convert LaTeX “label” and “ref” pairs into HTML “a name” and “a href=‘#’”, which R Markdown can handle. I am ludicrously pleased with myself. To that effect, you will occasionally see question parts beginning with a *; this means that other question parts refer back to this one. (One of my favourite question strategies is to ask how two different approaches lead to the same answer, or more generally to demonstrate that there are different ways to see the same thing.)
If you see anything, file an issue on the Github page. I will take pull requests and will acknowledge all the people who catch things. Likely problems include:
As I read through looking for problems like these, I realize that there ought to be a textbook that reflects my way of doing things. There isn’t one (yet), though there are lecture notes. Reasonably recent versions of these are at:
A little background:
STAC32 is an introduction to R (and also SAS) as applied to statistical methods that have (mostly) been learned in previous courses, for example a non-mathematical applied course like this. The idea is that students have already seen a little of regression and analysis of variance (and the things that precede them), and need only an introduction of how to run them in R.
STAC33 covers similar ground to STAC32. It is intended for students with a more mathematical background, and will include some extra material beyond what is in STAC32 (eg., bootstrapping, Bayesian statistics with Stan).
STAD29 is an overview of a number of advanced statistical methods. I start from regression and proceed to some regression-like methods (logistic regression, survival analysis, log-linear frequency table analysis), then I go a little further with analysis of variance and proceed with MANOVA and repeated measures. I finish with a look at classical multivariate methods such as discriminant analysis, cluster analysis, principal components and factor analysis. I cover a number of methods in no great depth; my aim is to convey an understanding of what these methods are for, how to run them and how to interpret the results. Statistics majors and specialists cannot take this course for credit (they have separate courses covering this material with the proper mathematical background). D29 is intended for students in other disciplines who find themselves wanting to learn more statistics; we have an Applied Statistics Minor program for which C32 and D29 are two of the last courses.


I was young when freedom come so I dont remember slavery much. My parents died when I was young, and I married Fox not to long after. He has the same name as me because we were owned by the same masta.
The only thing I remember about slavery was how we were not allowed to learn, and how the only stories or songs we could deal with had to be pre aproved to not teach us anything. The only freedom of communication we had was songs in the feilds while we worked


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#include <cloud_recognition.h>
Public Member Functions | |
| void | cloudCorrespondence (const pcl::PointCloud< pcl::PointXYZI >::ConstPtr blob) |
| It is RECOMMENDED to run "modelConstruct" before. More... | |
| void | cloudHough3D (const pcl::PointCloud< pcl::PointXYZI >::ConstPtr blob, std::vector< Eigen::Matrix4f, Eigen::aligned_allocator< Eigen::Matrix4f > > &transformations, std::vector< pcl::Correspondences > &clustered_corrs) |
| cloudRecognition (ros::NodeHandle nh) | |
| void | modelConstruct (const pcl::PointCloud< pcl::PointXYZI >::ConstPtr blob) |
| This will run neccessary function and store the\ model metadata in the structure. More... | |
| void | updateParm (double ns, double ks, double ds, double rs, double cs, double ct) |
| ~cloudRecognition () | |
| Public Member Functions inherited from nimbus::cloudFeatures< PointType, NormalType > | |
| void | cloudBoardLocalRefeFrame (const PointCloudConstPtr blob) |
| cloudFeatures (ros::NodeHandle nh) | |
| void | cloudNormalEstimationOMP (const PointCloudConstPtr blob) |
| Kd Tree Search method. More... | |
| void | cloudSHOTEstimationOMP (const PointCloudConstPtr blob) |
| SHOT Estimation. More... | |
| ~cloudFeatures () | |
| Public Member Functions inherited from nimbus::cloudKeypoints< PointType > | |
| void | cloudISSKeypoints (const PointCloudConstPtr blob) |
| ISSKeypoint3D Detects Intrinstic Shape Sigmature keypoint for a given point cloud Reconmmended to implement Filter before. More... | |
| cloudKeypoints (ros::NodeHandle nh) | |
| void | cloudNarf (const PointCloudConstPtr blob) |
| NARF (Normal Aligned Radial Feature) More... | |
| void | cloudUniformSampling (const PointCloudConstPtr blob) |
| Uniform sampling Keypoints extraction (Detectors) http://docs.pointclouds.org/trunk/classpcl_1_1_uniform_sampling.html. More... | |
| ~cloudKeypoints () | |
| Public Member Functions inherited from nimbus::cloudEdit< PointType > | |
| void | _toRangeImage (const PointCloudConstPtr cloud, pcl::RangeImage &range) |
| cloudEdit (ros::NodeHandle nh) | |
| double | computeCloudResolution (const PointCloudConstPtr cloud) |
| void | remover (const PointCloudConstPtr blob, int width, int height, float perW, float perH, PointCloud &res) |
| void | zRemover (const PointCloudConstPtr blob, float maxDis, float minDis, PointCloud &res) |
| ~cloudEdit () | |
Public Attributes | |
| double | cg_size_ |
| double | cg_thresh_ |
| modelData | mData |
| pcl::CorrespondencesPtr | model_scene_corr |
| Public Attributes inherited from nimbus::cloudKeypoints< PointType > | |
| double | keypoint_sr |
| pcl::PointCloud< PointType >::Ptr | keypointOut |
Private Attributes | |
| ros::NodeHandle | _nh |
Additional Inherited Members | |
| Protected Attributes inherited from nimbus::cloudFeatures< PointType, NormalType > | |
| double | normal_sr |
| pcl::PointCloud< NormalType >::Ptr | normalOut |
| double | reference_sr |
| pcl::PointCloud< pcl::ReferenceFrame >::Ptr | referenceOut |
| double | shot_sr |
| pcl::PointCloud< pcl::SHOT352 >::Ptr | shotOut |
Definition at line 28 of file cloud_recognition.h.
| nimbus::cloudRecognition< PointType, NormalType >::cloudRecognition | ( | ros::NodeHandle | nh | ) |
Definition at line 9 of file cloud_recognition.cpp.
| nimbus::cloudRecognition< PointType, NormalType >::~cloudRecognition | ( | ) |
Definition at line 14 of file cloud_recognition.cpp.
| void nimbus::cloudRecognition< PointType, NormalType >::cloudCorrespondence | ( | const pcl::PointCloud< pcl::PointXYZI >::ConstPtr | blob | ) |
It is RECOMMENDED to run "modelConstruct" before.
Correspondence Matching
| input | downsampled cloud |
| input | search radius |
| output | normal cloud |
Definition at line 43 of file cloud_recognition.cpp.
| void nimbus::cloudRecognition< PointType, NormalType >::cloudHough3D | ( | const pcl::PointCloud< pcl::PointXYZI >::ConstPtr | blob, |
| std::vector< Eigen::Matrix4f, Eigen::aligned_allocator< Eigen::Matrix4f > > & | transformations, | ||
| std::vector< pcl::Correspondences > & | clustered_corrs | ||
| ) |
Definition at line 71 of file cloud_recognition.cpp.
| void nimbus::cloudRecognition< PointType, NormalType >::modelConstruct | ( | const pcl::PointCloud< pcl::PointXYZI >::ConstPtr | blob | ) |
This will run neccessary function and store the\ model metadata in the structure.
Model Constructor
Definition at line 28 of file cloud_recognition.cpp.
| void nimbus::cloudRecognition< PointType, NormalType >::updateParm | ( | double | ns, |
| double | ks, | ||
| double | ds, | ||
| double | rs, | ||
| double | cs, | ||
| double | ct | ||
| ) |
Update Params This will run per loop and update the param/ if Changed by dymanic param
Definition at line 17 of file cloud_recognition.cpp.
|
private |
Definition at line 30 of file cloud_recognition.h.
| double nimbus::cloudRecognition< PointType, NormalType >::cg_size_ |
Definition at line 34 of file cloud_recognition.h.
| double nimbus::cloudRecognition< PointType, NormalType >::cg_thresh_ |
Definition at line 34 of file cloud_recognition.h.
| modelData nimbus::cloudRecognition< PointType, NormalType >::mData |
Definition at line 32 of file cloud_recognition.h.
| pcl::CorrespondencesPtr nimbus::cloudRecognition< PointType, NormalType >::model_scene_corr |
Definition at line 33 of file cloud_recognition.h.


{{/* browse-meals.html contains the list of meals page. Dot is expected to be a struct/map containing a field 'Highlight' of type bool and a field 'MealID' of integral type. */}}


--- title: novaprova - Documentation ---
These are left over from before the transition to readthedocs.org