This dataset contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It was obtained from the StatLib archive (http://lib.stat.cmu.edu/datasets/boston), and has been used extensively throughout the literature to benchmark algorithms. However, these comparisons were primarily done outside of Delve and are thus somewhat suspect. The dataset is small in size with only 506 cases.

Data from StatLib (ftp stat.cmu.edu/datasets)

Data from which conclusions were drawn in the article "Sleep in Mammals: Ecological and Constitutional Correlates" by Allison, T. and Cicchetti, D. (1976), Science, November 12, vol. 194, pp. 732-734. Includes brain and body weight, life span, gestation time, time sleeping, and predation and danger indices for 62 mammals.

Variables below (from left to right) for Mammals Data Set:

species of animal

body weight in kg

brain weight in g

slow wave ("nondreaming") sleep (hrs/day)

paradoxical ("dreaming") sleep (hrs/day)

total sleep (hrs/day) (sum of slow wave and paradoxical sleep)

maximum life span (years)

gestation time (days)

predation index (1-5) 1 = minimum (least likely to be preyed upon) 5 = maximum (most likely to be preyed upon)

sleep exposure index (1-5) 1 = least exposed (e.g. animal sleeps in a well-protected den) 5 = most exposed

overall danger index (1-5) (based on the above two indices and other information) 1 = least danger (from other animals) 5 = most danger (from other animals)

Note: Missing values denoted by -999.0

For more details, see

Allison, Truett and Cicchetti, Domenic V. (1976), "Sleep in Mammals: Ecological and Constitutional Correlates", Science, November 12, vol. 194, pp. 732-734.

The above data set can be freely used for non-commercial purposes and can be freely distributed (permission in writing obtained from Dr. Truett Allison).

Submitted by Roger Johnson rwjohnso@silver.sdsmt.edu

Total sleep treated as the class attribute. Attributes for slow wave and paradoxical sleep have been deleted. (The animal's name has also been deleted.)

This is the data repository for our manuscript in European Journal of Neuroscience. As this is our first attempt to share experiment data with others there could be some mistakes. Please feel free to contact me to improve this data repository if you are interested. The full data set used in the study, including the multi unit activity (MUA), the single unit activity (SUA), the excitatory post synaptic currents (EPSC) and inhibitory post synaptic currents (IPSC). for Bayesian Adaptive Regression Splines (BARS), please visit http://www.stat.cmu.edu/~kass/bars/bars.html for Matlab based scripts to find local peaks and vally, please visit https://www.mathworks.com/matlabcentral/fileexchange/25500-peakfinder-x0--sel--thresh--extrema--includeendpoints--interpolate-

Le geyser « Old Faithful » est un geyser en cône du parc de Yellowstone aux États-Unis https://simple.wikipedia.org/wiki/Old_Faithful_Geyser

On a mesuré : - duration : la durée de l’éruption - waiting : l’intervalle de temps depuis la dernière éruption - kind : une étiquette short ou long du type d’éruption

Version Seaborn du dataset https://www.stat.cmu.edu/~larry/all-of-statistics/=data/faithful.dat

https://upload.wikimedia.org/wikipedia/commons/1/1b/Old_Faithfull-pdPhoto.jpg" alt="Old Faithful Geyser">

Financial overview and grant giving statistics of Carnegie Mellon University

Statistical Machine Learning is a second graduate level course in advanced machine learning, assuming students have taken Machine Learning (10-715) and Intermediate Statistics (36-705). The course covers methodology and theoretical foundations. Function Spaces Concentration of Measure Linear Regression Nonparametric Regression Linear Classification Nonparametric Classification Minimax Theory Density Estimation Nonparametric Bayes Clustering Graphical Models Dimension Reduction Random Matrix Theory

The share of first-year female undergraduate students at Carnegie Mellon University's School of Computer Science has increased between 2010 and 2018 in the United States, going from a slim ** percent in 2010 to ** percent in 2018. CMU is leading the change in the computer science industry by taking a cultural approach rather than a gender difference approach when addressing the uneven gender representation in the industry, according to the source.

Financial overview and grant giving statistics of Osher Lifelong Learning Institute at Carnegie Mellon University

Financial overview and grant giving statistics of Chabad of Carnegie Mellon University Inc.