1650327960

Go Statistics Handler

- The
`gosh`

is an abbreviation for Go Statistics Handler. - This Repository is provided following functions.
- Go runtime statistics struct.
- Go runtime statistics API handler.
- Go runtime measure method.

- You can specify the favorite JSON encoder.
`encoding/json`

package.`goccy/go-json`

package.- The original package you created, and so on.

```
$ go get -u github.com/osamingo/gosh
```

```
package main
import (
"encoding/json"
"io"
"log"
"net/http"
"github.com/osamingo/gosh"
)
func main() {
h, err := gosh.NewStatisticsHandler(func(w io.Writer) gosh.JSONEncoder {
return json.NewEncoder(w)
})
if err != nil {
log.Fatalln(err)
}
mux := http.NewServeMux()
mux.Handle("/healthz", h)
if err := http.ListenAndServe(":8080", mux); err != nil {
log.Fatalln(err)
}
}
```

```
$ curl "localhost:8080/healthz" | jq .
{
"timestamp": 1527317620,
"go_version": "go1.10.2",
"go_os": "darwin",
"go_arch": "amd64",
"cpu_num": 8,
"goroutine_num": 6,
"gomaxprocs": 8,
"cgo_call_num": 1,
"memory_alloc": 422272,
"memory_total_alloc": 422272,
"memory_sys": 3084288,
"memory_lookups": 6,
"memory_mallocs": 4720,
"memory_frees": 71,
"stack_inuse": 491520,
"heap_alloc": 422272,
"heap_sys": 1605632,
"heap_idle": 401408,
"heap_inuse": 1204224,
"heap_released": 0,
"heap_objects": 4649,
"gc_next": 4473924,
"gc_last": 0,
"gc_num": 0,
"gc_per_second": 0,
"gc_pause_per_second": 0,
"gc_pause": []
}
```

Author: osamingo

Source Code: https://github.com/osamingo/gosh

License: MIT License

1649917336

This book was originally (and currently) designed for use with STAT 420, Methods of Applied Statistics, at the University of Illinois at Urbana-Champaign.

**Publication date**: 30 Oct 2020**Paperback**: 417 pages**Type**: Textbook**License**: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

This book was originally (and currently) designed for use with

STAT 420,Methods of Applied Statistics, at the University of Illinois at Urbana-Champaign. It may certainly be used elsewhere, but any references to “this course” in this book specifically refer to STAT 420.

This book is under active development. When possible, it would be best to always access the text online to be sure you are using the most up-to-date version. Also, the html version provides additional features such as changing text size, font, and colors. If you are in need of a local copy, apdf versionis continuously maintained, however, because a pdf uses pages, the formatting may not be as functional. (In other words, the author needs to go back and spend some time working on the pdf formatting.)

#statistics #r #programming #textbook #ebook #book

1649817710

Designed to introduce students to quantitative methods in a way that can be applied to all kinds of data in all kinds of situations, Statistics and Data Visualization Using R: The Art and Practice of Data Analysis by David S. Brown teaches students statistics through charts, graphs, and displays of data that help students develop intuition around statistics as well as data visualization skills. By focusing on the visual nature of statistics instead of mathematical proofs and derivations, students can see the relationships between variables that are the foundation of quantitative analysis. Using the latest tools in R and R RStudio® for calculations and data visualization, students learn valuable skills they can take with them into a variety of future careers in the public sector, the private sector, or academia. Starting at the most basic introduction to data and going through most crucial statistical methods, this introductory textbook quickly gets students new to statistics up to speed running analyses and interpreting data from social science research.

- Length: 616 pages
- Edition: 1
- Language: English
- Publisher: SAGE Publications
- Publication Date: 2021-09-22

#statistics #datavisualization #r #programming #developer #datascience #ebook #book #pdf

1647591780

Dynamically identify the suggested number of clusters in a data-set using the gap statistic.

Bleeding edge:

```
pip install git+git://github.com/milesgranger/gap_statistic.git
```

PyPi:

```
pip install --upgrade gap-stat
```

With Rust extension:

```
pip install --upgrade gap-stat[rust]
```

```
pip uninstall gap-stat
```

This package provides several methods to assist in choosing the optimal number of clusters for a given dataset, based on the Gap method presented in "Estimating the number of clusters in a data set via the gap statistic" (Tibshirani et al.).

The methods implemented can cluster a given dataset using a range of provided k values, and provide you with statistics that can help in choosing the right number of clusters for your dataset. Three possible methods are:

- Taking the
`k`

maximizing the Gap value, which is calculated for each`k`

. This, however, might not always be possible, as for many datasets this value is monotonically increasing or decreasing. - Taking the smallest
`k`

such that Gap(k) >= Gap(k+1) - s(k+1). This is the method suggested in Tibshirani et al. (consult the paper for details). The measure`diff = Gap(k) - Gap(k+1) + s(k+1)`

is calculated for each`k`

; the parallel here, then, is to take the smallest`k`

for which`diff`

is positive. Note that in some cases this can be true for the entire range of`k`

. - Taking the
`k`

maximizing the Gap* value, an alternative measure suggested in "A comparison of Gap statistic definitions with and with-out logarithm function" by Mohajer, Englmeier and Schmid. The authors claim this measure avoids the over-estimation of the number of clusters from which the original Gap statistics suffers, and can also suggest an optimal value for k for cases in which Gap cannot. They do warn, however, that the original Gap statistic performs better than Gap* in the case of overlapped clusters, due to its tendency to overestimate the number of clusters.

Note that none of the above methods is guaranteed to find an optimal value for `k`

, and that they often contradict one another. Rather, they can provide more information on which to base your choice of `k`

, which should take numerous other factors into account

First, construct an `OptimalK`

object. Optional intialization parameters are:

`n_jobs`

- Splits computation into this number of parallel jobs. Requires choosing a parallel backend.`parallel_backend`

- Possible values are`joblib`

,`rust`

or`multiprocessing`

for the built-in Python backend. If`parallel_backend == 'rust'`

it will use all cores.`clusterer`

- Takes a custom clusterer function to be used when clustering. See the example notebook for more details.`clusterer_kwargs`

- Any keyword arguments to be forwarded to the custom clusterer function on each call.

An example intialization:

```
optimalK = OptimalK(n_jobs=4, parallel_backend='joblib')
```

After the object is created, it can be called like a function, and provided with a dataset for which the optimal K is found and returned. Parameters are:

`X`

- A pandas dataframe or numpy array of data points of shape`(n_samples, n_features)`

.`n_refs`

- The number of random reference data sets to use as inertia reference to actual data. Optional.`cluster_array`

- A 1-dimensional iterable of integers; each representing`n_clusters`

to try on the data. Optional.

For example:

```
import numpy as np
n_clusters = optimalK(X, cluster_array=np.arange(1, 15))
```

After performing the search procedure, a DataFrame of gap values and other usefull statistics for each passed cluster count is now available as the `gap_df`

attributre of the `OptimalK`

object:

```
optimalK.gap_df.head()
```

The columns of the dataframe are:

`n_clusters`

- The number of clusters for which the statistics in this row were calculated.`gap_value`

- The Gap value for this`n`

.`gap*`

- The Gap* value for this`n`

.`ref_dispersion_std`

- The standard deviation of the reference distributions for this`n`

.`sk`

- The standard error of the Gap statistic for this`n`

.`sk*`

- The standard error of the Gap* statistic for this`n`

.`diff`

- The diff value for this`n`

(see the methodology section for details).`diff*`

- The diff* value for this`n`

(corresponding to the diff value for Gap*).

Additionally, the relation between the above measures and the number of clusters can be plotted by calling the `OptimalK.plot_results()`

method (meant to be used inside a Jupyter Notebook or a similar IPython-based notebook), which prints four plots:

- A plot of the Gap value versus n, the number of clusters.
- A plot of diff versus n.
- A plot of the Gap* value versus n, the number of clusters.
- A plot of the diff* value versus n.

Download Details:

Author: milesgranger

Source Code: https://github.com/milesgranger/gap_statistic

License: View license

1647154412

This video on Python for Data Science will make you understand the basics of data science, important libraries in Python for Data Science such as NumPy, Pandas, and Matplotlib. You will get an idea about the Data Science concepts along with mathematics, statistics, and linear algebra.

- Data Science Basics
- Data Science libraries
- Mathematics for Data Science
- Data Science algorithms using python
- Regularization, PCA, Cost Functions
- Who is a Data Scientist

#python #datascience #algorithms #datascientist #numpy #pandas #matplotlib #mathematics #statistics #linearalgebr

1645206180

This ACT math prep study guide review youtube video tutorial contains plenty of examples and practice problems with solutions to help you master the concepts that is commonly tested on the act. It contains tips and strategies to help you some common act math problems in algebra, geometry, and trigonometry. This video contains the formulas you need to answer very common questions. This video provides a basic overview of questions you might see on the actual test. If you need help, you came to the right place.

1645195200

This video explains how to find the correlation coefficient which describes the strength of the linear relationship between two variables x and y.

1645184160

This statistics video tutorial explains how to find the equation of the line that best fits the observed data using the least squares method of linear regression.

1645173180

This statistics video tutorial explains how to perform a hypothesis test of independence using the chi-square distribution.

1645162320

This statistics video tutorial provides a basic introduction of the chi square distribution test of a single variance or standard deviation. It explains how to use it in order to determine whether or not you reject the null hypothesis.

1645151400

This statistics video tutorial provides a basic introduction into the chi square test. It explains how to use the chi square distribution to perform a goodness of fit test to determine whether or not to accept or reject the null hypothesis.

1645129680

This Statistics video tutorial provides a basic introduction into matched or paired samples. It explains how to use the T-test and the student's t-distribution to determine whether or not if you should reject the null hypothesis in favor of the alternative hypothesis. It also explains how to construct a confidence interval and calculate the margin of error at a specified significance level.

1645118760

This statistics video tutorial covers hypothesis testing with two proportions. It provides an example problem that shows you how to determine if the difference between two proportions is significant using the z-test and the normal distribution curve.

1645107900

This statistics video tutorial explains how to calculate Cohen's d to determine if the size of the effect is small, medium, or large based on the differences between two sample means. This video also provides two ways to calculate the pooled standard deviation.

1645096980

This statistics video explains how to perform hypothesis testing with two sample means using the t-test with the student's t-distribution and the z-test with the normal distribution table.