1595218080

The world is incredibly messy, so it takes a certain audacity for us to even attempt to find timeless structure within it. Statisticians are among this audacious bunch of people always on the hunt for regularity. But regularity is hard to find, and robust statements about the world usually come about only at the price of long periods of stumbling around blindly.

But sometimes it’s as if nature rewards us for our persistence, and makes us a present that simplifies everything a hell of a lot. The central limit theorem (CLT) is one such a present. Its power does not fail to surprise even weathered statisticians, and its usefulness makes it one of the central concepts of probability theory.

Due to its importance in statistics and its wide applicability, the notion of the CLT has in some form entered what could be called the grey area between general knowledge and pop folklore. As with all notions on that boundary, it comes with the danger of being misrepresented and misunderstood. Therefore, it is important to distinguish what the CLT says and what it doesn’t say.

The CLT ** does not** state that almost all random variables are normally distributed. This frankly also doesn’t make much sense, because things can be distributed however they please.

On the other hand, it **does state** that adding large-enough samples even from many non-normal distributions will lead to a distribution of the**_ sample means_** which is in fact normal.

Let’s take a closer look to see how this works.

Say we start with a somewhat messy and unstructured distribution. This could represent a lot of different things, f.e. the outcomes of a dice throw or the distribution of heights within an inhomogeneous population. The only requirement we have is that the distribution has a well-defined mean and variance (as you can see in this example, that doesn’t mean the distribution has to follow a bell curve!):

#probability #data-science #mathematics #statistics #science

1626471028

Welcome back to my Magic App Review. Learn everything about this traffic app.

Magic is the World’s 1st mobile traffic app. Just press a button and get unlimited free traffic. Simple set up with your affiliate networks, affiliate offers, and get free traffic to make sales.

BUT,

Now, you think is it really possible or not? I know you’re confused.

It’s simple to be confused. Actually, no app can generate free traffic to get affiliate sales. Inside my Magic App Review, I disclose everything. As a beta tester, after getting access to the Magic app I tried to use it. You can add your Warriorplus, JVZoo, Clickbank account here. Then you can get affiliate links too. But, the main problem is traffic.

I don’t like to buy and use any app for getting traffic and affiliate sales. Let’s complete this Magic Ap Review.

**Read Magic Review, See Why Not Recommended >>**

#magic app review #magic app #magic app review billy darr

1598537400

Python is known for its libraries. So, Let’s check out this amazing Covid Librar

Hello Warriors, Hope You are all safe at home during this covid pandemic in the World. This article is not about the covid virus , it is about covid library.

Johns Hopkins university and worldometers.info provided this python package to get novel corona virus updates. There are other ways to get covid related information in python using matplotlib, numpy, requests, tabulate, beautiful soup libraries. But, this library helps you to get info with minimum lines required.

To install covid library, see the below snippet

```
pip install covid
```

Let’s look at the modules basic functionality:

```
covid = Covid()
results = covid.get_status_by_country_name(“india”)
#Here we get the status of Covid pandemic only for India. You can get the results of the country you want by replacing India with country name
results
```

You can see the following output in the console

Output

If you want data of all countries, You can use the below snippet

```
data= covid.get_data()
#this "get_data" will give you the status of all the countries.
print(data)
```

#python #magical python. #a library in magical python.

1595218080

The world is incredibly messy, so it takes a certain audacity for us to even attempt to find timeless structure within it. Statisticians are among this audacious bunch of people always on the hunt for regularity. But regularity is hard to find, and robust statements about the world usually come about only at the price of long periods of stumbling around blindly.

But sometimes it’s as if nature rewards us for our persistence, and makes us a present that simplifies everything a hell of a lot. The central limit theorem (CLT) is one such a present. Its power does not fail to surprise even weathered statisticians, and its usefulness makes it one of the central concepts of probability theory.

Due to its importance in statistics and its wide applicability, the notion of the CLT has in some form entered what could be called the grey area between general knowledge and pop folklore. As with all notions on that boundary, it comes with the danger of being misrepresented and misunderstood. Therefore, it is important to distinguish what the CLT says and what it doesn’t say.

The CLT ** does not** state that almost all random variables are normally distributed. This frankly also doesn’t make much sense, because things can be distributed however they please.

On the other hand, it **does state** that adding large-enough samples even from many non-normal distributions will lead to a distribution of the**_ sample means_** which is in fact normal.

Let’s take a closer look to see how this works.

Say we start with a somewhat messy and unstructured distribution. This could represent a lot of different things, f.e. the outcomes of a dice throw or the distribution of heights within an inhomogeneous population. The only requirement we have is that the distribution has a well-defined mean and variance (as you can see in this example, that doesn’t mean the distribution has to follow a bell curve!):

#probability #data-science #mathematics #statistics #science

1624701907

We are awash in data. “Big data” is too small of a description. By 2025 an estimated 463 exabytes of data will be created globally each day.

Well aware of this, companies are ramping up their abilities to analyze data to make good decisions. A 2020 Research and Markets report put global spending on big data analytics at $180 billion, and a New Vantage Partners 2021 survey of executives said 92% say the pace of investment is accelerating, up from 40% the prior year.

Yet investing more money isn’t resulting in an equal return in results. Long-term progress on corporate data initiatives has declined, according to the New Vantage Partners study. Cultural challenges persist.

But balancing your employees’ knowledge of data while providing them the right tools to make the best use of that data plays a role, too. To move your organization forward, you need to take stock of where you now fall on the learning curve of data literacy.

#big data #latest news #where does your organization fall on the data literacy curve? #organization fall #data literacy curve #fall

1625622180

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#sql injection #magic bytes #setuid