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Sometimes, my question took one week to get the answer 🥹

Source: Internet

There are the top 10 Data science use cases that were used by retail industries to grow their industries in today’s world.

1. Price optimization
2. Personalized Marketing
3. Fraud detection in Retail
4. Utilizing Social Media
5. Implementing Augmented Reality
6. Merchandising
7. Location of New Store
8. Inventory Management
9. Customer Sentiment Analysis
10. Recommendation System

Read more information at: https://www.analyticsvidhya.com/blog/2021/05/data-science-use-cases-in-retail-industry/

Data Science in Retail Industry | Data Science Use Cases in Retail Industry In this article let's see the top 10 Data science use cases that were used by retail industries to grow their industries in today's world.

How is your model? 👀👀

Source: internet

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Source: Twitter

Dear R - coders!

I know all we want to be an expert in R, we can do real projects, however, before becoming who we are on that day, we need to build basic knowledge.

In this table, we will take a look at how logical operators in R. This is critical for loop (i.e if()........else().....)

Thank you
P/s: I would love to learn and share knowledge with each other. If you have question, please don't hesitate.

Có bạn hỏi 𝗾𝘁(), 𝗽𝘁(), 𝗱𝘁() 𝘃𝗮̀ 𝗿𝘁() trong R là gì, nên mình xin chia sẻ cụ thể như sau:
Những hàm này được sử dụng đối với phân phối Student T test trong R

𝗱𝘁(): Hàm trả giá trị của hàm măt độ xác suất của phân phối Student T test tại giá trị x và bậc tự do là df
𝗽𝘁(): Hàm trả giá trị của Hàm xác suất tích lũy của phân phối Student T test tại giá trị x và bậc tự do là df
𝗾𝘁(): Hàm trả giá trị nghịch đảo của hàm xác suất tích lũy trong Student T test tại giá trị x và bậc tự do là df
𝗿𝘁(): Hàm trả kết quả là vector của các biến ngẫu nhiên tuân theo phân phối Student T test. Chiều dài của vector là n và bậc tự do là df.

Mời các bạn tham khảo thêm tại:
https://rpubs.com/rforusmd/915647

Top 3 Machine Learning Algorithms You Need to Know
☞ https://morioh.com/p/286102722cdc

SPSS or R 🤔?

Source: Statistics

Is that you? 🤭

𝘚𝘰𝘶𝘳𝘤𝘦: 𝘚𝘵𝘢𝘵𝘪𝘴𝘵𝘪𝘤𝘴

[Homework]
Hi all, one student asked us this question:
Suppose that 20% of all copies of a particular textbook fail a certain binding strength test. Let X denote the number of copies among 10 randomly selected copies that fail the test.
(i) Find the 𝗺𝗲𝗮𝗻 and 𝘃𝗮𝗿𝗶𝗮𝗻𝗰𝗲 of X.
(ii) Find the probability 𝗣(𝟯 ≤ 𝗫 ≤ 𝟱)

We will solve it by R
-------------------------------------------------------------
Step 1 : We know that p = 0.2; n = 10; this distribution is binomial.
(i) 𝗺𝗲𝗮𝗻 and 𝘃𝗮𝗿𝗶𝗮𝗻𝗰𝗲 of X
=> so mean = n*p = 0.2*10 = 2
=> variance = sqrt[n*p*(1-P)] = sqrt(10*0.2*0.8) = 1.264911
(ii) 𝗣(𝟯 ≤ 𝗫 ≤ 𝟱)
Use formula : 𝗽𝗯𝗶𝗻𝗼𝗺(𝗾,𝗻,𝗽), in R pbinom(q, size, prob)
=> pbinom(𝟱, 𝟭𝟬, 𝗽𝗿𝗼𝗯 = 𝟬.𝟮) - pbinom(𝟯, 𝟭𝟬, 𝗽𝗿𝗼𝗯 = 𝟬.𝟮)
[1] 𝟬.𝟭𝟭𝟰𝟱𝟬𝟰𝟱

Okay, we've done. We hope this solution can help her understand how to solve exercises related to the binomial distribution.

Please feel free for commenting below with your question about exercises in the binomial distribution type 👇👇👇

Do you actually understand the alternative hypothesis and null hypothesis?
Today, 𝗥 𝗳𝗼𝗿 𝗨𝘀 will share with you more information about two of those hypotheses in statistics.
𝗜𝗻 𝘁𝗵𝗲𝗼𝗿𝘆,
- The hypothesis that we want to validate is called an 𝙖𝙡𝙩𝙚𝙧𝙣𝙖𝙩𝙞𝙫𝙚 𝙝𝙮𝙥𝙤𝙩𝙝𝙚𝙨𝙞𝙨. . Usually, it represents a potential new theory, new method, new discovery…(from now to the future)
- The competitor of alternative hypothesis is a so-called 𝙣𝙪𝙡𝙡 𝙝𝙮𝙥𝙤𝙩𝙝𝙚𝙨𝙞𝙨, which represents
existing theory, existing method, existing knowledge (in the past)
𝗜𝗻 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲,
Assume that the prevalence rate of lung cancer in this population is an unknown parameter π. In contrast, the prevalence rate of the same disease among all non-smokers is known to be π0. Then, the above statement basically says that π > π0. Because we believe it may be that smokers are at higher risk than non-smokers related to lung cancer.

We're welcome to share with you useful knowledge about statistics before you become a data scientist. 🤟