Good evening to everyone . This week has been crazy for all people in the crypto world . The crazy bear market has left many worried but few other see this as an opportunity to just buy more and be more involved in the crypto . 

I belong to the latter category . I am here to stay . 


Anyway coming to the point , we always talk about the activity on Hive being affected by price of Hive . Although this time Hive is holding pretty strong , the LEO price has taken a hit so I thought of doing some analysis on "How much does the price affect the activity?" 



### Is it possible to find such a relationship ? 

Yes , it is . There is something called as Linear Regression which allows us to find the correlation between two variables ( or say factors ) . 

Before we move on , you have to know this - 

> The coefficient of determination is a statistical measurement that examines how differences in one variable can be explained by the difference in a second variable, when predicting the outcome of a given event. In other words, this coefficient, which is more commonly known as R-squared (or R2), assesses how strong the linear relationship is between two variables, and is heavily relied on by researchers when conducting trend analysis. 

[Source](https://www.investopedia.com/terms/c/coefficient-of-determination.asp)

Today I will be doing it for both LEO as well as Hive . 

Before I go to the data part , here are the steps I have followed 

1. I have taken comments made with #leo , #leofinance and some other community tags . So basically every single comment made on LeoFinance ( Irrespective of Frontend used ) 
2. I have taken the data for the period - April 1st to May 25th . 
3. I have taken comments made on Hive from April 1st to May 25th too . 
4. I then retrieved the data for LEO price against HIVE from April 1st . I calculated the LEO to HIVE price as well as LEO to USD price based on Hive price . 
5. I got the HIVE historical price from [COINGECKO](https://www.coingecko.com/en/coins/hive/historical_data/usd#panel). 
6. Contains both post as well as comments data.


## Data analysis for LEO activity 

#### Let us see the LEO activity first 


![LEO_daily1.gif](https://images.hive.blog/DQmcT39Ht3qdCrEyZrJNChrXtsScs5DeKy7ThA8CUR3AzQy/LEO_daily1.gif)

So clearly it looks very similar and doesn't tell us much . It is just up and down and up and down , lot of variance . 

You have to note that this contains all comments made from LeoFinance irrespective of frontend used . If frontend used is taken into count , this will reduce by almost 2.5 to 3 times . 

#### LEO_USD and Comment count


![LEO_price_comment_count.gif](https://images.hive.blog/DQmPoSDhGfVs6eX81VJzoDokrc9sBR5MJR8N3tusqKHDzdL/LEO_price_comment_count.gif)


Orange line is the LEO to USD price and Blue line is the activity on LeoFinance . 


Well although at particular points it may seem like they correlate , no real strong point to declare it is affecting each other . 


### Linear regression 

This is the actual part which can tell us the correlation 

Let us take a look at LEO price in USD vs Comment count 


Now pay close attention to this .


![LEO_correlation.gif](https://images.hive.blog/DQmVZwYvv14gXVXwaKNBBck9S8yVFQ1WYNu8DmS9P8iD9H9/LEO_correlation.gif)


The lowest activity you can see was 1987 comments and 2016 comments per day and they were when the LEO price was the lowest too . 

Also I have stressed on a particular portion of the chart if you observe . The 0.7 PRICE part , I keep on drawing rectangles there . It tells us that when LEO price was 0.7$ and above we never had a day in which the activity was less than 2600 . Never after April 1st . 


But still not convincing ? 

This is what statistics say 

R<sup>2</sup> - which is Coefficient of Determination tells us how much one variable variation impacts the other variable . 

R<sup>2</sup>  value for LEO activity is 0.134 which means LEO USD price affects comment count 13.4% . 

Is 13% more or less ? I will leave it to you but according to statistics ( the P value is less than level of significance ) , there is a significant amount of correlation between these two variables . 


### Who counts LEO to USD ? Give us LEO in HIVE Price . 

Alright , let us take a look at that too - 


![LEO_price_comment_count_hive.gif](https://images.hive.blog/DQmRNk1faR8rCKMUMeVhmUFmtMkHNk5GoXqXhxdN7rqCTBX/LEO_price_comment_count_hive.gif)


Now the graph may seem a little bit different to you but the outcome ? The exact same . 

The R<sup>2</sup>  hasn't changed at all , it is still 0.13 which means the LEO price ( in Hive ) affects the LeoFinance activity by 13% . 

P- value is once again lesser than level of significance so the impact is real . 

### Well yeah the activity might be less but users aren't leaving the number of users are same . 


Well okay let us take a look at that . Let us take a look at number of unique authors who posted on LeoFinance vs the LEO price ( in HIVE )


![LEO_correlation_users.gif](https://images.hive.blog/DQmZ5DeAgs6De9es3Jn5zTYUMUXqwqno9ierZpQBF6Tk5zs/LEO_correlation_users.gif)

I will come straight to the point . The p value is less than level of significance which means " Leo price does have an impact on number of users " . 

The R<sup>2</sup> value is 9% though which is good which means the impact of price on activity is more than impact of price on users which also means users hop on but tend to be less active than before . 


Note: I arrived at the R<sup>2</sup> and P value by writing code in python as well as cross verifying it in Tableau software.

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That's it from me today . I thought of including Hive activity vs Hive price too but the post will become way too long . So I will post that tomorrow if you guys liked this analysis  , let me know in comments if you would like to see it : )

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Regards,
MR.

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