This is a model containing the step by step method of predicting the price of HIVE when that of BNB is known using statistical analysis otherwise known as regression analysis.
The tools used for this analysis are SPSS and python.
SPSS was used to formulate a linear model for the price of HIVE whereas python was used to code the model to accept input which is the price of BNB and return the price of HIVE in USDT.
About the data
The data collected were the price of HIVE and BNB from the month of November, 2022 to February, 2023 which was analyzed in SPSS.
Here is a sample of the data collected in SPSS spreadsheet.
Conditions for modelling HIVE price
Correlations
An analysis of prediction can be useful if there is a form of relationship between both variables. The variables used are the price of HIVE and that of BNB.
The predictive analysis method in SPSS also calculated the correlation analysis level.
However, from the correlation table, there is a Pearson correlation value of 0.870. This value indicates a positive correlation which defines that as the price of BNB pumps, the price of HIVE will also pump and vice versa.
Normality Test
Normality test proves if the variables are from a given population following an order of normality.
However with the use of histogram shows normality is present but there is a little deviation from the normal in the price of HIVE. This will be counted as been part of the 0.2% of correlation loss.
Building the model
Building a model to predict for the state of HIVE (Bull or Bear) in the current or next Bull Run of BNB.
This starts with an entry variable, the entry variable is the price of BNB.
From the second table
The model summary can be determined from the adjusted R square column, however, with a value of 0.755 shows that the model predicts for 75% variance in the price of HIVE.
Coefficients
Coefficient produces the model to predict for the price of HIVE is done by considering the “unstandardized B” column.
However the model is summarized as this
Price of HIVE = 0.002 * price of BNB – 0.152.
Building the python program to use the model
The model was programmed in python. With the image below,
The algorithms for building the program is as stated;
The program allows for the user to input his or her name.
The code prompts the user to enter a price for BNB.
The algorithm calculates for the price of HIVE using the stated model presented under the coefficient. This then prints the value of HIVE price alongside in USDT.
Limitation
The limitation I encountered in this analysis was both variable prices were not enough to generate an accurate model for the analysis. I entered the price of HIVE and BNB from the beginning of December, 2022 to the February, 2023.
However, using a more advanced tool to capture the price of HIVE from 2020, will give a close to accurate prediction for the price of HIVE.
Thanks so much for reading this piece,
Here is my twitter link:
https://twitter.com/Iniobon81962037Link to python model:
https://drive.google.com/file/d/1HaM7mO8m0ppm8HW6EA3nsriUoqie4ABP/view?usp=drivesdkLink to data:
https://drive.google.com/file/d/1HdfernWkVXShbq_OiBwCr8d62BS6jZ_C/view?usp=drivesdkLink to SPSS output file:
https://drive.google.com/file/d/1HZXznRH8J-6qkDjG1Oast15w8xaliUzm/view?usp=drivesdk
All images used in this article are mine.