SERIAL POSITION EFFECT

We're conducting a fun and simple experiment to explore how people remember words!

What you'll do:

You'll see a list of 20 words for 20 seconds, ታማኝ ሁኑ ከ20 ሰከንድ በላይ አትጠቀሙ
After that, you’ll recall and type as many words as you can remember, ሁሉንም ቃላት ለመሸምደድ ሞክሩ
It’s totally voluntary, and your participation will help us understand how memory works better!

Why participate?

Challenge your memory skills
Contribute to an interesting research experiment
Click here to start! ➡️ Google Form
Let’s see how well you can remember the words!

SERIAL POSITION EFFECT

Just completed the serial position effect experiment! The results didn’t show a perfect U-shape, but it closely resembled one.

With only 11 participants, the pattern was still visible although more participants might have made it clearer. It’s amazing how memory shows such predictable trends. The primacy and recency effects are also clearly visible.

The graph is made using the Python module Matplotlib. Check it out above.


#SerialPositionEffect

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Have you guys seen this?

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Let's keep learning, sharing, and inspiring together. More exciting content is on the way!

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Next: Data Visualization in Python

በቀጣይ አንድን መረጃ Pythonን በመጠቀም እንዴት visualize ማረግ እንደምንችል እናያለን።

Stay Tuned!

#DataVisualizationInPython

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📊 Understanding Data Visualization in Python

🔹Data Visualization ምንድነው?

Data visualization የምንለው አንድን መረጃ graphically ወይም በሚታይ መልኩ የምናስቀምጥበት ወይም represent የምናረግበት ሂደት ነው።

በData Processing stages ውስጥ Data visualizationን የሚገባው በ Data Output ውይም Data interpretation እና Analysis ደረጃ ላይ ነው።

Data Output stage ላይ እንደ እነ charts, graphs, የመሳሰሉት የvisualization tools processed መረጃን ለመረዳት በሚቀል መልኩ እንድናስቀምጥ ይረዱናል።

Data Interpretation እና Analysis stage ላይ visualization የ አንድን መረጃ pattern ወይም trend እንድንረዳ፡ እናም መረጃ ላይ የተመሠረተ ውሳኔ እንድንወስን ያግዘናል።

Good data visualization enhances understanding and communication, making complex data more accessible.

To be cont'd

#DataVisualization
#Data

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Our channel is making money😜

በPython, data visualization moduleኦች በእነዚህ ቁልፍ ደረጃዎች ይሰራሉ።


1. Import the required library – Common libraries include matplotlib, seaborn, and plotly.

import matplotlib.pyplot as plt
import seaborn as sns

2. Prepare the data – Load or create a dataset.

data = [10, 20, 15, 25, 30]

3. Create the plot – Use functions to generate a visualization.

plt.plot(data)

4. Customize the visualization – Add labels, noscripts, colors, or styles.

plt.noscript("Simple Line Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")

5. Display the visualization – Render the plot.

plt.show()

This process applies to various chart types like bar plots, histograms, and scatter plots, depending on the library and data used.

to be cont'd

#DataVisualization
#Data

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