Let’s analyze the image to solve the tasks presented:

1. Construya la tabla de distribución de frecuencias (Frequency distribution table): I'll organize the data into intervals or groups and count their frequencies.

2. Dibuje el polígono de frecuencias (Frequency polygon): Once the frequency table is created, I will plot the frequencies as a graph.

3. Halle la media, mediana y moda (Mean, median, and mode): I will calculate:

   • Mean: $(\text{Sum of values}) / (\text{Number of values})$
   • Median: The middle value when the data is ordered.
   • Mode: The value(s) with the highest frequency.

Let me extract and organize the data from the image first. PIL import Image import pytesseract

Load the uploaded image

image_path = "/mnt/data/1733590757326obmqwazr.jpg" image = Image.open(image_path)

Use OCR to extract text from the image

extracted_text = pytesseract.image_to_string(image, lang="spa") extracted_textIt seems there was an issue with the OCR software for extracting text due to a missing language package. I can manually process the data visible in the image to proceed with the tasks. Let’s list the data points:

Extracted Numbers:

$75, 80, 75, 90, 100, 80, 65, 72, 65, 95, 75, 58, 62, 68, 40, 55, 73, 59, 52$

Now, I will:

1. Create a frequency distribution table.
2. Sketch a frequency polygon.
3. Calculate the mean, median, and mode. Let’s start. numpy as np import pandas as pd

Data points extracted manually from the image

data_points = [75, 80, 75, 90, 100, 80, 65, 72, 65, 95, 75, 58, 62, 68, 40, 55, 73, 59, 52]

Step 1: Create frequency distribution table

bins = range(40, 110, 10) # Create intervals of size 10 frequency_table = pd.cut(data_points, bins=bins, right=False).value_counts(sort=False) frequency_table_df = frequency_table.reset_index() frequency_table_df.columns = ['Interval', 'Frequency']

Step 2: Compute mean, median, and mode

mean = np.mean(data_points) median = np.median(data_points) mode = pd.Series(data_points).mode().tolist()

frequency_table_df, mean, median, mode