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Answer :
You can use Python libraries such as matplotlib or seaborn to create dot plots and simulate coin flips using random number generation functions.
1. To determine if any of the three students made up the x values shown in their dot plots, we can compare their dot plots to a simulation of randomly flipping a coin 30 times. By generating random numbers and counting the number of heads in each simulation, we can compare the distribution of the simulated data to the dot plots of the three students.
Here is an example of a Python simulation:
```python
import random
import matplotlib.pyplot as plt
# Simulating flipping a coin 30 times
def coin_flips():
flips = []
for _ in range(30):
flips.append(random.choice(['H', 'T']))
return flips.count('H')
# Simulating 100 observations
observations = []
for _ in range(100):
observations.append(coin_flips())
# Creating a dotplot of the simulated observations
plt.figure()
plt.plot(observations, [0.5]*len(observations), 'bo')
plt.xlabel('Number of Heads')
plt.ylabel('Frequency')
plt.title('Simulation Dotplot')
plt.show() ```
2. To construct a dotplot of the x values from the entire class, we can follow a similar approach as in the simulation above. Each student in the class should flip a coin 30 times and note the number of heads.
Here is an example of a Python simulation for the class:
``python
# Flipping a coin 30 times for each student in the class
class_observations = []
while len(class_observations) < 50:
class_observations.append(coin_flips())
3. After looking at the dotplot of the class observations, we can reconsider our answers for each of the three students. If their dotplots align with the class dotplot, it suggests that they actually performed the coin flipping
To know more about Python visit:
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