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Graphing Data 4
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Lecture1.1
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Lecture1.2
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Lecture1.3
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Lecture1.4
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Mean and Standard Deviation 5
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Lecture2.1
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Lecture2.2
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Lecture2.3
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Lecture2.4
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Lecture2.5
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Distributions 6
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Lecture3.1
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Lecture3.2
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Lecture3.3
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Lecture3.4
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Lecture3.5
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Lecture3.6
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Correlation and Linear Regression 7
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Lecture4.1
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Lecture4.2
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Lecture4.3
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Lecture4.4
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Lecture4.5
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Lecture4.6
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Lecture4.7
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Probability 3
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Lecture5.1
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Lecture5.2
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Lecture5.3
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Counting Principles 3
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Lecture6.1
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Lecture6.2
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Lecture6.3
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Binomial Distribution 3
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Lecture7.1
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Lecture7.2
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Lecture7.3
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Confidence Interval 7
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Lecture8.1
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Lecture8.2
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Lecture8.3
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Lecture8.4
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Lecture8.5
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Lecture8.6
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Lecture8.7
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Proportion Confidence Interval 3
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Lecture9.1
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Lecture9.2
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Lecture9.3
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Hypothesis Testing 5
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Lecture10.1
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Lecture10.2
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Lecture10.3
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Lecture10.4
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Lecture10.5
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Comparing Two Means 5
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Lecture11.1
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Lecture11.2
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Lecture11.3
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Lecture11.4
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Lecture11.5
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Chi-squared Test 3
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Lecture12.1
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Lecture12.2
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Lecture12.3
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Randomness
One great thing about programming is that we can simulate randomness, try the following code.
from random import randint
print(randint(0, 100))
The function randint() returns a random integer between the lower bound (0 in this case) and the upper bound (100 in this case).
Let’s set up our data as so….
from random import randint
grades = [randint(0, 100) for x in range(100)]
print(grades)
plt.hist() will allow us to plot a histogram.
plt.hist(grades)
plt.show()
We can have different numbers of bins by adding a bin=n argument.
plt.hist(grades,bins=30)
plt.show()
Source Code
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