Random sampling distributions in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to generate 1000 random samples from a normal distribution with mean 0 and standard deviation 1.

NumPy

samples = np.random.normal(loc=0, scale=[1], size=1000)

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A-1

B0

C10

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 as standard deviation causes no variation in samples.

Using negative values for standard deviation is invalid.

The standard deviation (scale) for a standard normal distribution is 1.

2fill in blank

medium

Complete the code to generate 500 random samples from a uniform distribution between 10 and 20.

NumPy

samples = np.random.uniform(low=[1], high=20, size=500)

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A10

B0

C20

D15

Attempts:

3 left

💡 Hint

Common Mistakes

Setting low greater than high causes errors.

Using 0 instead of 10 changes the range.

The lower bound (low) for the uniform distribution is 10.

3fill in blank

hard

Fix the error in the code to generate 100 samples from a binomial distribution with 10 trials and success probability 0.5.

NumPy

samples = np.random.binomial(n=10, p=[1], size=100)

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A1.5

B0.5

C-0.5

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using values greater than 1 or negative for p causes errors.

Confusing number of trials n with probability p.

The probability p must be between 0 and 1. 0.5 is valid.

4fill in blank

hard

Fill both blanks to create a dictionary of sample means from 1000 samples each, for sample sizes 10 and 50, using a normal distribution with mean 0 and std 1.

NumPy

means = {size: np.mean(np.random.normal(loc=0, scale=1, size=[1])) for size in [[2], 50]}

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A10

B20

C30

D40

Attempts:

3 left

💡 Hint

Common Mistakes

Using a fixed size instead of the variable size.

Mismatching sample sizes between the dictionary keys and sample generation.

The first blank is the sample size for the np.random.normal call, which should match the size variable in the dictionary comprehension. The second blank is the first sample size in the list, 10.

5fill in blank

hard

Fill all three blanks to create a dictionary of sample variances from 500 samples each, for sample sizes 20 and 100, using a uniform distribution between 0 and 1.

NumPy

variances = {size: np.var(np.random.uniform(low=0, high=[1], size=[2]), ddof=[3]) for size in [20, 100]}

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A1

B500

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using ddof=0 gives population variance, not sample variance.

Using wrong sample size causes incorrect variance calculation.

The high parameter is 1 for uniform distribution between 0 and 1. The size is 500 samples. The ddof parameter is 1 to get the sample variance.