Probability density and cumulative functions in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the normal distribution from scipy.stats.

SciPy

from scipy.stats import [1]

Drag options to blanks, or click blank then click option'

Astats

Bnormal

Cnorm

Ddistribution

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'normal' instead of 'norm' causes import errors.

Importing 'stats' or 'distribution' is incorrect for normal distribution.

The normal distribution in scipy.stats is imported as norm.

2fill in blank

medium

Complete the code to calculate the probability density function (PDF) at x=0 for a standard normal distribution.

SciPy

pdf_value = norm.[1](0)

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Appf

Bcdf

Cpmf

Dpdf

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'cdf' instead of 'pdf' returns cumulative probability, not density.

Using 'pmf' is for discrete distributions, not normal distribution.

The pdf method calculates the probability density function value at a given point.

3fill in blank

hard

Fix the error in the code to compute the cumulative distribution function (CDF) at x=1.5.

SciPy

cdf_value = norm.[1](1.5)

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Apdf

Bcdf

Cppf

Drvs

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'pdf' returns density, not cumulative probability.

Using 'ppf' returns quantiles, not cumulative probability.

The cdf method returns the cumulative probability up to the given value.

4fill in blank

hard

Fill both blanks to create a dictionary of PDF values for x in [0, 1, 2] where PDF is greater than 0.1.

SciPy

pdf_dict = {x: norm.[1](x) for x in [0, 1, 2] if norm.[2](x) > 0.1}

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Apdf

Bcdf

Crvs

Dppf

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'cdf' in the filter returns cumulative probabilities, not densities.

Using 'rvs' or 'ppf' are unrelated to density calculation.

We use pdf to get the density values and filter those greater than 0.1.

5fill in blank

hard

Fill all three blanks to create a dictionary of cumulative probabilities for x in [0, 1, 2] where the cumulative probability is less than 0.9.

SciPy

cdf_dict = {x[1]2: norm.[2](x) for x in [0, 1, 2] if norm.[3](x) < 0.9}

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A*

Bcdf

C<

D**

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication (*) instead of exponentiation (**) for keys.

Using 'pdf' instead of 'cdf' for cumulative probabilities.

The dictionary keys are x squared (**), values are cumulative probabilities (cdf), filtered where cumulative probability is less than 0.9.