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# Applied Statistics Assignment help, Homework Help | Practice Sample

```# importing libraries
import pandas as pd
import numpy as np
import warnings
import matplotlib.animation as animation
import seaborn as sns
import math
import matplotlib.pyplot as plt
sns.set(style="darkgrid")
pd.set_option('display.max_columns',100)
pd.set_option('display.max_colwidth', -1) # data and columns are displayed correctly
pd.options.display.float_format = '{:20,.2f}'.format # display float value with correct precision
warnings.filterwarnings('ignore')```
```##You observe from the above table, that 400 people planned to purchase and actually placed an order  is
##400 out of 2000 people.
plan_purchase = 400
total = 2000
joint_prob = round(plan_purchase/total,4)
joint_prob```

Output:

0.2

```##Que1b
# P1 = P(Actually placed an order | Planned to purchase)
# P2 = Planned to purchase and actually placed an order
# P3 = Planned to purchase
## P1 = P2/P3
P1   = (400 / 2000 )/ (500 / 2000)```

Solution:

```## Que2
import numpy as np
import pandas  as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
p   =  0.05 # failure rate of products that the company manufactures is 5%
n   =  10 # sample size
k   =  np.arange(0,11) # An array of quantiles
binomial = stats.binom.pmf(k,n,p)
print(binomial)```

Output:

```[5.98736939e-01 3.15124705e-01 7.46347985e-02 1.04750594e-02
9.64808106e-04 6.09352488e-05 2.67259863e-06 8.03789063e-08
1.58642578e-09 1.85546875e-11 9.76562500e-14]```

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