1.3 Practice Questions based on moving Average.ipynb

Kernel: Python 3 (ipykernel)

1. Determine the moving average

• Determine the moving average with a window size of 2 for the list [30, 29, 35, 33, 31, 37, 39, 40, 41, 45].

• Using the list [100, 95, 90, 85, 80, 75, 70, 65, 60, 55], calculate the moving average with a window size of 6.

• Find the moving average for the list of integers [4, 8, 12, 16, 20, 24, 28, 32, 36, 40] with a window size of 3.

• Calculate the moving average using a window size of 4 for the list [5, 10, 15, 20, 25, 30, 35, 40, 45, 50].

• For the list [11, 22, 33, 44, 55, 66, 77, 88, 99, 110], find the moving average with a window size of 2.

• Determine the moving average with a window size of 3 for the list [9, 18, 27, 36, 45, 54, 63, 72, 81, 90].

import numpy as np

# List of integers
data_q7 = np.array([5, 10, 15, 20, 25, 30, 35, 40, 45, 50])

# Window size
window_size_q7 = 4

# Calculate the moving average
moving_avg_q7 = np.convolve(data_q7, np.ones(window_size_q7)/window_size_q7, mode='valid')

print("Moving Average (Window Size 4) for Question 7:", moving_avg_q7)

Moving Average (Window Size 4) for Question 7: [12.5 17.5 22.5 27.5 32.5 37.5 42.5]

Objective:

2. Perform an Augmented Dickey-Fuller (ADF) test on a dataset containing attrition data to determine whether the attrition rate is stationary.

• Dataset: You are provided with a dataset named attrition_data.csv that contains the following columns:

• Date: The date of the recorded attrition rate (format: YYYY-MM-DD).

• Attr_rate: The attrition rate for the corresponding date. Instructions:

• Load the Data:

• Import necessary libraries such as pandas, statsmodels, and matplotlib.

• Load the dataset into a pandas DataFrame.

• Ensure the Date column is parsed as datetime objects and set it as the index of the DataFrame.

• Visualize the Data:

• Plot the Attr_rate over time to visually inspect if the series appears to be stationary or non-stationary.

• Perform the ADF Test:

• Use the adfuller function from the statsmodels library to perform the ADF test on the Attr_rate series.

• Extract and print the following from the test results:

• ADF Statistic

• p-value

• Number of lags used

• Number of observations used

• Critical values for 1%, 5%, and 10% levels

Interpret the Results:

Based on the p-value and the critical values, determine whether to reject the null hypothesis of the ADF test. Discuss whether the attrition rate series is stationary or not.

# Step 1: Load the Data
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.stattools import adfuller

# Load the dataset
data = pd.read_csv('attrition_data.csv')
data.head()

# Step 1: Load the Data
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.stattools import adfuller

# Load the dataset
data = pd.read_csv('attrition_data.csv')

# Step 2: Visualize the Data
plt.figure(figsize=(10, 6))
plt.plot(data['Attrition Rate'], label='Attrition Rate')
plt.title('Attrition Rate Over Time')
plt.xlabel('Date')
plt.ylabel('Attrition Rate')
plt.legend()
plt.show()

# Step 3: Perform the ADF Test
adf_test = adfuller(data['Attrition Rate'])
adf_statistic = adf_test[0]
p_value = adf_test[1]
used_lag = adf_test[2]
n_obs = adf_test[3]
critical_values = adf_test[4]

print(f'ADF Statistic: {adf_statistic}')
print(f'p-value: {p_value}')
print(f'Number of lags used: {used_lag}')
print(f'Number of observations used: {n_obs}')
print('Critical Values:')
for key, value in critical_values.items():
    print(f'   {key}: {value}')

# Step 4: Interpret the Results
if p_value < 0.05:
    print('Reject the null hypothesis: The series is stationary.')
else:
    print('Fail to reject the null hypothesis: The series is non-stationary.')

ADF Statistic: -0.10321535298059625
p-value: 0.9491173286548007
Number of lags used: 4
Number of observations used: 8
Critical Values:
   1%: -4.6651863281249994
   5%: -3.3671868750000002
   10%: -2.802960625
Fail to reject the null hypothesis: The series is non-stationary.