Xitoyning Yaim qiymati va yillar kesimidagi bog’liqligini polinominal usulda bosqichma bosqich aks ettiring

Seminar topshiriq

Xitoyning YaIM qiymati va yillar kesimidagi bog’liqligini polinominal usulda bosqichma bosqich aks ettiring.

import numpy as np

import matplotlib.pyplot as plt

# Given data

years = [1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014]
values = [59184116488.9977, 49557050182.9631, 46685178504.3274, 50097303271.0232, 59062254890.1871, 69709153115.3147, 75879434776.1831, 72057028559.6741, 69993497892.3132, 78718820477.9257, 91506211306.3745, 98562023844.1813, 112159813640.376, 136769878359.668, 142254742077.706, 161162492226.686, 151627687364.405, 172349014326.931, 148382111520.192, 176856525405.729, 189649992463.987, 194369049090.197, 203549627211.606, 228950200773.115, 258082147252.256, 307479585852.339, 298805792971.544, 271349773463.863, 310722213686.031, 345957485871.286, 358973230048.399, 381454703832.753, 424934065934.066, 442874596387.119, 562261129868.774, 732032045217.766, 860844098049.121, 958159424835.341, 1025276902078.73, 1089447108705.89, 1205260678391.96, 1332234719889.82, 1461906487857.92, 1649928718134.59, 1941745602165.09, 2268598904116.28, 2729784031906.09, 3523094314820.92, 4558431073438.22, 5059419738267.41, 6039658508485.59, 7492432097810.11, 8461623162714.07, 9490602600148.49, 10354831729340.4]

# Perform polynomial regression of degree 2

coefficients = np.polyfit(years, values, 2)
polynomial = np.poly1d(coefficients)

# Generate x values for the regression line

x_regression = np.linspace(min(years), max(years), 100)

# Generate y values for the regression line

y_regression = polynomial(x_regression)

# Plot the original data points

plt.scatter(years, values, color='blue', label='Data Points')

# Plot the regression line

plt.plot(x_regression, y_regression, color='red', label='Regression Line')

# Label the axes and add a legend

plt.xlabel('Year')
plt.ylabel('Value')
plt.legend()

# Show the plot

plt.show()

import numpy as np

import matplotlib.pyplot as plt

# Given data

years = [1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014]
values = [59184116488.9977, 49557050182.9631, 46685178504.3274, 50097303271.0232, 59062254890.1871, 69709153115.3147, 75879434776.1831, 72057028559.6741, 69993497892.3132, 78718820477.9257, 91506211306.3745, 98562023844.1813, 112159813640.376, 136769878359.668, 142254742077.706, 161162492226.686, 151627687364.405, 172349014326.931, 148382111520.192, 176856525405.729, 189649992463.987, 194369049090.197, 203549627211.606, 228950200773.115, 258082147252.256, 307479585852.339, 298805792971.544, 271349773463.863, 310722213686.031, 345957485871.286, 358973230048.399, 381454703832.753, 424934065934.066, 442874596387.119, 562261129868.774, 732032045217.766, 860844098049.121, 958159424835.34, 1025276902078.73, 1089447108705.89, 1205260678391.96, 1332234719889.82, 1461906487857.92, 1649928718134.59, 1941745602165.09, 2268598904116.28, 2729784031906.09, 3523094314820.9, 4558431073438.2, 5059419738267.41, 6039658508485.59, 7492432097810.11, 8461623162714.07, 9490602600148.49, 10354831729340.4]

# Convert data to numpy arrays

x = np.array(years)
y = np.array(values)

# Perform polynomial regression

degree = 3 # Choose the degree of the polynomial
coefficients = np.polyfit(x, y, degree)
polynomial = np.poly1d(coefficients)

# Generate predicted values using the polynomial

x_pred = np.linspace(min(x), max(x), 100)
y_pred = polynomial(x_pred)

# Plot the original data and the polynomial regression curve

plt.scatter(x, y, label='Data')
plt.plot(x_pred, y_pred, color='red', label='Polynomial Regression')
plt.xlabel('Year')
plt.ylabel('Value')
plt.title('Polynomial Regression')
plt.legend()
plt.show()



419-guruh talabasi HAKIMOV MUZAFFAR