2022학년도 1학기 다변량분석 과제물

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2022학년도 1학기 다변량분석 과제물에 대한 보고서 자료입니다.

목차

2022년 기출문제
방송대 다변량교재
연습문제 1장 (p.38) 4번
연습문제 2장 (p.75) 1번
연습문제 3장 (p.122) 2번

본문내용

_csv(\"c:/data/favoritesujects.csv\")
del fasu[\"SUBJECT\"]
fasu.head(3)
fasu.describe()
pip install factor-analyzer
from factor_analyzer import FactorAnalyzer
fa = FactorAnalyzer(rotation=None)
fa.fit(fasu)
fa.loadings_
fa.get_communalities()
fa.get_uniquenesses()
fa.get_factor_variance()
ev, v = fa.get_eigenvalues()
ev
plt.scatter(range(1, fasu.shape[1]+1), ev)
plt.plot(range(1, fasu.shape[1]+1), ev)
plt.title(\'Scree Plot\')
plt.xlabel(\'Factors\')
plt.ylabel(\'Eigenvalues\')
plt.grid()
plt.show()
import seaborn as sns
sns.heatmap(fa.loadings_, cmap=\"Blues\", annot=True, fmt=\'.2f\')
plt.show()
fa_varimax = FactorAnalyzer(n_factors=3, rotation=\'varimax\', method=\'principal\')
fa_varimax.fit(fasu)
fa_varimax.loadings_
fa_varimax.get_communalities()
fa_varimax.get_uniquenesses()
fa_varimax.get_factor_variance()
fa_promax = FactorAnalyzer(n_factors=3, rotation=\'promax\', method=\'principal\')
fa_promax.fit(fasu)
fa_promax.loadings_
fa_promax.get_communalities()
fa_promax.get_uniquenesses()
fa_promax.get_factor_variance()
결과값
fasu.describe()
Out[35]:
BIO GEO CHEM ALG CALC STAT
count 300.000000 300.000000 300.000000 300.000000 300.000000 300.000000
mean 2.353333 2.170000 2.236667 3.050000 3.063333 2.936667
std 1.227536 1.229963 1.272551 1.174207 1.127042 1.258811
min 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
25% 1.000000 1.000000 1.000000 2.000000 2.000000 2.000000
50% 2.000000 2.000000 2.000000 3.000000 3.000000 3.000000
75% 3.000000 3.000000 3.000000 4.000000 4.000000 4.000000
max 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000
fa.loadings_
Out[40]:
array([[ 0.74882246, -0.42933797, -0.05488136],
[ 0.70544419, -0.37010412, 0.0742006 ],
[ 0.71514214, -0.48614684, -0.05121496],
[ 0.50346786, 0.58989815, -0.02414972],
[ 0.68048177, 0.72014763, -0.11910879],
[ 0.46358192, 0.30891564, 0.255808 ]])
fa.get_communalities()
Out[41]:
array([0.74807813, 0.6401343 , 0.75039 , 0.60204292, 0.99585496,
0.3757748 ])
fa.get_uniquenesses()
Out[42]:
array([0.25192187, 0.3598657 , 0.24961 , 0.39795708, 0.00414504,
0.6242252 ])
fa.get_factor_variance()
Out[43]:
(array([2.50125839, 1.51966821, 0.09134851]),
array([0.4168764 , 0.25327803, 0.01522475]),
array([0.4168764 , 0.67015443, 0.68537918]))
ev
Out[45]:
array([2.78672826, 1.77778185, 0.63046377, 0.33664744, 0.2621086 ,
0.20627009])
fa_varimax.loadings_
Out[50]:
array([[0.89950632, 0.08902106, 0.06390784],
[0.86353099, 0.08339965, 0.12452531],
[0.90728477, 0.02896738, 0.03270613],
[0.04162009, 0.94295906, 0.11660795],
[0.12169482, 0.89234356, 0.26432819],
[0.12433885, 0.29319907, 0.9453823 ]])
fa_varimax.get_communalities()
Out[51]:
array([0.82112059, 0.76814782, 0.82507445, 0.90450144, 0.88095605,
0.99517353])
fa_varimax.get_uniquenesses()
Out[52]:
array([0.17887941, 0.23185218, 0.17492555, 0.09549856, 0.11904395,
0.00482647])
fa_varimax.get_factor_variance()
Out[53]:
(array([2.40996505, 1.78713387, 0.99787495]),
array([0.40166084, 0.29785565, 0.16631249]),
array([0.40166084, 0.69951649, 0.86582898]))

키워드

R분석,   deug,   longley