python+统计学+源码+用Python动手学统计学

[{"title":"（ 37 个子文件 1.72MB ） python+统计学+源码+用Python动手学统计学","children":[{"title":"pystat-code-2021-01-25","children":[{"title":"7-4-线性模型与神经网络.ipynb <span style='color:#111;'> 10.59KB </span>","children":null,"spread":false},{"title":"5-3-含有多个解释变量的模型.ipynb <span style='color:#111;'> 161.83KB </span>","children":null,"spread":false},{"title":"3-3-基于matplotlib-seaborn的数据可视化.ipynb <span style='color:#111;'> 272.15KB </span>","children":null,"spread":false},{"title":"3-6-正态分布及其应用.ipynb <span style='color:#111;'> 78.83KB </span>","children":null,"spread":false},{"title":"6-4-广义线性模型的评估.ipynb <span style='color:#111;'> 5.62KB </span>","children":null,"spread":false},{"title":"2-3-Python编程基础.ipynb <span style='color:#111;'> 13.18KB </span>","children":null,"spread":false},{"title":"6-5-1-poisson-regression.csv <span style='color:#111;'> 268B </span>","children":null,"spread":false},{"title":"2-4-认识numpy与pandas.ipynb <span style='color:#111;'> 23.67KB </span>","children":null,"spread":false},{"title":"6-3-logistic回归.ipynb <span style='color:#111;'> 44.46KB </span>","children":null,"spread":false},{"title":"6-1-各种概率分布.ipynb <span style='color:#111;'> 47.74KB </span>","children":null,"spread":false},{"title":"3-4-1-fish_length_100000.csv <span style='color:#111;'> 2.38MB </span>","children":null,"spread":false},{"title":"3-9-均值差的检验.ipynb <span style='color:#111;'> 5.18KB </span>","children":null,"spread":false},{"title":"3-2-3-cov.csv <span style='color:#111;'> 96B </span>","children":null,"spread":false},{"title":"3-10-1-click_data.csv <span style='color:#111;'> 78B </span>","children":null,"spread":false},{"title":"3-8-假设检验.ipynb <span style='color:#111;'> 6.01KB </span>","children":null,"spread":false},{"title":"3-1-使用Python进行描述统计：单变量.ipynb <span style='color:#111;'> 13.46KB </span>","children":null,"spread":false},{"title":"3-7-1-fish_length.csv <span style='color:#111;'> 257B </span>","children":null,"spread":false},{"title":"3-2-2-shoes.csv <span style='color:#111;'> 79B </span>","children":null,"spread":false},{"title":"3-2-1-fish_multi.csv <span style='color:#111;'> 50B </span>","children":null,"spread":false},{"title":"3-5-样本统计量的性质.ipynb <span style='color:#111;'> 143.77KB </span>","children":null,"spread":false},{"title":"3-7-参数估计.ipynb <span style='color:#111;'> 8.46KB </span>","children":null,"spread":false},{"title":"7-3-Python中的Ridge回归与Lasso回归.ipynb <span style='color:#111;'> 103.87KB </span>","children":null,"spread":false},{"title":"5-1-1-beer.csv <span style='color:#111;'> 412B </span>","children":null,"spread":false},{"title":"5-3-1-lm-model.csv <span style='color:#111;'> 2.68KB </span>","children":null,"spread":false},{"title":"3-9-1-paired-t-test.csv <span style='color:#111;'> 182B </span>","children":null,"spread":false},{"title":"7-3-1-large-data.csv <span style='color:#111;'> 101.54KB </span>","children":null,"spread":false},{"title":"5-2-方差分析.ipynb <span style='color:#111;'> 23.65KB </span>","children":null,"spread":false},{"title":"2-2-认识Jupyter-Notebook.ipynb <span style='color:#111;'> 1.51KB </span>","children":null,"spread":false},{"title":"3-10-列联表检验.ipynb <span style='color:#111;'> 3.24KB </span>","children":null,"spread":false},{"title":"2-4-1-sample_data.csv <span style='color:#111;'> 48B </span>","children":null,"spread":false},{"title":"5-1-一元回归.ipynb <span style='color:#111;'> 126.88KB </span>","children":null,"spread":false},{"title":"3-8-1-junk-food-weight.csv <span style='color:#111;'> 507B </span>","children":null,"spread":false},{"title":"6-3-1-logistic-regression.csv <span style='color:#111;'> 514B </span>","children":null,"spread":false},{"title":"3-3-2-fish_multi_2.csv <span style='color:#111;'> 119B </span>","children":null,"spread":false},{"title":"3-4-用Python模拟抽样.ipynb <span style='color:#111;'> 49.26KB </span>","children":null,"spread":false},{"title":"6-5-泊松回归.ipynb <span style='color:#111;'> 25.72KB </span>","children":null,"spread":false},{"title":"3-2-使用Python进行描述统计：多变量.ipynb <span style='color:#111;'> 10.50KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]