Viny与CS的邂逅

虽千万人吾往矣

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1. Chapter1: Getting started with linear regression
2. Chapter2: The basics of simple linear regression
3. Chapter 3: Equivalence of the null linear model and the one-sample t-test(零线性模型的等价性和单样本t检验)
4. [1] 52.87671
5. [1] 18.67799
6. [1] 146
7. [1] 1.97646
8. [1] 49.82150 55.93193
9. [1] 1.545802
10. [1] -1.373583
11. [1] 0.171691
12. [1] 0.171691
13. 2.5 % 97.5 %
14. (Intercept) 49.8215 55.93193
15.
16. One Sample t-test
17.
18. data: Stats20x.df$Exam
19. t = -1.3736, df = 145, p-value = 0.1717
20. alternative hypothesis: true mean is not equal to 55
21. 95 percent confidence interval:
22. 49.82150 55.93193
23. sample estimates:
24. mean of x
25. 52.87671
26. Chapter 4: Fitting curves(曲线) with the linear model
27.
28. Call:
29. lm(formula = Exam ~ Assign + I(Assign^2), data = Stats20x.df)
30.
31. Residuals:
32. Min 1Q Median 3Q Max
33. -32.541 -9.149 1.273 9.087 41.116
34.
35. Coefficients:
36. Estimate Std. Error t value Pr(>|t|)
37. (Intercept) 28.41396 5.99081 4.743 5.05e-06 ***
38. Assign -0.68172 1.07242 -0.636 0.525999
39. I(Assign^2) 0.16102 0.04545 3.542 0.000536 ***
40. ---
41. Signif. codes: 0 '' 0.001 '' 0.01 '' 0.05 '.' 0.1 ' ' 1
42.
43. Residual standard error: 12.65 on 143 degrees of freedom
44. Multiple R-squared: 0.5477, Adjusted R-squared: 0.5414
45. F-statistic: 86.59 on 2 and 143 DF, p-value: < 2.2e-16
46. Chapter 5: Linear models with a 2-level categorical (factor) explanatory variable（具有2级分类（因素）解释变量的线性模型）
47. Chapter 6: Multiplicative（乘法） linear models（指数模型）
48. clothes
49. 15 999.99
50. (Intercept)
51. 70.38002
52. 2.5 % 97.5 %
53. (Intercept) 61.53073 80.502
54. [1] 85
55. 2.5% 97.5%
56. 50 100
57. Chapter7: Power law linear models（幂律线性模型，也就是幂指数模型）
58. [1] "diameter" "height" "volume" "logvolume" "logheight"
59. [6] "logdiameter"
60. 1.117123 1.982650
61. logheight logdiameter
62. 0.20443706 0.07501061
63. logheight logdiameter
64. 0.5729066 -0.2313018
65. logheight logdiameter
66. 0.5712805 0.8187623
67. Chapter8: Linear models with both numeric and factor explanatory variables(具有数值和因子解释变量的线性模型)
68. [1] "standard" "ri" "temp" "owner"
69. standard ri temp owner
70. 1 B1 1.52912 39.97 RB
71. 2 B1 1.52912 39.90 RB
72. 3 B1 1.52912 39.97 RB
73. 4 B1 1.52912 39.70 RB
74. 5 B1 1.52912 39.76 RB
75. 6 B2 1.52381 54.50 RB
76. Chapter9: Linear models with both numeric and factor explanatory variables（具有数值和因子解释变量的线性模型）
77. F-statistic: 33.82 on 3 and 26 DF, p-value: 3.986e-09
78. 2.5 % 97.5 %
79. (Intercept) 35.0127936 68.9140989
80. IQ 0.1647361 0.4665482
81. method1 -15.6759248 -4.0799363
82. method3 -29.7496781 -18.3246250
83. [1] 0
84. Warning in [<-.factor(*tmp*, water.df$Location != "N", value =
85. structure(c(NA, : invalid factor level, NA generated
86. [1] S N S N N N N S N S S S N S S N N N N S N N N N N S N S N S S S N N S
87. [36] S S N S N N N N N S S N N N N N S N S S S N N N S N
88. Levels: N S
89.
90. Call:
91. Chapter 10: Multiple linear regression models（多元线性回归模型）
92. [1] 148 294
93. [1] 295 353
94. sx rk yr dg yd sl
95. 1 male full 25 doctorate 35 36350
96. 2 male full 13 doctorate 22 35350
97. 3 male full 10 doctorate 23 28200
98. 4 female full 7 doctorate 27 26775
99. 5 male full 19 masters 30 33696
100. 6 male full 16 doctorate 21 28516
101. sx rk yr dg yd sl
102. 47 female assistant 2 doctorate 6 16150
103. 48 female assistant 2 doctorate 2 15350
104. 49 male assistant 1 doctorate 1 16244
105. 50 female assistant 1 doctorate 1 16686
106. 51 female assistant 1 doctorate 1 15000
107. 52 female assistant 0 doctorate 2 20300
108. F-statistic: 43.46 on 6 and 45 DF, p-value: < 2.2e-16
109.
110. Call:
111. lm(formula = log.sl ~ rk + log.yr, data = salary.df)
112.
113. Residuals:
114. Min 1Q Median 3Q Max
115. -0.15389 -0.06344 -0.02122 0.03568 0.26648
116.
117. Coefficients:
118. Estimate Std. Error t value Pr(>|t|)
119. (Intercept) 9.65710 0.03610 267.503 < 2e-16 ***
120. rkassociate 0.22719 0.03653 6.220 1.16e-07 ***
121. rkfull 0.43264 0.03670 11.789 8.85e-16 ***
122. log.yr 0.08661 0.01970 4.396 6.08e-05 ***
123. ---
124. Signif. codes: 0 '' 0.001 '' 0.01 '' 0.05 '.' 0.1 ' ' 1
125.
126. Residual standard error: 0.09886 on 48 degrees of freedom
127. Multiple R-squared: 0.85, Adjusted R-squared: 0.8407
128. F-statistic: 90.7 on 3 and 48 DF, p-value: < 2.2e-16
129. 2.5 % 97.5 %
130. (Intercept) 14537.893426 16809.266030
131. rkassociate 1.166196 1.350709
132. rkfull 1.431690 1.659352
133. log.yr 1.048118 1.134534
134. 2.5 % 97.5 %
135. 1.033112 1.091432
136. Chapter 11: Linear models with a single factor explanatory variable having three or more levels (One-way analysis of variance)具有三个或更多水平的单因素解释变量的线性模型（单因素方差分析）
137. Chapter 12: Linear models with two explanatory factor variables (Two-way analysis of variance)具有两个解释因子变量的线性模型（双向方差分析）
138. [1] 10
139. [1] 9.1
140. Multiple R-squared: 0.3878, Adjusted R-squared: 0.3749
141. F-statistic: 29.98 on 3 and 142 DF, p-value: 4.452e-15
142.
143. Call:
144. lm(formula = Cover ~ Time * Reef, data = Reef.df)
145.
146. Residuals:
147. Min 1Q Median 3Q Max
148. -7.3282 -0.9609 -0.4308 0.9049 7.9257
149. Chapter 13: Modelling count data using the Poisson distribution（使用 Poisson 分布对计数数据进行建模）
150. Chapter 14: Poisson modelling of count data: Two examples（针对GLM-Poisson的两个例题）
151. Locn Magnitude Freq
152. 1 SC 5.25 32
153. 2 SC 5.50 27
154. 3 SC 5.75 10
155. 4 SC 6.00 9
156. 5 SC 6.25 6
157. 6 SC 6.50 6
158. 7 SC 6.75 3
159. 8 SC 7.00 2
160. 9 SC 7.25 2
161. 10 WA 5.25 13
162. 11 WA 5.50 6
163. 12 WA 5.75 2
164. 13 WA 6.00 1
165. 14 WA 6.25 0
166. 15 WA 6.50 0
167. 16 WA 6.75 1
168. 17 WA 7.00 0
169. 18 WA 7.25 0
170. [1] 0.8770025
171. Waiting for profiling to be done...
172. 2.5 % 97.5 %
173. 0.1374743 0.3082437
174. Waiting for profiling to be done...
175. 2.5 % 97.5 %
176. 86.25257 69.17563
177. Waiting for profiling to be done...
178. 2.5 % 97.5 %
179. 0.009077661 0.140175445
180. Waiting for profiling to be done...
181. 2.5 % 97.5 %
182. 99.09223 85.98246
183. Residual deviance: 14.678 on 14 degrees of freedom
184. AIC: 69.143
185.
186. Number of Fisher Scoring iterations: 5
187. [1] 0.4005141
188. Chapter 15: Modelling proportion data using the binomial distribution（用二项式分布对比例数据进行建模）
189. [1] 0.9133009
190. Chapter 16: Analysis of contingency tables（列联表分析）
191. Outcome
192. Gender In Out
193. F 557 1278
194. M 1198 1493
195. [1] 0.001144215
196.
197. Pearson’s Chi-squared test
198.
199. data: Berk.tbl
200. X-squared = 92.205, df = 1, p-value < 2.2e-16
201. Estimate Std. Error t value Pr(>|t|)
202. 0.09987009 0.15686847 0.63664858 0.55235491

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数据分析方法

发表于 2025-03-03 更新于 2025-12-21 分类于学习阅读次数：

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