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1 SQL Server 2008 一危 覦 ODBC誤 #
--drop table 煙 create table 煙 ( 糾 int , 蟆一 int , int ); insert 煙 values (85, 3, 65) , (74, 7, 50) , (76, 5, 55) , (90, 1, 65) , (85, 3, 55) , (87, 3, 70) , (94, 1, 65) , (98, 2, 70) , (81, 4, 55) , (91, 2, 70) , (76, 3, 50) , (74, 4, 55);
ODBC誤 . 一危 覲 企 sql2008襦 .
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2 R 伎 れ蠏覿 #
> # SQL Server 2008 磯 R貊: れ蠏覿
> library("TSodbc")
> con <-odbcConnect(dsn = "sql2008")
> options(TSconnection = con)
> #襷 一危郁 襷る select * from 煙 tablesample(1 percent) 螳 襷 .
> result = try(dbGetQuery(con, paste("SELECT 糾, , 蟆一 FROM 煙")))
> model = lm(糾 ~ + 蟆一, result)
> summary(model)
Call:
lm(formula = 糾 ~ + 蟆一, data = result)
Residuals:
Min 1Q Median 3Q Max
-5.348 -2.274 -1.276 2.954 5.673
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53.6832 14.1808 3.786 0.00431 **
0.6073 0.1984 3.062 0.01353 *
蟆一 -1.9346 0.9144 -2.116 0.06348 .
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Residual standard error: 3.721 on 9 degrees of freedom
Multiple R-squared: 0.8289, Adjusted R-squared: 0.7909
F-statistic: 21.8 on 2 and 9 DF, p-value: 0.0003544
> anova(model) #, 螻燕, 螻燕蠏 F螳,
Analysis of Variance Table
Response: 糾
Df Sum Sq Mean Sq F value Pr(>F)
1 541.69 541.69 39.1297 0.0001487 ***
蟆一 1 61.97 61.97 4.4762 0.0634803 .
Residuals 9 124.59 13.84
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
> dbDisconnect(con)
[1] TRUE
> options(TSconnection = NULL)
> odbcCloseAll()
>
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3 蟆郁骸 伎 #
> summary(model)
Call:
lm(formula = 糾 ~ + 蟆一, data = result)
Residuals:
Min 1Q Median 3Q Max
-5.348 -2.274 -1.276 2.954 5.673
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53.6832 14.1808 3.786 0.00431 **
0.6073 0.1984 3.062 0.01353 *
蟆一 -1.9346 0.9144 -2.116 0.06348 .
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Residual standard error: 3.721 on 9 degrees of freedom
Multiple R-squared: 0.8289, Adjusted R-squared: 0.7909
F-statistic: 21.8 on 2 and 9 DF, p-value: 0.0003544
- 蠏讌 y=糾, x1=, x2=蟆一 企朱, y = 53.6832 + 0.6073x1 + -1.9346x2 朱 豢.
- -1.9346x2朱 襷企(-)螳 螳讌る 蟆 蟆一 襦 糾煙 觜讌る 蟆 覩誤.
- '硫1=0'企朱 蠏覓願れ T 蟆糾 3.062企, 願 P螳 0.01353朱 譴 0.05 蠏覓願れ 蠍郁蟆 . 讀, 覩誤 螳企.
- 蟆一螻 R2 anova(覿磯) 蟆郁骸 R2 = (541.69 + 61.97)/(541.69 + 61.97 + 124.59) = 0.8289186405願, 譟一 R2螳 0.7909 企. 讀, 糾煙 煙螻 蟆一 2螳 覲 83%螳 る伎.
- F 蟆糾 21.8企, F覿 留 = 0.05, v1=2, v2=9 螳 4.26企. 讀, 21.8 > 4.26 企襦 蠏覓願(硫1=硫2=0) 蠍郁. 讀, 覩誤 螳企. p-value螳 る襦 F覿襯 覲 . 譴 0.05 > 0.0003544 企襦 蠏覓願れ 蠍郁覃 .
- p-value螳 朱 襴所れ 讌讌螻, 貉れ覃 蠏覓願れ 讌讌. 0.0003544 蠏覓願れ 襷り 螳 蟆曙 詞伎 蟆糾覲企 蠏豪 蟆郁骸螳 襯企. 讀, 糾 豺 蠏手碓 蟆一 る(1譬 る)螳 襯.
- 譴 0.05朱 蟆 95%襷 蠏 螳蟾 朱 螳螻, 蠏螳 覃襴 伎 5% 觜朱 螳る 蟆企.
> anova(model) #, 螻燕, 螻燕蠏 F螳,
Analysis of Variance Table
Response: 糾
Df Sum Sq Mean Sq F value Pr(>F)
1 541.69 541.69 39.1297 0.0001487 ***
蟆一 1 61.97 61.97 4.4762 0.0634803 .
Residuals 9 124.59 13.84
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
| 螻燕 | 螻燕蠏 | F觜 | ||
| 蠏 | 603.66 | 11 | 301.83 | 21.802950 |
| 谿 | 124.59 | 9 | 13.84 | |
| 螻 | 728.25 | 11 |
- F 蟆糾 21.8企, F覿 留 = 0.05, v1=2, v2=9 螳 4.26企. 讀, 21.8 > 4.26 企襦 蠏覓願(硫1=硫2=0) 蠍郁. 讀, 覩誤 螳企.
- 蟆一螻 R2 R2 = 603.66/728.25 = 0.82891864 企.
- 襦 . ろ . 螳 讌 碁 覲危 覲語 螳襯 覩誤.
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4 れ螻旧(Multicollinearity) #
れ螻旧(Multicollinearity) ル 螳 蟯螳 襯 襷. 讀, 覲 x襯 觜手 蠏覿 x 蟯螳 覓 R螳 譟磯 蟆 る 蟆 襷. 讀, 譬覲 譟磯 蟯螻. 讀, "螳 襯 襷 葛" "蟲襷 螳 襷 螻螳れ 襷れ觜譴 " 襦 煙朱 . れ螻旧 覲螳 蟯蟯螻螳 るジ 覲覲企 麹 蟆曙一 . 覲企 覦覯 螻給郁骸 蟯螻覓語 螻給壱 覿覿 谿瑚.
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5 fRegression-package #
#library(fRegression)
#regFit(formula, data, use = "lm", ...)
#use = c("lm", "rlm", "glm","gam", "ppr", "nnet", "polymars")
model = rlm(NetAMT ~ Playtime + LoginCnt, data, use = "lm")
#regFit(formula, data, use = "lm", ...)
#use = c("lm", "rlm", "glm","gam", "ppr", "nnet", "polymars")
model = rlm(NetAMT ~ Playtime + LoginCnt, data, use = "lm")
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