# WAA06

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## データ解析基礎論 a weekly assignment A06

#### WAA06.1

```# データのinputと成形
dat\$condition = factor(dat\$condition, levels(dat\$condition)[2:1])
medicineA <- dat[dat\$medicine == "Medicine A",]
# lm
medicineA.lm <- lm(blood.pressure ~ condition,data = medicineA)
summary(medicineA.lm)
# 出力結果
Call:
lm(formula = blood.pressure ~ condition, data = medicineA)

Residuals:
Min     1Q Median     3Q    Max
-9.76  -3.88   0.18   4.12  11.12

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)   130.8800     0.7014  186.61   <2e-16 ***
conditionpost -26.1200     0.9919  -26.33   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.959 on 98 degrees of freedom
Multiple R-squared:  0.8762,    Adjusted R-squared:  0.8749
F-statistic: 693.5 on 1 and 98 DF,  p-value: < 2.2e-16
```

#### WAA06.2

```# データ成形
post <- dat[dat\$condition == "post",]
# lm
dat.lm <- lm(blood.pressure ~ medicine,data = post)
# plot
plot(as.numeric(post\$medicine)-1,post\$blood.pressure,pch = 19,
ylab = "blood pressure",xlab = "medicine type",xaxt = "n",
xlim=c(-0.5,1.5))
axis(1,c(0,1),c("Medicine A","Medicine B"))
abline(dat.lm,col = "red")
```

```summary(dat.lm)
# 出力結果
Call:
lm(formula = blood.pressure ~ medicine, data = post)

Residuals:
Min      1Q  Median      3Q     Max
-11.180  -4.760  -0.470   4.385  17.820

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)        104.7600     0.8742  119.84   <2e-16 ***
medicineMedicine B  25.4200     1.2363   20.56   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 6.181 on 98 degrees of freedom
Multiple R-squared:  0.8118,    Adjusted R-squared:  0.8099
F-statistic: 422.8 on 1 and 98 DF,  p-value: < 2.2e-16
```

#### WAA06.3

```dat<-read.table("http://www.matsuka.info/data_folder/tdkPATH01.txt",header=T)
plot(dat)
```

##### All
```varianceAll.lm <- lm(grade ~ study + absence + knowledge + interest,dat)
summary(varianceAll.lm)
# 出力結果
Call:
lm(formula = grade ~ study + absence + knowledge + interest,
data = dat)

Residuals:
Min      1Q  Median      3Q     Max
-17.900  -5.146  -0.587   6.524  13.202

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 54.94744    9.97293   5.510 9.83e-07 ***
study        0.08355    0.03667   2.279  0.02660 *
absence     -0.58676    0.12810  -4.580 2.70e-05 ***
knowledge    0.36450    0.12650   2.882  0.00563 **
interest     0.86450    1.56025   0.554  0.58177
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 7.463 on 55 degrees of freedom
Multiple R-squared:  0.7608,    Adjusted R-squared:  0.7434
F-statistic: 43.74 on 4 and 55 DF,  p-value: < 2.2e-16
```
##### 3変数
```variance3.lm <- lm(grade ~ study + absence + knowledge,dat)
summary(variance3.lm)
# 出力結果

Call:
lm(formula = grade ~ study + absence + knowledge, data = dat)

Residuals:
Min       1Q   Median       3Q      Max
-17.9720  -4.9393  -0.6443   6.6919  13.2094

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 55.70510    9.81742   5.674 5.12e-07 ***
study        0.09713    0.02711   3.583 0.000713 ***
absence     -0.61701    0.11516  -5.358 1.64e-06 ***
knowledge    0.39900    0.10943   3.646 0.000585 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 7.417 on 56 degrees of freedom
Multiple R-squared:  0.7595,    Adjusted R-squared:  0.7466
F-statistic: 58.95 on 3 and 56 DF,  p-value: < 2.2e-16
```
##### 2変数
```variance2.lm <- lm(grade ~ absence + knowledge,dat)
summary(variance2.lm)
# 出力結果

Call:
lm(formula = grade ~ absence + knowledge, data = dat)

Residuals:
Min       1Q   Median       3Q      Max
-17.8179  -5.2150   0.2177   4.5910  18.1371

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 76.89733    8.61051   8.931 2.00e-12 ***
absence     -0.88513    0.09619  -9.201 7.25e-13 ***
knowledge    0.29979    0.11634   2.577   0.0126 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 8.151 on 57 degrees of freedom
Multiple R-squared:  0.7044,    Adjusted R-squared:  0.694
F-statistic:  67.9 on 2 and 57 DF,  p-value: 8.246e-16
```
##### 1変数
```variance1.lm <- lm(grade ~ absence ,dat)
summary(variance1.lm)
# 出力結果
Call:
lm(formula = grade ~ absence, data = dat)

Residuals:
Min       1Q   Median       3Q      Max
-17.5207  -6.5420  -0.0378   6.5188  19.6460

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 98.31673    2.35253   41.79  < 2e-16 ***
absence     -0.99020    0.09126  -10.85 1.38e-15 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 8.538 on 58 degrees of freedom
Multiple R-squared:  0.6699,    Adjusted R-squared:  0.6642
F-statistic: 117.7 on 1 and 58 DF,  p-value: 1.384e-15
```