0412

輸入資料

##日期
DATE <- c("324", "325", "326", "327", "328", "329", "330", "331", "401", "402", "403", "404", "405", "406", "407", "408", "409", "410", "411", "412")

##本土案例數
Domestic <- c(15, 14, 21, 83, 34, 33, 56, 87, 104, 160, 183, 133, 216, 281, 382, 384, 442, 431, 439, 551)

##境外移入數
Oversea <- c(124, 122, 82, 120, 93, 63, 107, 152, 132, 244, 97, 142, 65, 78, 149, 123, 136, 144, 191, 112)

#高雄本土確診個案數
Kaohsiung <- c(13, 0, 4, 2, 2, 4, 1, 4, 2, 9, 1, 0, 9, 37, 59, 58, 38, 26, 29, 61)

資料處理

##create a dataframe
cvdta <- data.frame(DATE, Domestic, Oversea, Kaohsiung)

cvdta$DATE <- as.factor(cvdta$DATE)

畫海外和本土的散佈圖

scatterplot(Domestic ~ Oversea, 
            data = cvdta, 
            smooth = F)

用ggplot畫海外和本土的圖

ggplot(aes(x = Oversea, y = Domestic), data = cvdta) +
  geom_point() +
  geom_smooth(method = lm, se = T) +
  theme_bw()

## `geom_smooth()` using formula 'y ~ x'

簡單線性回歸模型 Fitting the simple linear regression model for 海外和本土

cvmod <- lm(Domestic ~ Oversea, data = cvdta)

summary(cvmod)

## 
## Call:
## lm(formula = Domestic ~ Oversea, data = cvdta)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -187.66 -136.95  -97.39  156.82  360.77 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   74.210    122.743   0.605    0.553
## Oversea        1.036      0.940   1.102    0.285
## 
## Residual standard error: 174.5 on 18 degrees of freedom
## Multiple R-squared:  0.0632, Adjusted R-squared:  0.01115 
## F-statistic: 1.214 on 1 and 18 DF,  p-value: 0.285

畫Kaohsiung和本土的散佈圖

scatterplot(Kaohsiung ~ Domestic, 
            data = cvdta, 
            smooth = F)

用ggplot畫Kaohsiung和本土的圖

ggplot(aes(x = Domestic, y = Kaohsiung), data = cvdta) +
  geom_point() +
  geom_smooth(method = lm, se = T) +
  theme_bw()

## `geom_smooth()` using formula 'y ~ x'

簡單線性回歸模型 Fitting the simple linear regression model for Kaohsiung和本土

cvmod2 <- lm(Kaohsiung ~ Domestic, data = cvdta)

summary(cvmod2)

## 
## Call:
## lm(formula = Kaohsiung ~ Domestic, data = cvdta)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -16.053  -6.770  -1.640   5.462  22.115 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -3.4002     3.9495  -0.861    0.401    
## Domestic      0.1055     0.0149   7.077 1.34e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.4 on 18 degrees of freedom
## Multiple R-squared:  0.7356, Adjusted R-squared:  0.7209 
## F-statistic: 50.08 on 1 and 18 DF,  p-value: 1.342e-06

結論

從ggplot和散佈圖來看，海外和本土的灰色地帶較寬，資料相關性較分散，所以相關程度可能較低。 相比之下，高雄本土確診數和本土的資料相關程度較高。

從簡單線性回歸模型的數字來看，海外和本土這一組的R平方為0.0632，高雄本土和本土這一組的R平方為0.7356，後者比前者更接近1，解釋力越強大。此外，前者這一組的P值未達顯著水準，後者這一組的P值<0.001，具有統計上的顯著意義。 於是，海外的確診數無法預測本土的確診數，但本土的確診數可以預測高雄本土的確診數。