(1)
額外在WDI中再找2個或以上的影響變數，利用相關係數、圖形、ANOVA，或複迴歸等任意方法分析描述他們各自與人均GDP為正向還是負向關係
(此部分需整理到 ``期末0611第四題作答檔案.RMD” R
Markdown檔案中並生成html檔上傳至tronclass) 。
install.packages("WDI")
## 程式套件 'WDI' 開啟成功，MD5 和檢查也透過
##
## 下載的二進位程式套件在
## C:\Users\ariel\AppData\Local\Temp\RtmpCINQNj\downloaded_packages 裡
install.packages("ggplot2")
## 程式套件 'ggplot2' 開啟成功，MD5 和檢查也透過
##
## 下載的二進位程式套件在
## C:\Users\ariel\AppData\Local\Temp\RtmpCINQNj\downloaded_packages 裡
install.packages("dplyr")
## 程式套件 'dplyr' 開啟成功，MD5 和檢查也透過
##
## 下載的二進位程式套件在
## C:\Users\ariel\AppData\Local\Temp\RtmpCINQNj\downloaded_packages 裡
library(WDI)
library(ggplot2)
library(dplyr)
countries <- c("USA", "CHN")
indicators <- c("NY.GDP.PCAP.CD", # 人均GDP
"SL.UEM.TOTL.ZS", # 失业率
"SP.DYN.CBRT.IN", # 出生率
"FP.CPI.TOTL") # 通货膨胀率
data <- WDI(country = countries, indicator = indicators, start = 2013, end = 2022)
names(data) <- c("iso2c", "country", "year", "gdp_per_capita", "unemployment_rate", "birth_rate", "inflation_rate")
head(data)
## iso2c country year gdp_per_capita unemployment_rate birth_rate inflation_rate
## 1 China CN CHN 2013 7020.386 4.60 13.03
## 2 China CN CHN 2014 7636.074 4.63 13.83
## 3 China CN CHN 2015 8016.446 4.65 11.99
## 4 China CN CHN 2016 8094.390 4.56 13.57
## 5 China CN CHN 2017 8817.046 4.47 12.64
## 6 China CN CHN 2018 9905.406 4.31 10.86
## NA
## 1 111.1580
## 2 113.2941
## 3 114.9221
## 4 117.2206
## 5 119.0881
## 6 121.5589
data <- na.omit(data)
summary(data)
## iso2c country year gdp_per_capita
## Length:20 Length:20 Length:20 Min. :2013
## Class :character Class :character Class :character 1st Qu.:2015
## Mode :character Mode :character Mode :character Median :2018
## Mean :2018
## 3rd Qu.:2020
## Max. :2022
## unemployment_rate birth_rate inflation_rate NA
## Min. : 7020 Min. :3.650 Min. : 6.77 Min. :106.8
## 1st Qu.: 9633 1st Qu.:4.442 1st Qu.:10.89 1st Qu.:112.1
## Median :33006 Median :4.615 Median :11.70 Median :117.2
## Mean :35818 Mean :4.949 Mean :11.32 Mean :118.4
## 3rd Qu.:60637 3rd Qu.:5.070 3rd Qu.:12.43 3rd Qu.:124.5
## Max. :76330 Max. :8.050 Max. :13.83 Max. :134.2
cor_matrix <- cor(data[, c("gdp_per_capita", "unemployment_rate", "birth_rate", "inflation_rate")])
print(cor_matrix)
## gdp_per_capita unemployment_rate birth_rate inflation_rate
## gdp_per_capita 1.0000000 0.1551991 -0.20087213 -0.77952588
## unemployment_rate 0.1551991 1.0000000 0.21250353 0.13677080
## birth_rate -0.2008721 0.2125035 1.00000000 0.03630817
## inflation_rate -0.7795259 0.1367708 0.03630817 1.00000000
ggplot(data, aes(x = unemployment_rate, y = gdp_per_capita, color = country)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Unemployment Rate vs GDP per Capita", x = "Unemployment Rate (%)", y = "GDP per Capita (current US$)")
ggplot(data, aes(x = birth_rate, y = gdp_per_capita, color = country)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Birth Rate vs GDP per Capita", x = "Birth Rate (per 1,000 people)", y = "GDP per Capita (current US$)")
ggplot(data, aes(x = inflation_rate, y = gdp_per_capita, color = country)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Inflation Rate vs GDP per Capita", x = "Inflation Rate (%)", y = "GDP per Capita (current US$)")
lm_unemployment <- lm(gdp_per_capita ~ unemployment_rate, data = data)
summary(lm_unemployment)
##
## Call:
## lm(formula = gdp_per_capita ~ unemployment_rate, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.791 -2.240 0.000 2.260 4.885
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.017e+03 1.118e+00 1804.306 <2e-16 ***
## unemployment_rate 1.667e-05 2.501e-05 0.667 0.514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.991 on 18 degrees of freedom
## Multiple R-squared: 0.02409, Adjusted R-squared: -0.03013
## F-statistic: 0.4443 on 1 and 18 DF, p-value: 0.5135
lm_birth_rate <- lm(gdp_per_capita ~ birth_rate, data = data)
summary(lm_birth_rate)
##
## Call:
## lm(formula = gdp_per_capita ~ birth_rate, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6860 -2.4075 -0.4072 2.7172 4.5165
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2020.1380 3.1040 650.81 <2e-16 ***
## birth_rate -0.5330 0.6127 -0.87 0.396
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.966 on 18 degrees of freedom
## Multiple R-squared: 0.04035, Adjusted R-squared: -0.01296
## F-statistic: 0.7568 on 1 and 18 DF, p-value: 0.3958
lm_inflation <- lm(gdp_per_capita ~ inflation_rate, data = data)
summary(lm_inflation)
##
## Call:
## lm(formula = gdp_per_capita ~ inflation_rate, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.162 -1.169 -0.230 1.172 4.108
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2031.481 2.682 757.56 < 2e-16 ***
## inflation_rate -1.235 0.234 -5.28 5.08e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.896 on 18 degrees of freedom
## Multiple R-squared: 0.6077, Adjusted R-squared: 0.5859
## F-statistic: 27.88 on 1 and 18 DF, p-value: 5.085e-05
lm_multiple <- lm(gdp_per_capita ~ unemployment_rate + birth_rate + inflation_rate, data = data)
summary(lm_multiple)
##
## Call:
## lm(formula = gdp_per_capita ~ unemployment_rate + birth_rate +
## inflation_rate, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.164 -1.094 -0.386 1.174 2.982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.034e+03 2.858e+00 711.750 < 2e-16 ***
## unemployment_rate 3.408e-05 1.435e-05 2.374 0.0304 *
## birth_rate -6.335e-01 3.515e-01 -1.802 0.0904 .
## inflation_rate -1.290e+00 2.071e-01 -6.231 1.2e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.663 on 16 degrees of freedom
## Multiple R-squared: 0.7319, Adjusted R-squared: 0.6817
## F-statistic: 14.56 on 3 and 16 DF, p-value: 7.768e-05