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37 lines
1.2 KiB
37 lines
1.2 KiB
3 years ago
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year <- c(26,16,20,7,22,15,29,28,17,3,1,
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16,19,13,27,4,30,8,3,12)
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annual_salary <- c(1267,887,1022,511,1193,795,
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1713,1477,991,455,324,944,1232,
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808,1296,486,1516,565,299,830)
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data <- data.frame(year, annual_salary)
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summary(data)
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plot(year, annual_salary)
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cor(year, annual_salary)
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ls <- lm(annual_salary~year, data = data)
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summary(ls)
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# Residuals:
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# Min 1Q Median 3Q Max
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# -115.282 -59.636 -3.018 37.011 215.873
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# Coefficients:
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# Estimate Std. Error t value Pr(>|t|)
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# (Intercept) 252.375 39.766 6.346 5.59e-06 ***
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# year 42.922 2.179 19.700 1.25e-13 ***
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# ---
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# Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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# Residual standard error: 89.02 on 18 degrees of freedom
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# Multiple R-squared: 0.9557, Adjusted R-squared: 0.9532
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# F-statistic: 388.1 on 1 and 18 DF, p-value: 1.25e-13
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# 1) 회귀식: y = 252.375 + 42.922x
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# 2) 총 변수 중 회귀선에 의에 95.57%(Multiple R-square)가 설명됨
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# 3) 유의수준 0.001에서 p-value가 더 작으므로 귀무가설 기각
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# 해석: 회귀계수가 0이 아니며 귀구가설이 기각되어 근무연수는 연봉에 매우 영향을 미친다고 보여짐
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