Obtenha para os valores abaixo, o modelo de previsão, usando o método dos mínimos quadrados, para os valores calculados:
| X | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Y | 1.7 | 2.3 | 3.2 | 3.6 | 4.5 | 5.3 | 6.0 | 6.5 |
X=c(1,2,3,4,5,6,7,8)
Y=c(1.7,2.3,3.2,3.6,4.5,5.3,6.0,6.5)
tab1=data.frame(X,Y)##
## Call:
## lm(formula = Y ~ X)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.18452 -0.08155 0.02143 0.09911 0.12143
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.96071 0.09397 10.22 5.10e-05 ***
## X 0.70595 0.01861 37.94 2.24e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1206 on 6 degrees of freedom
## Multiple R-squared: 0.9958, Adjusted R-squared: 0.9952
## F-statistic: 1439 on 1 and 6 DF, p-value: 2.24e-08