Data used in Examples 9.2 and 9.9

Apolipop

## Format

A data frame/tibble with 15 observations on two variables

coffee

number of cups of coffee per day

apolipB

level of apoliprotein B

## References

Kitchens, L. J. (2003) Basic Statistics and Data Analysis. Pacific Grove, CA: Brooks/Cole, a division of Thomson Learning.

## Examples


plot(apolipB ~ coffee, data = Apolipop)

linmod <- lm(apolipB ~ coffee, data = Apolipop)
summary(linmod)
#>
#> Call:
#> lm(formula = apolipB ~ coffee, data = Apolipop)
#>
#> Residuals:
#>    Min     1Q Median     3Q    Max
#> -5.067 -2.483  0.800  2.017  4.933
#>
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)
#> (Intercept)   13.500      2.089   6.461 2.13e-05 ***
#> coffee         4.567      0.630   7.249 6.46e-06 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 3.45 on 13 degrees of freedom
#> Multiple R-squared:  0.8017,	Adjusted R-squared:  0.7864
#> F-statistic: 52.55 on 1 and 13 DF,  p-value: 6.465e-06
#>
summary(linmod)\$sigma
#> [1] 3.450381
anova(linmod)
#> Analysis of Variance Table
#>
#> Response: apolipB
#>           Df Sum Sq Mean Sq F value    Pr(>F)
#> coffee     1 625.63  625.63  52.552 6.465e-06 ***
#> Residuals 13 154.77   11.91
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova(linmod)[2, 3]^.5
#> [1] 3.450381
par(mfrow = c(2, 2))
plot(linmod)

par(mfrow = c(1, 1))