8.2 Baby weights, Part II

a-) Baby weight = 120.7 - 1.93 * Parity

b-) The first born baby will be 120.07 - 1.93 * 0 = 120.07 and the not first born baby will be 120.07 - 1.93 * 1 = 118.14

c-)P value is greater than .05 wich nit significal relation between them.

8.4 Absenteeism

a-) Days Absent = 18.93 - 9.11 * eth + 3.10 * sex + 2.15 * lrn

b-) eth slope show a decrease (-9.11) when is not aboriginal
sex slope show an increase (3.10) when is male
lrn slope show an increase (2.15) when is slow leaner

c-) 18.93 - 9.11 * 0 + 3.10 * 1 + 2.15 * 1 = 24.18
Residual = 2 - 24.18 = -22.18

d-) R2 = 1 - 240.57 / 264.17 = 0.089

R2adj = 1 - 240.57 / 264.17 * (146-1) / (146-3-1) = 0.070

8.8 Absenteeism, Part II

The “No learner Status” should be removed because it has the highest R2

8.16 Challenger disaster, Part I.

a-) we can see that temperature between 53-63 has a high damage O-rings

b-)

c-) log(p/(1-p)) = 11.663 - 0.2162 * Temp

d-) on the model we see justified concern on O-rings.

8.18 Challenger disaster, Part II.

library(ggplot2)

#a-)
func = function(temp){
  
  return ((exp(11.6630 - 0.2162 * temp) / (1+exp(11.6630 - 0.2162 * temp))))
 
}

print(paste("The values 51, 53, 55", func(51),func(53),func(55)))
## [1] "The values 51, 53, 55 0.654029738456095 0.550922829727926 0.443245647107059"
temp = c(51,53,55,57,59,61,63,65,67,69,71)
prob = sapply(temp,func)


#b-)
df = data.frame(temp,prob)
ggplot(df) + 
  geom_point(aes(x = temp, y = prob,colour = temp)) +
  geom_line(data = df, aes(x = temp, y = prob))