Baby weights, Part I. (9.1, p. 350) The Child Health and Development Studies investigate a range of topics. One study considered all pregnancies between 1960 and 1967 among women in the Kaiser Foundation Health Plan in the San Francisco East Bay area. Here, we study the relationship between smoking and weight of the baby. The variable smoke is coded 1 if the mother is a smoker, and 0 if not. The summary table below shows the results of a linear regression model for predicting the average birth weight of babies, measured in ounces, based on the smoking status of the mother.

The variability within the smokers and non-smokers are about equal and the distributions are symmetric. With these conditions satisfied, it is reasonable to apply the model. (Note that we don’t need to check linearity since the predictor has only two levels.)

  1. Write the equation of the regression line.

\(\widehat{weight}\) = 123.05 - 8.94*smoke

  1. Interpret the slope in this context, and calculate the predicted birth weight of babies born to smoker and non-smoker mothers.

The average birth weight of babies is 8.94 ounces higher for mothers who donot smoke.

Predicted weight for mother’s who do smoke \(\widehat{weight}\) = 123.05 - 8.94*1 = 114.11

Predicted weight for mother’s who don’t smoke \(\widehat{weight}\) = 123.05 - 8.94*0 = 123.05

  1. Is there a statistically significant relationship between the average birth weight and smoking?

Seeing the table above, p-value for smoke is 0 which is less than 0.05. It indicates strong evidence against the null hypothesis and shows significant relationship between the average birth weight and smoking.

Absenteeism, Part I. (9.4, p. 352) Researchers interested in the relationship between absenteeism from school and certain demographic characteristics of children collected data from 146 randomly sampled students in rural New South Wales, Australia, in a particular school year. Below are three observations from this data set.

The summary table below shows the results of a linear regression model for predicting the average number of days absent based on ethnic background (eth: 0 - aboriginal, 1 - not aboriginal), sex (sex: 0 - female, 1 - male), and learner status (lrn: 0 - average learner, 1 - slow learner).

  1. Write the equation of the regression line.

\(\widehat{daysabsent}\) = 18.93 - 9.11 * eth + 3.10 * sex + 2.15 * lrn

  1. Interpret each one of the slopes in this context.

Non aboriginal students seems to be absent in the class 9.11 days less than the aborginal students. Male students seems to be absent in the class 3.10 days more than the female students. Slow learners seems to be absent in the class 2.5 days more than the avearage learners.

  1. Calculate the residual for the first observation in the data set: a student who is aboriginal, male, a slow learner, and missed 2 days of school.
# student who is aboriginal, eth => 0
# a male sex => 1
# a slow learner => 1

days_absent <- 18.93 - 9.11*0 + 3.10*1 + 2.15*1
residual <- 2 - days_absent
residual
## [1] -22.18
  1. The variance of the residuals is 240.57, and the variance of the number of absent days for all students in the data set is 264.17. Calculate the \(R^2\) and the adjusted \(R^2\). Note that there are 146 observations in the data set.
n <- 146
# number of predictors 3
k <- 3
var_res <- 240.57
var_out <- 264.17

# R-Square
r_2 <- 1 - (var_res/var_out)
r_2
## [1] 0.08933641
# Adjusted R-Square
adjr_2 <- 1 - (var_res/(n-k-1))/(var_out/(n-1))
adjr_2
## [1] 0.07009704

Absenteeism, Part II. (9.8, p. 357) Exercise above considers a model that predicts the number of days absent using three predictors: ethnic background (eth), gender (sex), and learner status (lrn). The table below shows the adjusted R-squared for the model as well as adjusted R-squared values for all models we evaluate in the first step of the backwards elimination process.

Which, if any, variable should be removed from the model first?

Per Backward elimination, variables are eliminated one-at-a-time from the model until we cannot improve the adjusted R2. The strategy within each elimination step is to eliminate the variable that leads to the largest improvement in adjusted R2. Here lrn should be removed first since the model without lrn has the highest adjusted R2 of 0.0723.

Challenger disaster, Part I. (9.16, p. 380) On January 28, 1986, a routine launch was anticipated for the Challenger space shuttle. Seventy-three seconds into the flight, disaster happened: the shuttle broke apart, killing all seven crew members on board. An investigation into the cause of the disaster focused on a critical seal called an O-ring, and it is believed that damage to these O-rings during a shuttle launch may be related to the ambient temperature during the launch. The table below summarizes observational data on O-rings for 23 shuttle missions, where the mission order is based on the temperature at the time of the launch. Temp gives the temperature in Fahrenheit, Damaged represents the number of damaged O-rings, and Undamaged represents the number of O-rings that were not damaged.

  1. Each column of the table above represents a different shuttle mission. Examine these data and describe what you observe with respect to the relationship between temperatures and damaged O-rings.

From above table, it seems if temperature lowers, there is a higher chance of damaged O-rings

  1. Failures have been coded as 1 for a damaged O-ring and 0 for an undamaged O-ring, and a logistic regression model was fit to these data. A summary of this model is given below. Describe the key components of this summary table in words.

slope: With every additional temperature, the damaged O-ring decreases by 0.2162. intercept: If temperature becomes ), avg damaged O-ring is 11.6630. The relationship is statistically significant since the p-value is less than 0.05

  1. Write out the logistic model using the point estimates of the model parameters.

\(\log\frac{\hat{p}}{1 - \hat{p}}\) = 11.6630 - 0.2162 * Temperature

  1. Based on the model, do you think concerns regarding O-rings are justified? Explain.

Yes, temperature is significant to determine damaged O-rings and eventually for successful space shuttle launch.

Challenger disaster, Part II. (9.18, p. 381) Exercise above introduced us to O-rings that were identified as a plausible explanation for the breakup of the Challenger space shuttle 73 seconds into takeoff in 1986. The investigation found that the ambient temperature at the time of the shuttle launch was closely related to the damage of O-rings, which are a critical component of the shuttle. See this earlier exercise if you would like to browse the original data.

\begin{center} \end{center}

  1. The data provided in the previous exercise are shown in the plot. The logistic model fit to these data may be written as \[\begin{align*} \log\left( \frac{\hat{p}}{1 - \hat{p}} \right) = 11.6630 - 0.2162\times Temperature \end{align*}\]

where \(\hat{p}\) is the model-estimated probability that an O-ring will become damaged. Use the model to calculate the probability that an O-ring will become damaged at each of the following ambient temperatures: 51, 53, and 55 degrees Fahrenheit. The model-estimated probabilities for several additional ambient temperatures are provided below, where subscripts indicate the temperature:

\[\begin{align*} &\hat{p}_{57} = 0.341 && \hat{p}_{59} = 0.251 && \hat{p}_{61} = 0.179 && \hat{p}_{63} = 0.124 \\ &\hat{p}_{65} = 0.084 && \hat{p}_{67} = 0.056 && \hat{p}_{69} = 0.037 && \hat{p}_{71} = 0.024 \end{align*}\]

logistic_model <- function(temps) {
  for (temp in temps) {
    trans_p <- 11.6630 - 0.2162*temp
    p_hat <- exp(trans_p) / (1+exp(trans_p))
    print(paste("O-ring failure probability is " , round(p_hat, 3), " for temperature as ", temp))
  }
}

temps <- c(51,53,55)
logistic_model(temps)
## [1] "O-ring failure probability is  0.654  for temperature as  51"
## [1] "O-ring failure probability is  0.551  for temperature as  53"
## [1] "O-ring failure probability is  0.443  for temperature as  55"
  1. Add the model-estimated probabilities from part~(a) on the plot, then connect these dots using a smooth curve to represent the model-estimated probabilities.
library(ggplot2)
## 
## Attaching package: 'ggplot2'
## The following object is masked from 'package:openintro':
## 
##     diamonds
df_comb <- NULL

#plot model based on temperatures
plot_logistic_model <- function(temps) {
  for (temp in temps) {
    trans_p <- 11.6630 - 0.2162*temp
    p_hat <- exp(trans_p) / (1+exp(trans_p))
    df <- data.frame(temp, p_hat)
    df_comb <- rbind(df_comb, df)
  }
  ggplot(df_comb, aes(temp, p_hat)) + geom_point() + geom_line()
}

temps <- c(51,53,55,57,59,61,63,65,67,69,71)
plot_logistic_model(temps)

  1. Describe any concerns you may have regarding applying logistic regression in this application, and note any assumptions that are required to accept the model’s validity.

Below are the keyconditions fto fit a logistic regression model:1. Each outcome Yi is independent of the other outcomes. 2. Each predictor xi is linearly related to logit(pi) if all other predictors are held constant.For first I assume independence. For second sample size is too small to verify linearity. Also I notice model is too simple for this type of system.