5E1. Which of the linear models below are multiple linear regressions? \[\begin{align} {μ_i = α + βx_i} \tag{1}\\ μ_i = β_xx_i + β_zz_i \tag{2} \\ μ_i = β_xx_i + β_zz_i \tag{3} \\ μ_i = α + β(x_i − z_i) \tag{4} \\ μ_i = α + β_xx_i + β_zz_i \tag{5} \\ \end{align}\]

# The first model has only one predictor, xi, so is not a multiple regression. The second model has two predictors, xi and zi, so is a multiple regression (despite having no intercept). The third model is tricky because it includes two predictor variables but only one slope term; however, algebra would apply the multiplication of β to both xi and zi, so we can consider this to be a strangely formatted multiple regression in which the slope for zi is constrained to be equal to −1 times the slope for xi. The fourth model has two predictors, xi and zi, so is a multiple regression.

5E2. Write down a multiple regression to evaluate the claim: Animal diversity is linearly related to latitude, but only after controlling for plant diversity. You just need to write down the model definition.

# The wording of the question makes it unclear which of the first two variables is the outcome and which is the predictor, but I will assume that latitude is the outcome (so as to keep the two diversity variables together).
# μi=α+βAAi+βPPi

5E3. Write down a multiple regression to evaluate the claim: Neither amount of funding nor size of laboratory is by itself a good predictor of time to PhD degree; but together these variables are both positively associated with time to degree. Write down the model definition and indicate which side of zero each slope parameter should be on.

# μi=α+βFFi+βSSi

5E4. Suppose you have a single categorical predictor with 4 levels (unique values), labeled A, B, C and D. Let Ai be an indicator variable that is 1 where case i is in category A. Also suppose Bi, Ci, and Di for the other categories. Now which of the following linear models are inferentially equivalent ways to include the categorical variable in a regression? Models are inferentially equivalent when it’s possible to compute one posterior distribution from the posterior distribution of another model. \[\begin{align} μ_i = α + β_AA_i + β_BB_i + β_DD_i \tag{1} \\ μ_i = α + β_AA_i + β_BB_i + β_CC_i + β_DD_i \tag{2} \\ μ_i = α + β_BB_i + β_CC_i + β_DD_i \tag{3} \\ μ_i = α_AA_i + α_BB_i + α_CC_i + α_DD_i \tag{4} \\ μ_i = α_A(1 − B_i − C_i − D_i) + α_BB_i + α_CC_i + α_DD_i \tag{5} \\ \end{align}\]

# The first model includes a single intercept (for category C) and slopes for A, B, and D.
# The second model is non-identifiable because it includes a slope for all possible categories (page 156).
# The third model includes a single intercept (for category A) and slopes for B, C, and D.
# The fourth model uses the unique index approach to provide a separate intercept for each category (and no slopes).
# The fifth model uses the reparameterized approach on pages 154 and 155 to multiply the intercept for category A times 1 when in category A and times 0 otherwise.

5M1. Invent your own example of a spurious correlation. An outcome variable should be correlated with both predictor variables. But when both predictors are entered in the same model, the correlation between the outcome and one of the predictors should mostly vanish (or at least be greatly reduced).

N <- 100
income <- rnorm(n = 100, mean = 0, sd = 1)
operas <- rnorm(n = N, mean = income, sd = 2)
scores <- rnorm(n = N, mean = income, sd = 1)
d <- data.frame(scores, operas, income)
pairs(d)

5M2. Invent your own example of a masked relationship. An outcome variable should be correlated with both predictor variables, but in opposite directions. And the two predictor variables should be correlated with one another.

N <- 100
rho <- 0.6
alcohol <- rnorm(n = N, mean = 0, sd = 1)
illness <- rnorm(n = N, mean = rho * alcohol, sd = sqrt(1 - rho^2))
happiness <- rnorm(n = N, mean = alcohol - illness, sd = 1)
d <- data.frame(happiness, alcohol, illness)
pairs(d)

5M3. It is sometimes observed that the best predictor of fire risk is the presence of firefighters— States and localities with many firefighters also have more fires. Presumably firefighters do not cause fires. Nevertheless, this is not a spurious correlation. Instead fires cause firefighters. Consider the same reversal of causal inference in the context of the divorce and marriage data. How might a high divorce rate cause a higher marriage rate? Can you think of a way to evaluate this relationship, using multiple regression?

5M4. In the divorce data, States with high numbers of members of the Church of Jesus Christ of Latter-day Saints (LDS) have much lower divorce rates than the regression models expected. Find a list of LDS population by State and use those numbers as a predictor variable, predicting divorce rate using marriage rate, median age at marriage, and percent LDS population (possibly standardized). You may want to consider transformations of the raw percent LDS variable.

# d <- WaffleDivorce
# 
# d$LDS <- c(0.0077, 0.0453, 0.0610, 0.0104, 0.0194, 0.0270, 0.0044, 0.0057, 0.0041, 0.0075, 0.0082, 0.0520, 0.2623, 0.0045, 0.0067, 0.0090, 0.0130, 0.0079, 0.0064, 0.0082, 0.0072, 0.0040, 0.0045, 0.0059, 0.0073, 0.0116, 0.0480, 0.0130, 0.0065, 0.0037, 0.0333, 0.0041, 0.0084, 0.0149, 0.0053, 0.0122, 0.0372, 0.0040, 0.0039, 0.0081, 0.0122, 0.0076, 0.0125, 0.6739, 0.0074, 0.0113, 0.0390, 0.0093, 0.0046, 0.1161)
# 
# d$logLDS <- log(d$LDS)
# d$logLDS.s <- (d$logLDS - mean(d$logLDS)) / sd(d$logLDS)
# simplehist(d$LDS)

5M5. One way to reason through multiple causation hypotheses is to imagine detailed mechanisms through which predictor variables may influence outcomes. For example, it is sometimes argued that the price of gasoline (predictor variable) is positively associated with lower obesity rates (outcome variable). However, there are at least two important mechanisms by which the price of gas could reduce obesity. First, it could lead to less driving and therefore more exercise. Second, it could lead to less driving, which leads to less eating out, which leads to less consumption of huge restaurant meals. Can you outline one or more multiple regressions that address these two mechanisms? Assume you can have any predictor data you need.

# To address these two mechanisms, we need variables that would capture them. For the first mechanism, a variable corresponding to time spent exercising would be a reasonable start. For the second mechanism, a variable corresponding to frequency of eating out at restaurant would be a reasonable start. We could get even fancier and try to measure things like calories burned during exercise and calories ingested at restaurants. However, we would want to be careful not to introduce multicollinearity by adding highly correlated variables. So, I would propose the following multiple regression model:
  
 # μi=α+βGGi+βEEi+βRRi