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  3. Causal Inference with R - Regression
  • 1

    Regressions 1: Introduction to Regression As Causality

    This chapter will introduce you to using regression analysis to find causal effects

  • 2

    Regressions 2: Using Regression to Estimate Causal Effects

    This chapter will introduce you to using regression analysis to find causal effects

  • 3

    Regressions 3: Introduction to Matching Methods

    This chapter will introduce you to using matching methods to find causal effects

Initializing

Exercicio

Reading a Bivariate Regression Table

The following table shows the regression results from the previous questions about eGulf's pricing algorithm for used wePhones. The coefficient for age reflects the slope in the previous line graph, and it suggests that for each additional year (or unit of age), the expected sales price of WePhones decreases by $61.99. The coefficient for the intercept matches the Y-axis intercept in the previous line graph. It indicates the predicted price sold for a WePhone at 0 years old (or more generally, it shows the value of an outcome when all other independent variables are at their reference point).

Variable Coefficient (SE)
Age -61.99 (7.28)
Intercept 592.56 (24.42)

To determine the predicted sales price of a WePhone with a given age, we can start with the initial intercept value and add to it the total change in price for the age of the phone. In other words, based on this regression model the formula for predicting the price of a WePhone sold on eGulf is:

Sales Price = 592.56 + Age * -61.99

Based on this formula, what is a valid predicted price for a WePhone that is exactly two years old? You can use the R console as a scratch space to do your calculations.

Instruções

50 XP

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