Questions
Familiarize yourself with the codebook for the Saratoga Houses dataset (codebook below) and then import/load the Saratoga Houses dataset.
Question 1: Construct a regression equation that allows you to predict price from number of bathrooms. How can you specifically explain the relationship between number of bathrooms and house price?
Question 2: Does the regression equation allow us to establish causation between number of bathrooms and price of house?
Question 3: Why might living area be considered as a possible confounding variable for the relationship between number of bathrooms and house price?
Question 4: Construct a multiple regression equation to test whether number of bathrooms are significantly associated with price after controlling for living area. After controlling for living area, is there a significant association between number of bathrooms and house price?
Question 5: Based on your findings – Does living area confound the relationship between number of bathrooms and house price?
Familiarize yourself with the codebook for the Financial Wellness dataset (codebook below) and then import/load the data set.
Question 6: Find the proportion of participants who are financially healthy based on the parent’s highest level of degree attainment.
Question 7: Construct the appropriate model that aims to predict whether someone is financially healthy based on their parent’s highest level of degree attainment. How does education relate to someone’s financial wellness?
Question 8: Construct the appropriate model that aims to predict whether someone is financially healthy based on their parent’s highest level of degree attainment and corresponding financial socialization. Does highest level of degree attainment significantly relate to someone’s financial well being after controlling for financial socialization?
Question 9: What can be said about how financial socialization relates to a their child’s future financial wellness?
CODEBOOK: Saratoga Houses
The dataset contains information on 1,063 houses in Saratoga County, New York, USA in 2006.
The variables in the dataset include:
- Price: Amount of house in US dollars
- Living.Area: Square feet of house (in SAS, the variable is living_area)
- Baths: Number of baths
- Bedrooms: Number of bedrooms
- Fireplace: “yes” or “no”
- Acres: Number of acres on the property
- Age: Age of house in years
CODEBOOK: Financial Wellness
The dataset contains information on 100 individual’s financial well-being status
The variables in the dataset include:
- ID
- fin_wb: a marker that designates whether the participant is considered financially healthy (1) or not (0)
- parent_education: the participants parent’s highest level level of degree attainment (Less than High School, High School, College Degree, Graduate Degree)
- parent_fin_soc: A score from 0-7 that represents the level of financial socialization the parent gave their child growing up. A 0 represents no financial socialization took place and a 7 represents all actions of financial socialization took place.