Consider the monthly rent (Rent in $) of a home in Ann Arbor, Michigan, as a function of the number of bedrooms (Beds), the number of bathrooms (Baths), and square footage (Sqft). Epicture Click here for the Excel Data File a. Estimate: Rent = 69 + 61Beds + 62Baths + 63Sqft + ɛ. (Round your answers to 2 decimal places.) Rent = Bed + Bath + Sqft b-1. Which of the predictor variables is most likely to be the cause of changing variability? O Beds because it is likely correlated with Rent. O Baths because its distribution tends to be highly skewed. O Sqft because the variability of rent tends to increase with square footage. b-2. Discuss the consequences of changing variability (heteroskedasticity). O OLS estimators and their standard errors are both biased. O OLS estimators are biased but their standard errors are unbiased. O OLS estimators are unbiased but their standard errors are biased.
Consider the monthly rent (Rent in $) of a home in Ann Arbor, Michigan, as a function of the number of bedrooms (Beds), the number of bathrooms (Baths), and square footage (Sqft). Epicture Click here for the Excel Data File a. Estimate: Rent = 69 + 61Beds + 62Baths + 63Sqft + ɛ. (Round your answers to 2 decimal places.) Rent = Bed + Bath + Sqft b-1. Which of the predictor variables is most likely to be the cause of changing variability? O Beds because it is likely correlated with Rent. O Baths because its distribution tends to be highly skewed. O Sqft because the variability of rent tends to increase with square footage. b-2. Discuss the consequences of changing variability (heteroskedasticity). O OLS estimators and their standard errors are both biased. O OLS estimators are biased but their standard errors are unbiased. O OLS estimators are unbiased but their standard errors are biased.
Transcribed Image Text:Consider the monthly rent (Rent in $) of a home in Ann Arbor, Michigan, as a function of the number of bedrooms (Beds), the number of bathrooms (Baths), and square footage (Sqft).
[Image: Click here for the Excel Data File]
a. Estimate: \( \text{Rent} = \theta_0 + \theta_1 \text{Beds} + \theta_2 \text{Baths} + \theta_3 \text{Sqft} + \varepsilon \). (Round your answers to 2 decimal places.)
\[ \text{Rent} = \]
\[ + \text{Bed} + \]
\[ \text{Bath} + \]
\[ \text{Sqft} \]
b-1. Which of the predictor variables is most likely to be the cause of changing variability?
- Beds because it is likely correlated with Rent.
- Baths because its distribution tends to be highly skewed.
- Sqft because the variability of rent tends to increase with square footage.
b-2. Discuss the consequences of changing variability (heteroskedasticity).
- OLS estimators and their standard errors are both biased.
- OLS estimators are biased but their standard errors are unbiased.
- OLS estimators are unbiased but their standard errors are biased.
Transcribed Image Text:The table presents data on rental properties, detailing the rent cost alongside the number of bedrooms (Beds), bathrooms (Baths), and square footage (Sqft) for both one-section and two-section listings. Below is the data outlined in two columns:
### Column 1
- **Rent**: Monthly rent in dollars.
- **Beds**: Number of bedrooms.
- **Baths**: Number of bathrooms.
- **Sqft**: Square footage of the property.
### Column 2
- **Rent**: Monthly rent in dollars for different-sized properties.
- **Beds**: Number of bedrooms.
- **Baths**: Number of bathrooms.
- **Sqft**: Square footage of the property.
#### Detailed Data:
| Rent | Beds | Baths | Sqft |
|-------|------|-------|------|
| 645 | 1 | 1 | 500 |
| 675 | 1 | 1 | 648 |
| 760 | 1 | 1 | 700 |
| 800 | 1 | 1 | 903 |
| 820 | 1 | 1 | 817 |
| 850 | 2 | 1 | 920 |
| 855 | 1 | 1 | 900 |
| 859 | 1 | 1 | 886 |
| 900 | 1 | 1.5 | 1000 |
| 905 | 2 | 1 | 920 |
| 905 | 2 | 1 | 876 |
| 929 | 2 | 1 | 920 |
| 960 | 2 | 1 | 975 |
| 975 | 2 | 2 | 1100 |
| 990 | 1 | 1.5 | 940 |
| 995 | 2 | 1 | 1000 |
| 1029 | 2 | 2 | 1299 |
| 1039 | 2 | 2 | 1164 |
| 1049 | 2 | 2 | 1180 |
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![Consider the monthly rent (Rent in $) of a home in Ann Arbor, Michigan, as a function of the number of bedrooms (Beds), the number of bathrooms (Baths), and square footage (Sqft).
[Image: Click here for the Excel Data File]
a. Estimate: \( \text{Rent} = \theta_0 + \theta_1 \text{Beds} + \theta_2 \text{Baths} + \theta_3 \text{Sqft} + \varepsilon \). (Round your answers to 2 decimal places.)
\[ \text{Rent} = \]
\[ + \text{Bed} + \]
\[ \text{Bath} + \]
\[ \text{Sqft} \]
b-1. Which of the predictor variables is most likely to be the cause of changing variability?
- Beds because it is likely correlated with Rent.
- Baths because its distribution tends to be highly skewed.
- Sqft because the variability of rent tends to increase with square footage.
b-2. Discuss the consequences of changing variability (heteroskedasticity).
- OLS estimators and their standard errors are both biased.
- OLS estimators are biased but their standard errors are unbiased.
- OLS estimators are unbiased but their standard errors are biased.](https://content.bartleby.com/qna-images/question/80f5a0b5-fa3f-4609-a3a3-be6affb5f07a/18d3255d-0426-4832-95ba-0097741bec00/pdi93m_processed.png)
