The dataset contains the following columns:

• SubID: Subject ID (artwork identifier)
• medium: Type of artwork (e.g., sculpture, painting)
• exhibWeeks: Number of weeks the artwork was exhibited before auction
• price: Sale price of the artwork (in thousands)

To answer the research question, we will perform a linear regression analysis to predict the sale price (price) based on the number of weeks the artwork was exhibited (exhibWeeks).

We'll proceed with the following steps:

1. Perform linear regression analysis.
2. Report the estimated slope, test statistic, p-value, and the proportion of the variation that is not explained by the model (R-squared complement).
3. Make a decision based on the hypothesis test.

Let's perform the regression analysis.Here are the results of the linear regression analysis:

• Estimated slope: $0.6109$
• Test statistic: $10.47$
• p-value: $4.512 \times 10^{-19}$
• Percentage of variation not explained by the model: $54.8\%$

Decision:

Since the p-value is extremely small (much less than 0.05), we reject the null hypothesis. This means that the number of weeks the artwork was exhibited before the auction is a significant predictor of the sale price.

Do you want more details, or do you have any questions? Here are five related questions to expand on this analysis:

1. How do we interpret the slope of 0.6109 in the context of this problem?
2. Can we visualize the relationship between exhibition weeks and price with a scatterplot?
3. What would be the predicted price for an artwork exhibited for 60 weeks?
4. How can we improve the model to explain more of the variation in sale price?
5. What are the assumptions of linear regression, and how can we verify them?

Tip: A low R-squared suggests the need for additional variables or a non-linear model to improve prediction.