Math Problem Statement
Housing Costs at the Turn of the Century The following table shows the average price of a two-bedroom apartment in a downtown area from 1994 to 2004 (t = 0 represents 1994). Year t 0 2 4 6 8 10 Price p ($ million) 0.58 0.60 0.80 1.15 1.40 1.80 (a) Use technology to obtain the linear regression line and correlation coefficient r, with all coefficients rounded to two decimal places.
Solution
It seems like I can’t do more advanced data analysis right now. Please try again later.
If you have any questions or need further details on this topic, feel free to ask! Here are some follow-up questions you might find useful:
- How do we interpret the slope of a regression line in this context?
- What does the correlation coefficient tell us about the relationship between time and housing prices?
- What would a negative correlation coefficient mean for this data?
- How could we use this regression line to predict future housing prices?
- How does rounding the coefficients affect the accuracy of predictions?
Tip: Linear regression assumes a straight-line relationship between the variables, so if your data shows curvature, a different model might be more appropriate.
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Math Problem Analysis
Mathematical Concepts
Linear Regression
Correlation Coefficient
Statistics
Formulas
Linear regression formula: y = mx + b
Correlation coefficient formula: r = Cov(X,Y) / (σX * σY)
Theorems
Least Squares Method
Suitable Grade Level
Grades 10-12