Question

1. People in the aerospace industry believe the cost of a space project is a function of the mass of the major object being sent into space. Use the following data to develop a regression model to predict the cost of a space project by the mass of the space object. Determine r² and s_e.

Weight (tons)	Cost ($ millions)
1.897	$ 53.6
3.019	184.0
0.453	6.4
0.996	23.5
1.058	33.4
2.100	110.4
2.382	104.6

*(Do not round the intermediate values. Round your answers to 4 decimal places.)
**(Round the intermediate values to 4 decimal places. Round your answer to 3 decimal places.)


ŷ = enter a number rounded to 4 decimal places  * + enter a number rounded to 4 decimal places * x
r² = enter a number rounded to 3 decimal places  **
s_e = enter a number rounded to 3 decimal places  **

2. People in the aerospace industry believe the cost of a space project is a function of the mass of the major object being sent into space. Use the following data to develop a regression model to predict the cost of a space project by the mass of the space object. Determine r² and s_e.

Weight (tons)	Cost ($ millions)
1.897	$ 53.6
3.019	184.5
0.453	6.4
0.977	23.5
1.058	33.0
2.100	110.4
2.388	104.6

*(Do not round the intermediate values. Round your answers to 4 decimal places.)
**(Round the intermediate values to 4 decimal places. Round your answer to 3 decimal places.)


ŷ = enter a number rounded to 4 decimal places  * + enter a number rounded to 4 decimal places * x
r² = enter a number rounded to 3 decimal places  **
s_e = enter a number rounded to 3 decimal places  **

Answer 1

Coefficient of Determination(R-squared):
It gives the measure of how close the data points are to the best fit line. In other words, it gives the proportion of variability in dependent variable that can be explained by the independent variable. Higher the Rsquared value, better the model is.

c)

2)



Coefficient of Determination(R-squared):
It gives the measure of how close the data points are to the best fit line. In other words, it gives the proportion of variability in dependent variable that can be explained by the independent variable. Higher the Rsquared value, better the model is.

c)

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