The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the r
egression line for predicting a woman's bone density based on her age. Note: The correlation coefficient may or may not be statistically significant for the data given. In practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant.Age 35 42 45 66 67
Bone Density356 339 338 323 315The following scatterplot is for your information:Bone Density Scatterplot (2)Question 1: Use the equation of the regression line for predicting a woman's bone density based on her age. (round to 2 decimals).Question 2: Enter the estimated value of y when x = 43 (round to 2 decimals).Question 3: Substitute the values you found in steps 1 and 2 into the equation for the regression line (or find the regression equation in the session output) to find the estimated linear model. According to this model, if the value of the independent variable is increased by one unit, then find the change in the dependent variable y (round to 2 decimals).Question 4: Enter the estimated value of y when x = 43 (round to 2 decimals).Question 5: Enter the error prediction when x = 43 (round to 2 decimals).Question 6: Enter the value of the coefficient of determination (as a decimal, not a percentage) and round to 2 decimals.Question 7: Enter the p-value for the regression model (round to 3 decimals)Question 8: Based on the p-value, I would consider the given model in making predictionsA) True B) False C) There is not enough information to make a decision.
Bone Density356 339 338 323 315The following scatterplot is for your information:Bone Density Scatterplot (2)Question 1: Use the equation of the regression line for predicting a woman's bone density based on her age. (round to 2 decimals).Question 2: Enter the estimated value of y when x = 43 (round to 2 decimals).Question 3: Substitute the values you found in steps 1 and 2 into the equation for the regression line (or find the regression equation in the session output) to find the estimated linear model. According to this model, if the value of the independent variable is increased by one unit, then find the change in the dependent variable y (round to 2 decimals).Question 4: Enter the estimated value of y when x = 43 (round to 2 decimals).Question 5: Enter the error prediction when x = 43 (round to 2 decimals).Question 6: Enter the value of the coefficient of determination (as a decimal, not a percentage) and round to 2 decimals.Question 7: Enter the p-value for the regression model (round to 3 decimals)Question 8: Based on the p-value, I would consider the given model in making predictionsA) True B) False C) There is not enough information to make a decision.
1 answer:
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It is already in the simplest form. It can be written as 0.216 in decimal form (rounded to 6 decimal places).
Answer:
72
Step-by-step explanation:
As you can see the top - view is a rectangle of 2 by 9 dimensions. Respectively the right - side view is a rectangle of 2 by 4 dimensions. The common dimension among both rectangles would be 2, making this rectangular prism have dimensions 2 by 4 by 9.
Therefore the rectangular prism will have a volume of 2 4
9
2 4
9 = 8( 9 ) = 72 cubic units
Solution : 72 unit cubes