It is thought that x and y are related by a power relationship, that is, a function of the form y=ax^b.

Perform power regression on these data, and then choose the one option describing a situation which these data could represent.

You should assume in each case that appropriate units have been chosen for x and y, and that the values are quoted to an appropriate accuracy;you should consider only whether or not the power function you have found is appropriate to the situation.

Options

A. X is the time since a pebble was dropped down a well, and y is the distance it has fallen.

B. x is the distance a pebble has fallen, and y is the time since it was dropped.

C. x is the height and y is the radius of a 200ml cylindrical can.

D. x is the radius and y is the height of a 200lm cylindrical can.

E. x is the time and y is the distance from home for someone travelling towards their home at constant speed.

F. x is the time taken to walk between two villages, and y is the average speed of the walker.

G. x is the age of a wooden carving, and y is the amount of radioactive carbon in the carving.

H. x is the amount of radioactive carbon in a carving, and y is its age.

... estimated regression equation is given by, Strength = -28.99 + 0.2117 * Force + 0.4983 * Power + 0.0811 * Temp + 0.326 * Time The regression coefficients can ...

... first unit cost, weight, power output, and ... familiar with performing linear regression using Microsoft ... following univariate and multivariate regressions using a ...

... answering these questions on T-tests and Regressions: 1) You ... Solutions: 1. R^2 is calculated as regression sums of ... in the cost is being explained by the power. ...

... prepare a report on the following question: Does the purchasing power parity hold ... the intercept and the slope coefficient, and interpret the regression results ...

... ANOVA df SS MS F Significance F Regression 1 7167.874452 7167.874 100.215706 4.81315E ... C2:C27) Squared deviation of weight from its mean New value =POWER(B2-$B ...

... log-log regression is also called "power regression." I found a calculator online (http://www.xuru.org/rt/PowR.asp) and found that the regression equation is: ...

... You could use regression analysis to compare the effect of horse power of the engine and the engine weight separately on the MPG of the engine. ...

... If we have the inputs such as power, alpha and minimum detectable odds ratio (OR) for logistic regression with a single binary covariate or two covariates and ...

... MPG = 53.5094 - 0.2666 *Horse Power For a unit increase in horse power, the MPG ... Thus 76.06 % variability in MPG can be explained by the regression model. ...