| 1) You are estimating the install hours for an electronics upgrade based on the number of components affected. The upgrade for which you are estimating affects 15 components. Given the following equation, select the correct response from each pair. Install Hours = 2.65 + 2.15 (# Components) | | The independent variable is # Components | | The independent variable is Install Hours | | The slope is 2.15 | | The slope is 2.65 | | The estimated install hours for your upgrade is 34.9 hours | | The estimated install hours for your upgrade is 56.4 hours | |
| 2) Given a one independent variable linear equation that states cost in $K, and given the following information, calculate thestandard errorand determine its meaning. [Image Description: n =10, Summation of (Y Yhat)2= 10591, Ybar=314.375] | | | If we used this equation, we could typically expect to be off by 42.01%. | | If we used this equation, we could typically expect to be off by 36.39%. | | If we used this equation, we could typically expect to be off by $42.01K. | | If we used this equation, we could typically expect to be off by $36.39K. | |
| 3) A coworker is considering the use of a log linear (power) model using weight to estimate the cost of a utility vehicle. They have performed the following calculations inlog spaceusing natural logarithms. Select the correspondingunit spaceform of this power model equation. Log Space b1= 2.680741Log Space b0= -2.778156 | | Cost = 1.268074 + 0.062153 (Weight) | | Cost = 1.064125 (Weight)1.268074 | | Cost = 0.062153 + 1.268074 (Weight) | | Cost = 0.062153 (Weight)2.680741 | |
| 4) You have calculated the followingpowermodel and associatedunit spacevalues: [Image Description: n =6, Summation of (Y Yhat)2= 412] | | | Linear equation because it has a higher standard error than the power model. | | Power equation because it has a lower standard error than the linear model. | | Linear equation because it has a lower standard error than the power model. | | Power equation because it has a higher standard error than the linear model. | |
| 5) You are estimating the cost of optical sensors based on thepower outputof the sensor. You decide to calculate the coefficient of determination (R2) as part of determining the goodness of fit of an equation. Using the preliminary calculations below, calculate the R2and determine its meaning. [Image Description: Summation of (Yi Ybar)2= 147172 Summation of (Yhat Ybar)2= 132368 Summation of (Y Yhat)2= 14804] | | | 89.94% of the variation in the power is being explained by the cost. | | 10.06% of the variation in the power is being explained by the cost. | | 10.06% of the variation in the cost is being explained by the power. | | 89.94% of the variation in the cost is being explained by the power. | |
| 6) You are trying to determine the statistical significance of an equation. Given the following information, test the slope of the equation at the 90% level of confidence. Select the correct answer out of each pair of choices. Cost = - 76.25 + 114.82 (Range) n=9 Sb1= 17.669 | | The tp is 1.895 | | The tp is 2.365 | | The tc is 4.315 | | The tc is 6.498 | | We would reject the null hypothesis | | We wouldfail to rejectthe null hypothesis | | We would consider using the equation | | We would not use the equation | |
| 7) You are estimating the cost ($K) of optical sensors based on the power output of the sensor. Using the preliminary calculations from a data set of 8 sensors, determine the equation of the line. (Round your intermediate calculations to 3 decimal places) Y = 2575 X=680 XY=241400 X2=62600 | | Cost = 4.693 + (-77.005) (Power) | | Cost = -77.005 + 4.693 (Power) | | Cost = 6.763 + 3.786 (Power) | Cost = 3.786 + 6.763 (Power) | |
