8). What can be said about the linear relationship between Price and Sales? a).

Question

8). What can be said about the linear relationship between Price and Sales?

            a). The relationship is negatively moderate.

            b). There is no relationship.

            c). The relationship is positively strong.

            d). The relationship is negatively strong.

9). What is the number of estimated coefficients of the cubic regression model?

            a). 1

            b). 2

            c). 3

            d). 4

10). Using the quadratic equation, predict the sales if the luxury good is priced at $100.

            a). 1191.87

            b). 12,660.6

            c). 1160.79

            d). 1168.00

11). Using the cubic regression equation, predict the sales if the luxury good is priced at $100.

            a). 1171.85

            b). 1133.10

            c). 1106.61

            d). 1092.91

12). Which of the following models is most likely to be chosen in order to describe the relationship between Price and Sales?

            a). Quadratic

            b). Linear

            c). Exponential

            d). Cubic

13). For which price do sales predicted by the quadratic equation reach their minimum?

            a). 97.54

            b). 1157.16

            c). 106.33

            d). 1166.64

Use the following scenario and Excel output to answer questions (8) through (13): Typically, the sales volume declines with an increase of a product price. It has been observed, however, that for some luxury goods the sales volume may increase when the price increases. The following Excel output illustrates this rather unusual relationship. Scatterplot of Price Vs Sales for a Luxury Good Linear (Scaterplot of Price Poly (5catterplot of Price

Explanation / Answer

(8) a). The relationship is negatively moderate.

since the regression coefficent is negative so negatively correlated and R2=0.0012 and correlation coefficeint=sqrt(0.0012)=0.0346

(9) (d)4

there are four coefficient were estimated ( intercept, x, x2 andx3)

(10) d). 1168.00

for x=100, y=0.2702*100*100 -57.463*100+4212.3=1168

(11) b)1133.1

for x=100, y=0.0007*100*100*100+0.0352*100*100-34.581*100+3539.2=1133.1

(12)c)cubic

R2 for this is highest

(13) a) 97.54

as it is mimimum for 97.54 which is 6727.947

coefficient 97.54 1157.16 106.33 1166.64 0.2702 2570.697 361803 3054.9 367755.4 -0.57463 -56.0494 -664.939 -61.1004 -670.386 4213.3 4213.3 4213.3 4213.3 4213.3 sum (predicted y) 6727.947 365351.4 7207.099 371298.3

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