Question

In: Statistics and Probability

Use Excel Analysis ToolPak to solve. Helmet   Weight   Price Pyrotect Pro Airflow    64   248 Pyrotect...

Use Excel Analysis ToolPak to solve.

Helmet   Weight   Price
Pyrotect Pro Airflow    64   248
Pyrotect Pro Airflow Graphics   64   278
RCi Full Face    64   200
RaceQuip RidgeLine    64   200
HJC AR-10    58   300
HJC Si-12   47   700
HJC HX-10   49   900
Impact Racing Super Sport   59   340
Zamp FSA-1   66   199
Zamp RZ-2   58   299
Zamp RZ-2 Ferrari   58   299
Zamp RZ-3 Sport   52   479
Zamp RZ-3 Sport Painted   52   479
Bell M2    63   369
Bell M4    62   369
Bell M4 Pro   54   559
G Force Pro Force 1   63   250
G Force Pro Force 1 Grafx   63   280

Automobile racing, high-performance driving schools, and driver education programs run by automobile clubs continue to grow in popularity. All these activities require the participant to wear a helmet that is certified by the Snell Memorial Foundation, a not-for-profit organization dedicated to research, education, testing, and development of helmet safety standards. Snell “SA” (Sports Application) rated professional helmets are designed for auto racing and provide extreme impact resistance and high fire protection. One of the
key factors in selecting a helmet is weight, since lower weight helmets tend to place less stress on the neck. The following data show the weight and price for 18 SA helmets
(SoloRacer website, April 20, 2008).

Required:

a. Develop a scatter diagram with weight as the independent variable.
b. Does there appear to be any relationship between these two variables?
c. Develop the estimated regression equation that could be used to predict the price given the weight.
d. Test for the significance of the relationship (slope) at the .05 level of significance.
e. Did the estimated regression equation provide a good fit? Explain.

Solutions

Expert Solution

helmet weight(X) price(Y) XY (X^2) (Y^2)
Pyrotect Pro Airflow 64 248 15872 4096 61504
Pyrotect Pro Airflow Graphics 64 278 17792 4096 77284
RCi Full Face 64 200 12800 4096 40000
RaceQuip RidgeLine 64 200 12800 4096 40000
HJC AR-10 58 300 17400 3364 90000
HJC Si-12 47 700 32900 2209 490000
HJC HX-10 49 900 44100 2401 810000
Impact Racing Super Spor 59 340 20060 3481 115600
Zamp FSA-1 66 199 13134 4356 39601
Zamp RZ-2 58 299 17342 3364 89401
Total 593 3664 204200 35559 1853390
Correlation = (it is strongly negatively correlated,becoz increase in weight decreases the price and they have inverse relationship among them)
-0.921461091

Y(dependent variable) = a(intercept) + b(slope)X(independent variable) + e(error term)

Y = 232.8500253 + 1.859443038 * X

Regression Statistics
Multiple R 0.921461091
R Square 0.849090543
Adjusted R Square 0.830226861
Standard Error 98.17057993
Observations 10
ANOVA
df SS MS F Significance F
Regression 1 433800.6979 433800.6979 45.01191948 0.000151282
Residual 8 77099.70211 9637.462763
Total 9 510900.4
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 2333.817813 294.8851541 7.914327936 4.71834E-05 1653.811428 3013.824197 1653.811428 3013.824197
weight(X) -33.17736615 4.945134851 -6.709092299 0.000151282 -44.58086757 -21.77386473 -44.58086757 -21.77386473
RESIDUAL OUTPUT
Observation Predicted price(Y) Residuals Standard Residuals
1 210.4663791 37.53362091 0.405522885
2 210.4663791 67.53362091 0.729650594
3 210.4663791 -10.46637909 -0.113081449
4 210.4663791 -10.46637909 -0.113081449
5 409.530576 -109.530576 -1.183396488
6 774.4816037 -74.48160365 -0.804718385
7 708.1268714 191.8731286 2.073046586
8 376.3532098 -36.35320985 -0.392769421
9 144.1116468 54.88835321 0.593027872
10 409.530576 -110.530576 -1.194200745

yes the regression model is a good fit


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