Part a. Use the area below to draw a scatter diagram.Part c.1000800600400e.200WeANOVABack0SUMMARY OUTPUTRegression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservationsRegressionResidualTotalIntercept010R-Sq=R-Sq (adj) =20Chart Titledf0.877308360.769669973040Weight (oz)0.7543146394.975537917can concludecannot conclude●Series1The regression equation is (to 1 decimal)Price(pred) =+b. Does there appear to be any relationship between these two variables?There appears to be a(to 4 decimals)(to 4 decimals)(to 4 decimals)●1516 587440.9412Automobile racing, high-performance driving schools, and driver education programs run by automobile clubs continue to grow in popularity. All these activities require theparticipant 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 helmetsafety standards. Snell "SA" (Sports Application)-rated professional helmets are designed for auto racing and provide extreme impact resistance and high fire protection. One ofthe key factors in selecting a helmet is weight, since lower weight helmets tend to place less stress on the neck. The data in the Excel Online file below show the weight and pricefor 18 SA helmets. Construct a spreadsheet to answer the following questions.●50WeightSSMSSignificance F1 452135.6492 452135.6 50.1239429 3.74774E-06135305.292 9020.353c. Develop the estimated regression equation that can be used to predict the price given the weight. Also report the standard error of the estimate,R-sq, andR-sq adj.d. Test for the significance of the relationship at the .05 level of significance.P-value is(to 4 decimals).that the two variables are related.e. Did the estimated regression equation provide a good fit?R-sq is(to 4 decimals).The estimated regression equation provided aCoefficients Standard Errort Stat P-value Lower 95% Upper 95% Lower 95% Upper 95%2063.64585 238.7896118 8.642109 3.29E-07 1554.677839 2572.61385 1554.677839 2572.613854.056718472 -7.079826 3.7477E-06 -37.36755355 -20.0741721 -37.3675535 -20.07417264 -28.7208628fit.linear relationship between the two variables. The heavier helmets tend to be less expensive.ůThe estimated regression equation provided a60-goodbadb. Does there appear to be any relationship between these two variables?There appears to be ✓linear relationship between the two variables. The heavier helmets tend to be less expensive.c. Develop the estimat negative equation that can be used to predict the price given the weight. Also report the standard error of the estimate,R-sq, andpositiveR-sq adj.e. Did the estimated regression equation provide a good fit?R-sq is(to 4 decimals).The estimated regression equation providedFd. Test for the significance of the relationship at the .05 level of significance.P-value is(to 4 decimals).that the two variables are related.ession equation provide a good fit?4 decimals).70fit.#fit.USE THE SUMMARY OUTPUT TO ANSWER THE QUESTIONS

Part b: From the scatter plot, the variables weight and price are distributed in downward direction. That is, there exists a negative relation between weight and price. There appears to a negative linear relationship between the two variables. The heavier helmets tend to be less expensive. c) From the output, Pricepred=2063.6-28.7Weight s=Standard error=94.9755 R-sq=R square=0.7697R-sq(adj)=Adjusted R square=0.7543 d) Null Hypothesis: H0: Two variables are not related Alternative Hypothesis: H1: Two variables are related From the output, significance F is 0.0000 That is, p value=0.0000 Decision Rule: If p-value ≤ α, then reject the null hypothesis. Conclusion: The level of significance is α=0.05 Here, the p-value is less than the level of significance. From the decision rule, reject the null hypothesis. We can conclude that the two variables are related. e. R-sq=0.7697=76.97% That is, the 78.97% of the variation in the price explained by the weight. Hence, the constructed model is a good fit. The estimated regression equation provides good fit.