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Data points that lie apart from the rest of the points are called O a. outliers O b. intercepts O c. squared errors O d.

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Data points that lie apart from the rest of the points are called O a. outliers O b. intercepts O c. squared errors O d. residuals Peter, a cost accountant at Sunway Plastics Lid (SPL), is analysing the manufacturing costs of a molded plastic telephone handset produced by PPL. Peter's independent variable is production lot size (in 1000s of units) and his dependent variable is the total cost of the lot (in RM 100s). Regression analysis of the data yielded the following tables. Coefficients Standard Error Statistic P-value Intercept 3.996 1.161268 3.441065 0.004885 0.358 0. 102397 3.496205 0.004413 Source df SS F Se = 0.898 Regression 1 9.858769 9.85876912.22345 R = 0.526341 Residual 11 8.872 0.806545 Total 12 18.73077 For a lot size of 10,000 handsets, Peter's model predicts total cost is O a. RM3960.20 O b. RM757.60 O C. RM354.01 O d. RM4031.80A regression model was developed, and the intercept was 21.4 while the slope was -0.4. For this, it can be determined that_ O a. the coefficient of correlation is negative O b. the coefficient of correlation is positive O c. the coefficient of determination is zero O d. the coefficient of determination is negative A researcher has developed a simple regression model from fourteen pairs of data points. He wants to test to determine if the slope is significantly different from zero. He uses a two-tailed test and alpha = 0.01. The critical table t-value is O a. 3.055 O b. 2.650 O c. 2.718 O d. 3.012 If x and yin a regression model are totally unrelated, O a. the correlation coefficient would be -1 O b. the coefficient of determination would be 0 O c. the SSE would be 0 O d. the coefficient of determination would be 1Peter, a cost accountant at PlayPlastics Lid (PPL), is analysing the manufacturing costs of a moulded plastic telephone handset produced by PPL. Peter's independent variable is production lot size (in 1000s of units) and his dependent variable is the total cost of the lot (in $100s). Regression analysis of the data yielded the following tables. Coefficients Standard Error tStatistic Pvalue Intercept 3.996 1.161 3.441 0.005 X 0.358 0.102 3.496 0.004 Source df SS MS E Se = 0.898 Regression 1 9.858 9.858 12.223 R = 0.526 Residual 11 8.872 0.806 Total 12 18.731 Peter's regression model is _ O a. y= -3.996 + 0.358x O b. y= 0.358 + 3.996x O c. y= -0.358 + 3.996x O d. y= 3.996 + 0.358x

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