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A computer manufacturer is interested in comparing assembly times for two keyboard assembly processes. Process 1 is an updated process hoped to bring a decrease in assembly time, while Process 2 is the standard process used for several years. Assembly times can vary considerably from worker to worker, and the company decides to eliminate this effect by selecting 10 workers at random and timing each worker on each assembly process. Half of the workers are chosen at random to use Process 1 first, and the rest use Process 2 first. For each worker and each process, the assembly time (in minutes) is recorded, as shown in the table below. Worker 10 Process 1 67 31 56 Process 2 76 71 64 Difference - 10 -4 -14 -14 -11 -34 -21 2 [Process 1 - Process ?) Sand data to calculator Based on these data, can the company conclude, at the 0.03 level of significance, that the mean assembly time for Process 2 exceeds that of Process 17 Answer this question by performing a hypothesis test regarding #y (which is us with a letter "d' subscript), the population mean difference in assembly times for the two processes. Assume that this population of differences (Process 1 minus Process 2) is normally distributed. Perform a one-tailed test. Then complete the parts below. Carry your Intermediate computations to three or more decimal places and round your answers as specified. (If necessary, consult a list of formulas.) (a) State the null hypothesis Ho and the alternative hypothesis H1- Ho : 1 H, :0 (b) Determine the type of test statistic to use. Type of test statistic: (Choose one) 0=0 050 020 (c) Find the value of the test statistic. (Round to three or more decimal places.) Ox0 00 0 X ? [d) Find the critical value at the 0.05 level of significance. (Round to three or more decimal places.) (e) At the 0.05 level, can the company conclude that the mean assembly time for Process 2 exceeds that of Process 1? OYes ONo