(a) State the null hypothesis Ho and the alternative hypothesis H₁. H:0 H₁:0 b) Determine the type of test statistic to use,

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**Hypothesis Test for the Difference of Population Means: Paired**

(a) **State the Null and Alternative Hypothesis:**
   - \( H_0 \): [ ]
   - \( H_1 \): [ ]

(b) **Determine the Type of Test Statistic to Use:**
   - Type of test statistic: (Choose one) [Dropdown menu]

(c) **Find the Value of the Test Statistic:**
   - [Input box] (Round to three or more decimal places.)

(d) **Find the Critical Value at the 0.05 Level of Significance:**
   - [Input box] (Round to three or more decimal places.)

(e) **Conclusion at the 0.05 Level:**
   - Can the company conclude that the mean assembly time for Process 1 exceeds that of Process 2?
     - [ ] Yes
     - [ ] No
Transcribed Image Text:**Hypothesis Test for the Difference of Population Means: Paired** (a) **State the Null and Alternative Hypothesis:** - \( H_0 \): [ ] - \( H_1 \): [ ] (b) **Determine the Type of Test Statistic to Use:** - Type of test statistic: (Choose one) [Dropdown menu] (c) **Find the Value of the Test Statistic:** - [Input box] (Round to three or more decimal places.) (d) **Find the Critical Value at the 0.05 Level of Significance:** - [Input box] (Round to three or more decimal places.) (e) **Conclusion at the 0.05 Level:** - Can the company conclude that the mean assembly time for Process 1 exceeds that of Process 2? - [ ] Yes - [ ] No
A computer manufacturer is interested in comparing assembly times for two keyboard assembly processes. Process 1 is the standard process used for several years, and Process 2 is an updated process hoped to bring a decrease in assembly time. Assembly times can vary considerably from worker to worker, and the company decides to eliminate this effect by selecting 8 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 (k) | 1  | 2  | 3  | 4  | 5  | 6  | 7  | 8  |
|------------|----|----|----|----|----|----|----|----|
| Process 1  | 79 | 39 | 56 | 72 | 49 | 90 | 75 | 67 |
| Process 2  | 74 | 19 | 60 | 77 | 38 | 75 | 44 | 44 |
| Difference (Process 1 - Process 2) | 5  | 20 | -4 | -5 | 11 | 15 | 31 | 23 |

Based on these data, can the company conclude, at the 0.05 level of significance, that the mean assembly time for Process 1 exceeds that of Process 2? Answer this question by performing a hypothesis test regarding \(\mu_d\) (which is \(\mu\) 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 \(H_0\) and the alternative hypothesis \(H_1\).

- \(H_0\): 
- \(H_1\): 

Carry out the hypothesis test using these hypotheses to determine if Process 1's mean assembly time significantly exceeds that of Process 2.
Transcribed Image Text:A computer manufacturer is interested in comparing assembly times for two keyboard assembly processes. Process 1 is the standard process used for several years, and Process 2 is an updated process hoped to bring a decrease in assembly time. Assembly times can vary considerably from worker to worker, and the company decides to eliminate this effect by selecting 8 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 (k) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |------------|----|----|----|----|----|----|----|----| | Process 1 | 79 | 39 | 56 | 72 | 49 | 90 | 75 | 67 | | Process 2 | 74 | 19 | 60 | 77 | 38 | 75 | 44 | 44 | | Difference (Process 1 - Process 2) | 5 | 20 | -4 | -5 | 11 | 15 | 31 | 23 | Based on these data, can the company conclude, at the 0.05 level of significance, that the mean assembly time for Process 1 exceeds that of Process 2? Answer this question by performing a hypothesis test regarding \(\mu_d\) (which is \(\mu\) 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 \(H_0\) and the alternative hypothesis \(H_1\). - \(H_0\): - \(H_1\): Carry out the hypothesis test using these hypotheses to determine if Process 1's mean assembly time significantly exceeds that of Process 2.
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