1.Usage research data elicitation homework project. The interviewers should ask the interviewees to recollect their usage of a mobile application. Focus on context, breakdowns, key tasks, ecology, and so on. Ask them to think about how that usage fits into the context of the rest of their lives. This is a case where you should ask about how they might want to use the proposed new product/system, too. Look especially for any emotional impact data and look at long-term usage, not just short snapshots of usage During the interviews, take raw data notes.

2.-Build a limited work activity affinity diagram from the work activity notes you wrote on Post-it stick or electronic notes from #1.

-Write the essence of the note on a Post-it or electronic note.

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1. Update the draw probabilities for the final draw in Section 4.5.3.

2. In Section 4.5.3, suppose that number 2 is picked again in the second draw. What would be the updated probabilities for the third draw?

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1)utilities expense, 2)accounts payable, 3)commissions,4)capital, 5)withdrawals.where do they appear? income statement or statement of changes in owner equity or balance sheet.i think #1 is balance sheet #2 is income statement, #3 statement of chnges in owner equity #4 i think blance sheet #5 owner equity #6 income statement. please correct if iam wrong and write the right ans. and mke it clear so i could understand why.please.

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1. Using the data in Table 2.3, calculate average fixed cost and average variable cost. Plot these on a graph with the average total cost and briefly explain the result.

2. When should a business cease production in the short run?

3. What costs need to be covered in the long run?

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1. Using the formulation in Section 10.3, plot the bet size (m) as a function of the maximum predicted probability

2. Draw 10,000 random numbers from a uniform distribution with bounds

(a) Compute the bet sizes m for

(b) Assign 10,000 consecutive calendar days to the bet sizes.

(c) Draw 10,000 random numbers from a uniform distribution with bounds

(d) Form a pandas series indexed by the dates in 2.b, and with values equal to the index shifted forward the number of days in 2.c. This is a t1 object similar to the ones we used in Chapter 3.

(e) Compute the resulting average active bets, following Section 10.4.

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