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The 鈥渓ast name鈥 effect in purchasing. The Journal of Consumer Research (August 2011) published a study demonstrating the 鈥渓ast name鈥 effect鈥攊.e., the tendency for consumers with last names that begin with a later letter of the alphabet to purchase an item before consumers with last names that begin with earlier letters. To facilitate the analysis, the researchers assigned a number, x, to each consumer based on the first letter of the consumer鈥檚 last name. For example, last names beginning with 鈥淎鈥 were assigned x = 1; last names beginning with 鈥淏鈥 were assigned x = 2; and last names beginning with 鈥淶鈥 were assigned x = 26.

a. If the first letters of consumers鈥 last names are equally likely, find the probability distribution for x.

b. Find E (x) using the probability distribution, part a. If possible, give a practical interpretation of this value.?

c. Do you believe the probability distribution, part a, is realistic? Explain. How might you go about estimating the true probability distribution for x

Short Answer

Expert verified
  1. The probability distribution of x is f(x)=1n where x = 1,...26
  2. Ex=a+b2and the interpretation of the value isEx=1+262=13.5
  1. The probability distribution is realistic. Estimating the probability distribution of x isfx=1n

where x = 1,...26.

Step by step solution

01

Given information

Given that researchers assigned a number is x. Here the 鈥渓ast name鈥 effect in purchasing.

02

Finding the probability distribution for x

a.

The first letters of consumers last names are equally likely. There are equally likely so there follows discrete uniform distribution. Then the probability distribution for x is

f(x)=1n where x = 1,...26

03

Finding E(x) and also define the practical interpretation

b.

The mean value using this distribution to given by

Ex=a+b2

The practical interpretation is- Here researchers assigned by numbers. So then the probability distribution of the value is

Ex=1+262=13.5

04

Probability distribution is realistic and estimating the true probability distribution

c.

Yes the probability distribution is realistic.

The true probability distribution foris defined by discrete uniform distribution. We can also calculate this in step-2.

Thus, the step-2 follows the distribution that will be given by fx=1nwhere x = 1,...26.

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Most popular questions from this chapter

Estimating production time.A widely used technique for estimating the length of time it takes workers to produce a product is the time study. In a time study, the task to be studied is divided into measurable parts, and each is timed with a stopwatch or filmed for later analysis. For each worker, this process is repeated many times for each subtask. Then the average and standard deviation of the time required to complete each subtask are computed for each worker. A worker鈥檚 overall time to complete the task under study is then determined by adding his or her subtask-time averages (Gaither and Frazier, Operations Management, 2001). The data (in minutes) given in the table are the result of a time study of a production operation involving two subtasks.


Worker AWorker B

Repetition

Subtask 1

Subtask 2

Subtask 1

Subtask 2

1

30

2

31

7

2

28

4

30

2

3

31

3

32

6

4

38

3

30

5

5

25

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29

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6

29

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30

1

7

30

3

31

4

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Chemistry, Dec. 15, 2009). Drug concentrations (measuredas a percentage) for 50 randomly selected tablets are listedin the table below and saved in the accompanying file.

a. Descriptive statistics for the drug concentrations areshown at the top of the XLSTAT printout on the nextpage. Use this information to assess whether the dataare approximately normal.

b. An XLSTAT normal probability plot follows. Use thisinformation to assess whether the data are approximatelynormal.

91.28 92.83 89.35 91.90 82.85 94.83 89.83 89.00 84.62

86.96 88.32 91.17 83.86 89.74 92.24 92.59 84.21 89.36

90.96 92.85 89.39 89.82 89.91 92.16 88.67 89.35 86.51

89.04 91.82 93.02 88.32 88.76 89.26 90.36 87.16 91.74

86.12 92.10 83.33 87.61 88.20 92.78 86.35 93.84 91.20

93.44 86.77 83.77 93.19 81.79

Descriptive statistics(Quantitative data)

Statistic

Content

Nbr.of Observation

50

Minimum

81.79

Maximum

94.83

1st Quartile

87.2725

Median

89.375

3rd Quartile

91.88

Mean

89.2906

Variance(n-1)

10.1343

Standard deviation(n-1)

3.1834

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Descriptive Statistics: Support

Variables

N

Mean

StDev

Variance

Minimum

Maximum

Range

Support

992

67.755

26.871

722.036

0.000

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