SPSS Work-Volunteers dataset for which the codes are provided in label fields

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

QUESTION 1 

Consider the 'Diamond' dataset. It is provided as Microsoft Excel Workbook. Data are for 308 round-cut diamonds, taken from a newspaper ad. The codes are as

follows:

1. IDNO - an identification number for each data point.

2. WEIGHT - weight of the diamond, in carats.

3. COLOR - degree of color purity in the diamond: "D" represents top color purity grade; Lesser grades are "E", "F", and so on, through the alphabet. Note that these grades are ranked and should be coded according to their order.

4. CLARITY - diamond clarity (presence or absence of minute flaws): "IF" means "internally flawless" - the top grade; "VVS1" and "VVS2" for "very very slightly imperfect"; and, "VS1" and "VS2" for "very slightly imperfect". Note that these grades are ranked and should be coded according to their order.

5. RATER - the diamond was evaluated by one of three independent rating agencies: "GIA" - Gemmological Institute of America (based in New York); "IGI"

- International Gemmological Institute (Antwerp); "HRD" - Hoge Raad Voor Diamant (Antwerp).

6. PRICE - in Singapore dollars.

Operationalize the data and enter it into an SPSS file (you need to submit your SPSS dataset into the specified submission link on the LMS), then statistically solve the following business problems:

1.1. In the diamond market, it is always argued that diamond price is associated with diamond weight. Provide statistical evidence to prove or disprove that argument.

1.2. Another argument in the diamond market is that diamond price is predicted by diamond weight. Provide statistical evidence to prove or disprove that argument.

1.3. A third argument is that diamond weight is not the sole predictor of diamond price. It should be controlled by diamond colour and clarity. Provide statistical evidence to prove or disprove that argument.

For all answers above, be sure to include relevant SPSS output (graphs, tables) to support your solutions (screenshots must include the time and date of your work).

You should interpret, discuss and report your answers. You should proceed if any of the assumptions is violated. However, you must report it.

QUESTION 2- (20 marks)

Consider the SPSS "Work-Volunteers" dataset, for which the codes are provided in label fields, then solve the following problems:

2.1. The Scale of Volunteers Experience comprises 13 questions numbered in your data file from Q1 to Q13. However, they were not developed based on a proven theory. You need to confirm whether those questions are interrelated so that they can form a scale. Again, be sure to include relevant SPSS output (graphs, tables) to support your answers.

2.2. We are interested in exploring the differences in the scores of Total Volunteering Experience according to the respondents' Total Volunteering Services Groups; and the respondents' age groups 46-55 and 56-65.

2.3. Somebody has already tested the normality of the variable Factor 1 according to the length of service groups. It was found Factor 1 for each length of service groups was normally distributed. Accordingly, we would like to explore whether there are differences in the Factor 1 scores between the length of service groups.

2.4. Somebody has already tested the normality of the variable Factor 2 according to the length of service groups. It was found Factor 2 for each length of service groups was NOT normally distributed. Accordingly, we would like to explore the following:

2.4.1. whether there is a difference in the Factor 2 scores between the length of service groups.

2.4.2. Whether there is a difference between the (5 or less) group and the (11- 15) group

Attachments:

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