Statistics report

2. A bar or pie chart showing the number of customer purchases attributable to the method of payment

3. Cross tabulation of type of customers

4. Scatter diagram explore the relationship between net sales and customer age

Noted Observations:

• First of all, it should be noticed that the majority of the customers using a credit card were ‘Promotional Customers,’ referring to the fact that 70% of the customers were using the coupon. Obviously, the coupon promotion was a success, as that many more new customers were drawn in (assuming that they were there because of the coupon).
• Secondly, the main audience is middle aged married females, as shown by frequency distribution above. Married females should definitely be the company’s target audience, as 93% of customers were female and 84% of customers were married. I’m sure if we took notice of the relationship between marital status and gender, it would be married females that come out on top.
• 66% of our customers purchased 1-3 items and 39% spent $0-$50. Perhaps by offering promotional coupons that offer the chance to save more when you purchase a certain amount would be perfect to send to middle aged married females.

5. Covariance and correlation matrix

Correlation between Age and Net Sales:

Correlation between Item sold and Net Sales:

Noted Observations:

• First of all, the promotion customers had a mean of $84.29 and the regular customers had a mean of $61.99. There is a difference of $22.30 between the average Net Sales of the two types of customers. The promotion customers had a discount coupon and this clearly had some sort of encouragement towards spending more money at Pelican Stores. The promotion coupons were a great benefit to the company when we take into consideration that there were many more promotion customers than regular and the promotion customers spent much more on average than the regular customers.
• The correlation coefficient between Age and Net Sales is -0.0106359. This clearly shows that there is neither a positive or negative correlation between the two variables. Therefore, all ages of audience should be targeted; there is no one particular audience regarding age that spent more money than the other. One audience in particular that we should target would be women. It seems that women tend to spend much more money at Pelican Stores than men.
• The correlation coefficient between Items sold and Net Sales is 0.755059391. This indicates a positive correlation between items sold and net sales; it also shows that the more items that were purchased, the more net sales per customer. Perhaps it would be beneficial to create an appeal towards buying more items; for example: 20% off the purchase of three or more items, 30% off the purchase of four of more items, etc.

MANAGERIAL REPORT

To: National Health Care Association Management

                                      From: Nguyen Huu Hung (Mr.) ReMBA#7

                                                       Date: 10th December 2008

                                       Subject: Nurse Satisfaction Results (Case No.2)

Dear Sir or Madam,

This is my full report about Nurses satisfaction results. I used data analysis to express the result on study of Hospitals throughout the country in period of November 2008.

I have prepared some descriptive statistics that will help you see distinct relationships between job satisfaction variables. More specifically, i am trying to determine the relationship that does or does not exist between type of hospitals and variables: work, pay and promotion. These statistics will help you further understand what variable satisfy the nurses. I have also included some suggestions and recommendations regarding which fields should be improved to higher job satisfaction.

1. Percent frequency distribution for key variables

2. A bar or pie chart

3. Descriptive statistics

Noted Observations:

4. Covariance and correlation matrix

                

Noted Observations:

• The correlation coefficient between Promotion and Pay is -0.214949. This clearly shows that there is neither a positive or negative correlation between the two variables. Therefore, both Promotion and Pay should be targeted; there is not adequated to state that spent more on Promotion can have a better results than Pay, or reversely. It seems that Hospitals should spend on both Promotion and Pay in the same time.
• The correlation coefficient between Pay and Work is 0.2594039. This indicates a positive correlation between Salary (Pay) and Work; it also shows that the more Salary that were paid, the more Work Contribution of nurses. Perhaps it would be beneficial to create an appeal towards increasing salary; for example: 30% increase in salary, etc.
• The correlation coefficient between Promotion and Work is -0.149472. This indicates a negative correlation between Promotion and Work; it also shows that the more Promotion that were paid, the less Work Contribution of nurses. Perhaps it would be beneficial to create a just sufficient Promotion for nurses, not too high.