A call centre manager is keen to understand the relationship between Average Speed of Answer (ASoA, measured in minutes) and Call Abandonment Rate (CARate, a number between 0 and 1). They believe that the slower calls are answered, the more customers will abandon the call before they are answered. They take a sample of 16 random quarter hour periods during a week, and for each quarter hour period the ASoA and CARate are determined, and captured in the table below. NB In this question, if you use Excel functions you should list the functions you used. If you use an analysis tool add-in you should cut and paste the output into your answer, and if you use and "old school" approach you should quote the relevant formulae. (Show working).

Sample	A (ASoA)	C (CARate)
1	1.1	0.00
2	3.7	0.38
3	2.3	0.06
4	7.5	1.00
5	4.1	0.56
6	7.7	0.92
7	3.0	0.16
8	3.1	0.22
9	4.5	0.72
10	0.8	0.06
11	7.1	0.91
12	1.6	0.09
13	0.8	0.05
14	6.5	0.95
15	0.1	0.03
16	0.0	0.06

Accumulations are SA = 53.9, SC = 6.17, SA² = 287.91, SC² = 4.6497, SAC = 35.6970

1. Determine the mean, median and standard deviation of Call Abandonment Rate for this sample.
2. Determine the Ordinary Least Squares regression line that uses Average Speed of Answer to explain or predict Call Abandonment Rate
3. Interpret the slope coefficient.
4. Determine and interpret the standard error of the estimate.
5. Determine and interpret the coefficient of determination
6. Use your regression equation to predict the CARate if the average speed of answer is nine minutes, and explain why this might be problematic.
7. Create a scattergram of the data and study it carefully. Identify and describe any feature that may cause you to doubt whether an OLS linear regression model is appropriate. [NB There is no need to include the scattergram in your answer.]