Part A question continue and part b is the same question with different questions

Question

Employment numbers are constantly changing. Having a low unemployment rate is good for a country because it builds confidence for investors to invest in that country. The table below was modified from the ESTAT-CANSIM table 183-0002. The table displays the number of employed public workers in Canada from 1981 to 2006.

Year -------------->Public Sector Employment (Number of People in Millions)
1981	2.69009
1982	2.72483
1983	2.74130
1984	2.76072
1985	2.79665
1986	2.84086
1987	2.87816
1988	2.94450
1989	2.96726
1990	3.02734
1991	3.05678
1992	3.06323
1993	3.03748
1994	3.00269
1995	2.95784
1996	2.85133
1997	2.78940
1998	2.77897
1999	2.76987
2000	2.78673
2001	2.81360
2002	2.84346
2003	2.90811
2004	2.94086
2005	2.97973
2006	3.04614

Complete the following tasks on this data:

1. Create a scatter plot of this data.
2. Find the average rate of change for the following periods:
3. 1981 to 2006
4. 2000 to 2006
5. 2005 to 2006
6. Perform regression analysis and determine the best algebraic model that fits the data and explain why you chose this model.
7. Calculate the instantaneous rate of change for the following points:
8. 1985
9. 1995
10. 2000
11. 2006

Answer 1

Hello Caroline,

I have a very complete answer for this problem but unfortunately it is in an Excel

file that can not be dropped into the answer space as text or excel or image.

See my profile and if there is a way to send the file.

Be well. Bruce S

So let's at least outline the scatter plot manual methods:

Method #1 One "scatter plot" could be just the plot of Employment Level Y vs years X. That gives the evolution of the employment.

Method #2 Try a more interesting "scatter plot" that says, 'How many times was the employment level at a given interval during 1981 to 2006?"

Pick employment level intervals (bins in Excel 'frequency') between 1981 and 2006. For example 26 intervals as: (note: M is millions)

>2.60M & < 2.62M

>2.62M & 2.64M

etc to .....

>3.10M & < 3.12M

>3.12M

Next, for each interval count the number of times the employment was in that interval. Then plot the number of times and the intervals as Y,X pairs. Here are some results:

2.96M 3 times

2.98M 2 times

3.04M 2 times

3.06M 2 times

3.02M 1 time

3.08M 1 times

11 times above 2.96M employed or42% of the intervals between 1981 & 2006!

That is the kind of data politicians love to brag about to their constituents.

Challenge: Could you determine this same result from the "scatter plot' initially proposed?

Be well, Bruce S