Investigation 1: Doctors in Alaska
Data:
years since 1999 number of doctor in Alaska
Regression equation(y=978.42e^0.0768x)
0
1054
978.42
1
1124
1056.5
2
1174
1140.9
5
1463
1436.5
7
1547
1657
8
1556
1808.7
9
1546
1953
13
3521
2655.4
Graph:

Steps and results: The first thing I did after writing the data for the doctors in Alaska on a paper and with the help of excel 2013 a table as shown above was constructed, then I made a scatter plot as told in question number 1, then by adding the appropriate trend line(appropriate regression) to my graph which showed me my regression function(equation) and the r2 (0.8262) value which was easy to choose the best one that fit my equation.
Investigation 2: Nurses in Alaska
Data:
years since 1999 number of nurses in Alaska
Regression equation (y= -69.732x+6044.7)
0
7350
6044.7
1
5030
5975
2
4930
5905.2
5
6800
5696
7
5260
5556.6
8
5150
5486.8
9
5350
5417.1
13
5350
5138.2
Graph:

Steps and Results: For this graph, as in the investigation 1, I took the data graphed it and made a data table, however this graph was the hardest one to pick the appropriate regression for because the r2 values were really close, the linear regression was 0.1193, the exponential was 0.1192 and the log.regression was 0.1190, and other values were close as well, so I did my best and compared all of them and turned out that the linear regression was the most fit for this data.
Investigation 3: Nurse to doctor ratio in Alaska
Data:
years after 1999
Nurse to Doctor ratio
Regression equation(y=6.0668e^-0.087x)
0
6.973434535
6.067
1
4.475088968
5.561
2
4.199318569
5.097
5
4.647983595
3.926
7
3.400129282
3.299
8
3.309768638
3.024
9
3.460543338
2.772
13
1.5194547
1.957
Graph:

Steps and Results: Finally the last investigation which was to graph the ratio of nurses to doctors which was pretty challenging, at first I divided the nurses by the doctors at which point I decided to use the (Auto complete) feature which