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