- The file SeaPopData.mat, which is included with the homework, contains the following population data for the city of Seattle.

Year | Population |

1860 | 188 |

1870 | 1151 |

1880 | 3533 |

1890 | 42837 |

1900 | 80671 |

1910 | 237194 |

1920 | 315312 |

1930 | 365583 |

1940 | 368302 |

1950 | 467591 |

1960 | 557087 |

1970 | 530831 |

1980 | 493846 |

1990 | 516259 |

2000 | 563374 |

2010 | 608660 |

You should load this data into Matlab using the load command. Be sure that the file SeaPopData.mat has been downloaded into the same directory as your script file. **You do NOT need to upload this file to Scorelator. Scorelator has its own copy. **If the load command is successful, you will have two new vectors in your workspace, t and Seattle_Pop. The values of the vector t are the number of years since 1860. Therefore, *t *= 0 is 1860 and *t *= 150 is 2010. The vector Seattle_Pop has the corresponding populations from the table above.

Data from:

- https://en.wikipedia.org/wiki/Demographics_of_Seattle
- https://www.seattle.gov/opcd/population-and-demographics

As you work through this assignment, it is a good idea to plot the data points and the best fit curves or interpolants to make sure your code is working correctly. However, you should remove all plot commands before submitting to Scorelator.

- Find the line of best fit for the data. That is, find a line
*P*=*mt*+*b*where*t*is the number of years since 1860 and*P*is the population of Seattle. Save the slope of the line in dat. Calculate the root-mean-square error and save it in A2.dat. The most recent population estimate for Seattle is that the 2017 population was 713,700. Use the equation of the best fit line to predict the population in 2017 (i.e. plug in*t*= 157). Save this value in A3.dat.

**Things to think about: **What is the meaning of the slope of the line of best fit? What does it tell us about how the population is changing?

- Find the best fit quadratic function for the data. Use this curve to predict the population in 2017, and save the prediction in dat. Repeat this process for the best fit polynomials of degree 3 (cubic) and degree 9. Save the predictions in A5.dat and A6.dat, respectively.
- Figure out the degree of the polynomial interpolant of the data. Save the degree in dat. Then find the polynomial interpolant, and use it to predict the population in 2017. Save the prediction in A8.dat.

**Things to think about: **How accurate were the predictions given by the different polynomial fits? Try calculating more best fit polynomials to see how well they predict the 2017 population. In particular, try using a degree 5 polynomial. It seems to do a good job. Would you trust it to predict the population in 2060? Now try adding the 2017 population to the data set (add a 157 at the end of the t vector and 713,700 at the end of the Seattle_Pop vector). How much does this change each of your best fit curves? Are some more resilient to new information than others? You can also try changing just one of the data points to the wrong number to see how it changes the best fit curves. Which will make the biggest mistake if given “bad” data?

- Fit an exponential function (
*P*=*ae*) to the data by taking a natural logarithm of the population values and using a linear fit. Make a 1×2 row vector with the values of the parameters with^{rt}*a*being the first component and*r*being the second (i.e. [a r]). Save this vector in dat. Then use this exponential function to predict the population in 2017, and store the prediction in A10.dat.

**Things to think about: **You can also do an exponential fit by using fminsearch, but it gives a very different answer. Why? Which method is better?

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