4. Methodology The data will first be imported into the python file using a csv file reader. From here the data will be cleaned up to remove any unnecessary and the important data will be compiled into their respective data structures of list and dictionaries. The unemployment data will only be considered from 2000 to 2010 so that it matches the census data. In order to compare the yearly census housing data with the monthly unemployment rates the yearly unemployment rate for each county will be computed. A graph will then be constructed to better visualize the unemployment data for each county for each year. The years of interest are from 2006 to 2010. Trends in the unemployment data will then be analyzed to see which counties were affected the most. The sum of squared residuals was used to calculate how much each counties rates differed from the state rate. Next the occupancy rate changes for each county were extracted from the census data along with the raw number of occupied houses. 5. Results Results Housing Occupancy Rate Change King County -2.89 % Occupied Homes Change King County -76,226 Housing Occupancy Rate Change King County -1.50 % Occupied Homes Change Adams County -287 These are results for the unemployment analysis. The graph shows the unemployment rates for each county along with the state average. The sum of squared residuals for King County was calculated to be 3.61 and 4.84 for Adams County. This result means that on average King County was closer to the state average over time. However the King County rate was below the state at all times. The housing bubble occurred at the end of 2006, but the unemployment rates didn’t see a large increase until 2009. This result is more inline with the financial crisis which
4. Methodology The data will first be imported into the python file using a csv file reader. From here the data will be cleaned up to remove any unnecessary and the important data will be compiled into their respective data structures of list and dictionaries. The unemployment data will only be considered from 2000 to 2010 so that it matches the census data. In order to compare the yearly census housing data with the monthly unemployment rates the yearly unemployment rate for each county will be computed. A graph will then be constructed to better visualize the unemployment data for each county for each year. The years of interest are from 2006 to 2010. Trends in the unemployment data will then be analyzed to see which counties were affected the most. The sum of squared residuals was used to calculate how much each counties rates differed from the state rate. Next the occupancy rate changes for each county were extracted from the census data along with the raw number of occupied houses. 5. Results Results Housing Occupancy Rate Change King County -2.89 % Occupied Homes Change King County -76,226 Housing Occupancy Rate Change King County -1.50 % Occupied Homes Change Adams County -287 These are results for the unemployment analysis. The graph shows the unemployment rates for each county along with the state average. The sum of squared residuals for King County was calculated to be 3.61 and 4.84 for Adams County. This result means that on average King County was closer to the state average over time. However the King County rate was below the state at all times. The housing bubble occurred at the end of 2006, but the unemployment rates didn’t see a large increase until 2009. This result is more inline with the financial crisis which
mrl26-report.docx