Q Quantitative Research Project This project uses the KCHousing_2014_2015.sav data that is on Canvas Module 4 and 5. Number each part of your report as indicated below. 1. For this project, you’ll be using the housing price data in this data set from your home Zip code and a (different) south King County Zip code of your choice (see https://www.zipdatamaps.com/king-wa-county-zipcodes (Links to an external site.) for a map of Zip codes) Identify the two Zip code areas at the top of your paper along with the number of housing transactions in this data set for each of your two Zip code areas. (If either of your Zip code areas has less than 60 transactions, pick a different Zip code area to work with. If you don’t live in King County, use the Highline College Zip code of 98198 as one of your Zip codes and pick another of your choice.) (2 points) 2. Go into the dataset and select only the cases that correspond to your two Zip codes. You’ll need to use a condition of “zipcode = __________ | zipcode = _________” where the “ | “ is SPSS’ way of saying OR. Use the compare means option with price as the dependent variables and Zip code as the independent variable. Use the options to include minimum and maximum. Copy the resulting table into your report. What does a comparison of these descriptive statistics tell you? Refer to at least 4 different descriptive statistics pairs in your explanation. (8 points) 3. Use the “population pyramid” graph type under histogram to create back-to-back vertical histograms for selling price in each of the two Zip codes. (Distribution variable will be price, Split variable will be parcel Zip code) Change the title of the graph to “Back-to-back Histograms of 2014-15 selling prices of houses by Zip code areas”. Copy the graph and paste it into your report document. Explain in words what your comparison of the two Zip codes shows about the distributions of housing selling prices in the two areas. What could help explain any differences or similarities that you see in the two distributions. (5 points) 4. Using your dataset from your two Zip code areas, create a correlation matrix for the following independent variables and paste it into your report document. (It may fit better on a landscape-oriented page.) Explain whether there are any correlations that suggest the problem of multicollinearity. Why or why not? Use at least two examples from your correlation matrix as part of your explanation. (5 points) Bedrooms Floors Bathrooms Building Condition 2015 living sq. ft. Building Grade 2015 lot sq. ft. Year built 5. Using your dataset from your two Zip code areas, run a regression with Price as the dependent variables and the following independent variables. Copy the results into your report document. Bedrooms Floors Waterfront Bathrooms Building Condition View 2015 living sq. ft. Building Grade 2015 lot sq. ft. Year built If there are any variables that are potentially multicollinearity problems, pick only one of them and re-run the regression. Are there any of the independent variables (not including the constant) that are statistically not significant at the 0.10 level or lower? If yes, drop them from the list of independent variables and repeat the regression. Continue the process until all of the independent variables are significant at least at the 0.10 level or lower. Copy the final regression Model Summary and Coefficients table into your report document. Describe in words the process that you followed in getting to the final regression version. (10 points) Note: Waterfront and View are “binary” variables. They are a 1 when it is waterfront/view and 0 when it is not waterfront/view. That means that they influence the dependent variable in a lump sum, up or down, based on the estimated coefficient when the property has that characteristic. 6. Comment on the strength of your regression model based on the adjusted R-square. Interpret the estimated regression coefficients for each of the variables that are significant at the 0.05 level or better. Explain what your regression suggests about the influence of a one unit change in each of these independent variables on the selling price of a house in your two zip codes in 2014-15. Based on the size of the coefficients, what 3 independent variables have the biggest impact on price? Comment very briefly on the independent variables that were not significant at the 0.05 level but are included in your regression. (15 points) (see https://www.spss-tutorials.com/linear-regression-in-spss-example/ (Links to an external site.) if you want help with the interpretation step) 7. What was interesting to you in your final results? What did you learn, or what surprised you about factors that influence King County housing prices from this project? What questions do you still have about influences on these housing prices? (5 points) 8. Bonus: Repeat the model construction process for the data set as a whole. Copy your final Model summary and Coefficients for the whole data set into your report document. Comment on whether the factors influencing housing prices in your two Zip codes appear to be similar or different than King County as a whole. Use at least 3 coefficient comparisons as part of your explanation. (4 points possible) Rubric Quantitative Final Quantitative Final Criteria Ratings Pts This criterion is linked to a Learning OutcomeQuestion 1 pick a zipcode 2 pts Full Marks 1 pts partial credit 0 pts No Marks 2 pts This criterion is linked to a Learning OutcomeQuestion 2 Comparison of zip code descriptive statistics 8 pts Full Marks 4 pts partial credit 0 pts No Marks 8 pts This criterion is linked to a Learning OutcomeQuestion 3 Population pyramid 5 pts Full Marks 2.5 pts partial credit 0 pts No Marks 5 pts This criterion is linked to a Learning OutcomeQuestion 4 correlation matrix 5 pts Full Marks 2.5 pts partial credit 0 pts No Marks 5 pts This criterion is linked to a Learning OutcomeQuestion 5 regression 10 pts Full Marks 5 pts partial credit 0 pts No Marks 10 pts This criterion is linked to a Learning OutcomeQuestion 6 discussion of model strength 15 pts Full Marks 7.5 pts partial credit 0 pts No Marks 15 pts This criterion is linked to a Learning OutcomeQuestion 7 what was interesting? 5 pts Full Marks 2.5 pts partial credit 0 pts No Marks 5 pts This criterion is linked to a Learning OutcomeQuestion 8: Bonus bonus question (up to 4 points) 0 pts Full Marks 0 pts No Marks 0 pts partial credit 0 pts Total Points: 50 PreviousNext