2017/2018, Semester 2

School of Design and Environment (Dean's Office (School Of Design & Env))

Modular Credits: 4

Dr. Diao Mi

Email:

Office: SDE1-05-11

Office Hours: By appointment only

Working with quantitative data is common in the planning profession. Developing the skill of expressing statistical ideas in a clear and simple language is essential for effective urban planning practices. This module provides the students in the Master in Urban Planning with an introduction to the quantitative methods and techniques used in planning practice and urban research. It will prepare students to conduct basic statistical analysis of data themselves as well as to critically review analyses prepared by others. The emphasis is on how to develop sound arguments and research design, such that students appreciate both the power and limitation of quantitative analysis in planning discussions. As a result of this module, students will learn:

- To develop statistical skills for the description and comparison of sets of planning data;
- To identify the sources of data most frequently used by urban planners;
- To be equipped with a variety of quantitative tools used to test hypotheses and generate estimates;
- To generate variables, perform linear regressions, and interpret the results;
- To critically review quantitative analyses and assess the validity of arguments made therein;
- To be familiar with real world practices of quantitative analysis in the planning profession;
- To be able to use statistical packages for the diverse quantitative analyses.

There will be also several guest speakers sharing the real-world experience of using quantitative methods in urban planning research.

1) Problem Sets: Four problem sets will account for 20% of the course grade. Unless otherwise stated, all problem sets are DUE one week after distribution. It will usually be possible to receive credit for problem sets that are up to one week late. After that date, no credit will be given for late problem sets. You are encouraged to work on problem sets in groups, but each individual is required to complete his/her own problem set. Copying the answers from someone else’s problem set is plagiarism and will be treated as such.

2) Final Exam: There will be a final exam at the end of the semester.

3) Planning Analysis Report Using Quantitative Methods: Students, working in small groups, will use quantitative method(s) to address a real planning problem. Select a problem/decision of interest to you, and formulate the problem and construct hypotheses to be tested, collect the data, perform the statistical analysis, and write up your results. You may use data that have been collected and/or analyzed by other researchers, but you must clearly differentiate your analysis from their work. You are allowed to choose one or more statistical techniques that you have learned from the module (descriptive stats, comparison of means, correlation, linear regression, etc.).

4) Class participation.

20% Problem Sets (Due: one week after distribution)

40% Final Exam (During the examination period)

30% Planning Analysis Project Using Quantitative Methods (Presentation on April 12, report due on April 12)

10% Class Participation

Sullivan, Michael.

1. Weiss, Neil. A.

2. Newbold, Paul, Carlson, William L, and Thorne, Betty. Statistics for Business and Economics. 7

3. A set of instructional videos that were created by PBS a number of years ago called

4. Other articles, reports, and web sites demonstrating the use in urban planning settings of the statistical techniques taught in the module will be posted on IVLE.

Week Dates |
Lecture |

1. Jan 15-19 | Introduction and Expectations: Introduction to statistics and its application to urban planning; Graphical presentation of quantitative resultsDescriptive Statistics: Sample and population; Central tendency(mean/median/mode); Dispersion (variance, standard deviation); Correlation |

2. Jan 22-26 | Probability I: Introduction to probability and probability distribution; Random events; Bayes’ Rule; Binomial/Normal/Poisson distribution |

3. Jan 29-Feb 2 | Probability II: Interpretation and application of the normal probability distribution; Population distribution vs. sampling distribution; Z score· Lab session 1: STATA |

4. Feb 5-9 (HW1 Due) |
Statistical Inference: Differentiation of a sample and a population; Confidence interval; Significance tests· Lab session 2: STATA |

5. Feb 12-16 |
Research Design: Potential topics; Research process; Data collection; Data organization · Presentation of MUP Year 2 Students |

6. Feb 19-23 (HW2 Due) |
Hypotheses Concerning a Single Population: Logic of hypothesis testing; Definition of research/null hypotheses; Type I and Type II errors |

Feb 24 – Mar 4 | MID SEMESTER BREAK |

7. Mar 5-9 |
No class due to MUP field trip |

8. Mar 12-16 |
Hypotheses Comparing Two Populations: Inferences for two population means; Comparing proportions from independent samples · Lab session 3: STATA |

9. Mar 19-23 (HW3 Due) |
Additional Hypothesis Tests: One-way ANOVA (Analysis of Variance); F-distribution; Association between categorical variables · Lab session 4: STATA |

10. Mar 26-30 |
Linear Regression I: Describing the relation between two variables; Introduction to simple, linear regression analysis · Lab session 5: STATA |

11. Apr 2-6 (HW4 Due) |
Linear Regression II: Interpretation of a coefficient of regression and correlation; Multiple regressions; Recognition of the limitations of regressions · Lab session 6: STATA |

12. Apr 9-13 | Group Project |

13. Apr 16-20 | Project Presentation |

Week Dates |
Reading List |

1. Jan 15-19 | Introduction and Expectations:1. Sullivan Chapter 1 Data Collection; Chapter 2 Organizing and Summarizing Data 2. Weiss Chapter 1 The Nature of Statistics 3. Against All Odds #1 What is Statistics #2 Stemplots #3 HistogramsDescriptive Statistics: 1. Sullivan Chapter 2 Organizing and Summarizing Data; Chapter 3 Numerically Summarizing Data; Chapter 4 Describing the Relation between Two Variables 2. Weiss Chapter 3 Descriptive Measures 3. Against All Odds #4 Measures of Center #5 Boxplots #6 Standard |

2. Jan 22-26 | Probability I: 1. Sullivan Chapter 5 Probability; Chapter 6 Discrete Probability Distributions 2. Weiss Chapter 4 Probability Concepts; Chapter 5 Discrete Random Variables 3. Against All Odds #13 Two-Way Tables #18 Introduction to Probability #19 Probability Models #20 Random Variables #21 Binomial Distributions |

3. Jan 29-Feb 2 | Probability II: 1. Sullivan Chapter 7 The Normal Probability Distribution; Chapter 8 Sampling Distributions2. Weiss Chapter 6 The Normal Distribution3. Against All Odds #7 Normal Curves #8 Normal Calculations #9 Checking Assumption of Normality #22 Sampling Distribution |

4. Feb 5-9 |
Statistical Inference:1. Sullivan Chapter 9 Estimating the Value of a Parameter 2. Weiss Chapter 7 The Sampling Distribution of the Sample Mean; Chapter 8 Confidence Intervals for One Population Mean 3. Against All Odds #24 Confidence Intervals #25 Test of Significance |

5. Feb 12-16 |
Research Design: 1. Sullivan Chapter 1 Data Collection; Chapter 2 Organizing and Summarizing Data 2. Weiss Chapter 1 The Nature of Statistics; Chapter 2 Organizing Data 3. Against All Odds #15 Designing Experiments #16 Census and Sampling #17 Samples and Surveys |

6. Feb 19-23 |
Hypotheses Concerning a Single Population: 1. Sullivan Chapter 10 Hypothesis Tests Regarding a Parameter 2. Weiss Chapter 9 Hypothesis Tests for One Population Mean 3. Against All Odds #26 Small Sample Inference for One Mean |

8. Mar 12-16 |
Hypotheses Comparing Two Populations: 1. Sullivan Chapter 11 Inferences on Two Samples 2. Weiss Chapter 10 Inferences for Two Population Means 3. Against All Odds #27 Comparing Two Means |

9. Mar 19-23 | Additional Hypothesis Tests: 1. Sullivan Chapter 12 Inferences on Categorical Data Chapter 13 Comparing Three or More Means 2. Weiss Chapter 13 Chi-Sqaure Procedures Chapter 16 Analysis of Variance (ANOVA) 3. Against All Odds #29 Inference for Two-Way Tables #31 One-Way ANOVA |

10. Mar 26-30 |
Linear Regression I: 1. Sullivan Chapter 4 Describing the Relation between Two Variables 2. Weiss Chapter 14 Descriptive Measures in Regression and Correlation 3. Against All Odds #10 Scatterplots #11 Fitting Lines to Data #12 Correlation |

11. Apr 2-6 |
Linear Regression II: 1. Sullivan Chapter 14 Inference on the Least-Square Regression Model and Multiple Regression 2. Weiss Chapter 14 Descriptive Measures in Regression and Correlation; Chapter 15 Inferential Methods in Regression and Correlation 3. Against All Odds #14 The Question of Causation #30 Inference for Regression |