Lecture Notes

SES # TOPICS LECTURE NOTES
L1 Introduction to Course Introduce instructors; how the course is set up; describe "Peer Instruction" and expectations; divide into groups for recitations or labs. Give first assignment requesting areas of interest and the reading for next lecture on arguments. What is statistics? The three parts of statistics: collection, evaluation, and drawing conclusions. Discuss the schematic diagram for our study of statistics. (PDF)
L2 What is an Argument? Premise; conclusion; deductive vs. inductive; falsehoods and fallacies; discuss argument assignment and the link to quantitative reasoning. (PDF)
L3 Measurement Variable; indicator; observation; construct; validity; reliability; bias; ikert scale; qualitative; quantitative; discrete; continuous; nominal; ordinal; interval; ratio; indices. (PDF)
L4 Research Design Research design and its implications for analysis. Begin univariate data analysis segment. (PDF)
L5 Frequency Distributions, Histograms, Stemplots, Boxplots, Measures of Dispersion Summarizing and presenting univariate data; stem and leaf plots; frequency distributions; relative frequency; cumulative frequency; ogives; pareto plots; histograms; pie charts; boxplots. Mean; median; mode; the five number summary; variance and standard deviation; grouped data. (PDF)
L6 Introduction to Probability Basics of probability theory; general rules of probability. (PDF)
L7 The Normal and Binomial Distributions Characteristics of the normal distribution; z-scores; binomial probabilities. (PDF)
L8 Sampling Sampling and probability; bias in sampling; strategies of sampling - random, systematic, stratified, cluster, quota, self-selected, purposive; sampling in the U.S. census; sampling strengths and weaknesses; geographic aggregation; margin of error. (PDF)
L9 Basics of Confidence Intervals and Tests of Significance Introduction to inference; estimating a population mean; estimating a population standard deviation; the standard error; t distribution; estimating with confidence; how confidence intervals behave. (PDF)
L10 Recap: CIs and Inference for Means Review the theory of sampling distributions and confidence intervals for means. (PDF)
L11 CIs and Inference for Proportions and Hypothesis Testing Proportions: estimating for population, confidence intervals. (PDF)

Hypothesis testing: The null and alternative hypotheses; hypothesis testing with samples; one and two tailed tests; determining sample size; p-Values.
L12 Testing the Difference Between Two Groups Issues with inference. Differences of means: independent and dependent samples, equal vs. unequal variances. (PDF)
L13 Testing the Difference Between Two Groups (cont.) Differences of proportions. Discuss mid-term and paper requirements. (PDF)
L14 Review for Mid-term Review of all major concepts to be covered on the mid-term exam.
L15 Mid-term Exam
L16 Analyzing Categorical Data Constructing contingency tables - 2 way; marginals; percentage difference; larger contingency tables; chi-square statistical significance. (PDF)

Discuss final paper descriptive analysis due.
L17 Scatterplots and Correlation Exploring bivariate relationships; r. (PDF)
L18 Introduction to Regression Analysis Simple linear regression; terminology and interpretation; causation vs. association; predicted values; r square. (PDF)
L19 Regression (cont.) The assumptions of linear regression. (PDF)
L20 Multiple Regression Partial slopes, adjusted R2, and assumptions for multiple regression. (PDF)
L21 Multiple Regression (cont.) Constructing predictive models; SPSS output; regression output and data management. (PDF)
L22 Spatial Data Analysis GIS; GeoDa; spatial autocorrelation.
L23 The Census How the Census is conducted, organized, and used in practice.
L24 Quiz Regression quiz and Q&A on final paper.
L25 Research Design and Regression Analysis Example application of research involving regression technique applied to a contemporary urban topic.
L26 Wrap-up Review and wrap-up. Putting your QR skills to work.