93064 - Statistics

Learning outcomes

The aim of the course is to introduce the elementary concepts of descriptive statistics, probability, statistical inference, and linear regression. The course will provide students with the basic knowledge to develop applied quantitative analyses of complex social and economic phenomena such as those characterizing the modern tourism sector and the urban economy. Prerequisite is knowledge of basics of Mathematics.

Course contents

1st half

Introduction to the R language and the software RStudio. Data frames, observations, variables, computing and interpreting means.

Basics of estimating causal effects with randomized controlled trials. Randomized experiment, treatment group, control group. Examples and case studies in R. Difference-in-means estimator.

Inferring population characteristics via survey research. Sample, population, random sampling, frequency table of a variable, table of proportions, histogram of a variable, density histogram, descriptive statistics (mean, median, standard deviation, variance), z-score, correlation between two variables.

Predicting outcomes using linear regression. The linear model. Outcome and predictor variables. Fitted linear model, estimated intercept and estimated slope. Linear regression with binary outcome variables. Connection to the difference-in-means estimator.

Basics of estimating causal effects with observational data. Confounding variables.

2nd half

Basics of Probability theory. Probability distribution, Bernoulli distribution, Normal distribution, probability density function of the Normal distribution, Standard normal distribution. Sample mean. Law of large numbers and central limit theorem. Sampling distribution of the sample mean.

Quantifying uncertainty. Parameter, estimate, estimator. Sampling distribution of an estimator. Standard error of an estimator. Confidence intervals. Hypothesis testing.

Readings/Bibliography

Text book:

Data Analysis for Social Science. A friendly and practical introduction. Alena Llaudet, Kosuke Imai. (Princeton University Press)

Teaching methods

Frontal lectures with the help of slides, blackboard. You will use your own laptop in class for the practical sessions with Rstudio. The classroom is wired, so bring your own laptop at class.

Considering the nature of the activities and the teaching methods adopted, the attendance of this training activity requires all students to participate in the safety modules 1 and 2 on studying places [https://elearning-sicurezza.unibo.it/ ] in e-learning mode.

Assessment methods

Exam aim

Evaluate whether students:

1) are able to recognize and interpret quantitative information; in particular, read and understand quantitative data in various formats, communicate the meaning of quantitative data and the results of data analysis;

2) understand the theoretical basis of quantitative reasoning. In particular, explain the basic concepts of quantitative reasoning, such as variables, constants, and estimates; understand how inferences are drawn from quantitative analysis and recognize the limitations of quantitative methods;

3) understand the practical application of quantitative data analysis. In particular, whether they are able to determine and use appropriate quantitative methods to solve problems and accurately interpret the results.

 

Grading policy

Minimum passing grade: 18/30; maximum passing grade 30/30.

Exam options

Option A: Homework + Midterm + Final. The final grade is the (equal weight) average of three grades: Homerwork, Midterm and Final exam.

Option B: Midterm + Final. The final grade is the average of Midterm exam (covering 1st half) and Final exam (covering 2nd half).

Option C: Total exam. The final grade is determined by the Total exam only (covering both 1st and 2nd half).

Students can choose between A, B and C. Option A is highly recommended for regular attendants. Students choosing either A or B must complete the exam within the 1st exam date ("appello") in summer, meaning that a sufficient grade obtained at midterm will not be valid for the 2nd "appello". NOTE: Students choosing to do the homework (i.e. option A) will be given a shorter set of questions at midterm and final exam.

Details on homework, midterm and final exams

Homework. Four homework problems (HW) will be assigned by the teacher every second week (2 HWs before midterm and 2 after midterm). The average grade (averaging over the 4 HWs) will be in a scale from 18 to 30 and will be communicated at the end of the course. Students who get maximum grade at each HW get an extra bonus. More info in class about the policy applied to collaboration between students (which is permitted under some conditions) and late submissions.

Midterm exam. It covers 1st half, grades from 18 to 30. It is a computer-based quiz taken in a University LAB, containing multiple choice questions and open-ended questions;

Final Exam. It covers 2nd half, grades from 18 to 30. It is a computer-based quiz taken in a University LAB, containing multiple choice questions and open-ended questions.

 

Enrollment, grades and registration, rejecting grades.

Students must enroll to an exam date via almaesami website; you can do this until 3 days before the exam date.

Students will be notified via email of their grades, which are are published on almaesami, and the date set for registration. Registration usually takes place a week after publication.

Students having passed the exam successfully can reject the grade once. To this end, they must email a request to the instructor within the date set for registration. The instructor will confirm reception of the request within the same date. Rejection is intended with respect to the final grade, not midterm grades.

 

Teaching tools

Slides, blackboard, laptop for practicing Rstudio.

RStudio is based on the R language. Both R and RStudio are free softwares that are installed in lab computers, I recommend students download and install both R and RStudio in their laptops. We will use RStudio Desktop version. First install R and then RStudio Desktop.

 

To download R

https://cran.r-project.org

To download RStudio Desktop:

https://posit.co/download/rstudio-desktop/

Office hours

See the website of Massimo Ventrucci