Ateneo - UPO

Statistics: basic statistical knowledge, including random variables, descriptive statistics, probability, distributions and statistical inference. Working with data: use of the R software for statistical analysis and graphics.

OpenIntro Statistics https://www.openintro.org/book/os/

The module aims to provide students with a oundation in basic statistical concepts and data analysis, with the support of the R programming environment.

Basic mathematics

Lectures, R computer labs.

The written examination will be a DIR-based examination. The statistics section will consist of questions assessing both theoretical knowledge and the practical application of the concepts and methods covered throughout the course, including statistical analyses and procedures.

The statistics section will be worth a total of 32 points, corresponding to a maximum of 16 points in the final grade.

Questions may be presented in different formats, including:

* text-response questions;
* numerical-response questions;
* multiple-choice questions (single best option);
* true/false questions.

Individual questions may combine two or more of these formats. Questions may carry different weights depending on their level of difficulty and complexity.

Where necessary, specific supporting materials, such as formulae or R code, may be provided directly within the examination. The written examination will be administered together with the examinations for the other modules. Further details regarding the organization and administration of the examination will be provided on the corresponding page of the integrated course examination.

Statistics: Basic concepts of descriptive statistics. Univariate descriptive statistics, including the mean and other summary measures, and graphical representations of data, including histograms and boxplots.

Bivariate data analysis and regression models.

Probability and discrete random variables. Continuous random variables. The normal distribution and areas under the normal curve. Standardization. Confidence intervals and the Student’s t-distribution.

Statistical hypothesis testing. The t-test and two-sample t-test. The p-value. One-sided and two-sided alternative hypotheses. Chi-square tests. Analysis of variance (ANOVA).

R: Introduction to R. Importing data from Excel into R. Data visualization using base R and the ggplot2 package. Data simulation. Statistical analysis using R.

Students will be able to identify and select appropriate statistical procedures for given datasets and independently perform statistical analyses.