# Statistics for Enterprise Data Analysis

## Objectives

This curricular unit aims at supplying to the students the theoretical and practical knowledge about methodologies on summarizing data, and parametric statistical inference. Students will explore the core principles of statistics, from both the conceptual and applied perspectives. The students will acquire competences related to descriptive statistics, random variables, sampling and confidence intervals, hypothesis testing, regression model and time series linear models. The students will clearly understand the conditions of applicability of each procedure. The concepts and principles will be applied and discussed using the environment, functions, and visualizations of R using real-world concepts applicable to many industries, including medical, business, sports, insurance, etc.

## General characterization

400089

7.5

##### Responsible teacher

Jorge Morais Mendes

##### Hours

Weekly - Available soon

Total - Available soon

##### Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

NA

### Bibliography

• Kutner, M.H., Nachtsheim, C.J., Neter, J., Li,W. (2004) Applied Linear Statistical Models, 5th edition,  McGraw-Hill.
• Lock, Robin, H., Lock, P. F., Morgan, K.L., Lock, E.F., Lock, D.F. (2017) Statistics: unlocking the power of data. Second edition, Wiley.
• Shumway, R.H., Stoffer, D.S. Time Series Analysis and its Applications with Examples in R, 3rd edition, Springer, 2011

### Teaching method

The curricular unit is based on theoretical and practical lessons. A variety of instructional strategies will be applied, including lectures, slide show demonstrations, step-by-step applications using Microsoft Excel, questions and answers. The sessions include presentation of concepts and methodologies, solving examples, discussion and interpretation of results. The practical component is geared towards solving problems and exercises, including discussion and interpretation of results.

### Evaluation method

1st round: Project (40%) + Final exam (60%).

2nd call: Project (40%) + Final exam (60%).

## Subject matter

The curricular unit is organized in seven Learning Units (LU):

LU1. Descriptive statistics

• Introduction to statistics
• Organizing information
• Frequency distributions
• Descriptive statistics
• Outliers detection
• Qualitative and quantitative random variables
• Samling distributions
• Interval estimation
• Hypothesis testing

LU2. Linear regression model

• Introduction
• Simple linear regression model: definition, estimation, inference and quality.
• Multiple linear regression model: definition, estimation, inference and quality
• Other issu regarding linear regression model

LU3. Time series linear models

• Introduction and basic concepts
• AR models
• MA models
• ARMA models
• Non-stationary and seasonal models (ARIMA e SARIMA)
• Diagnostics and assessment

## Programs

Programs where the course is taught: