BANL 6100: Business Analytics

.title[
# BANL 6100: Business Analytics
]
.subtitle[
## Data & Desriptive Statistics
]
.author[
### Mehmet Balcilar </br> <a href="mailto:mbalcilar@newhaven.edu" class="email">mbalcilar@newhaven.edu</a>
]
.institute[
### Univeristy of New Haven
]
.date[
### 2023-08-28 (updated: 2024-01-24)
]

---

# .red[ .fat[ Data & Desriptive Statistics ] ]

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class: center, middle, sydney-blue

# Overview of Using Data: Definitions and Goals

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## Definitions

**Data:** The facts and figures collected, analyzed, and summarized for presentation and interpretation.

**Variable:** A characteristic or a quantity of interest that can take on different values.

**Observation:** A set of values corresponding to a set of variables.

**Variation:** The difference in a variable measured over observations.

**Random variable/Uncertain variable:** A quantity whose values are .red[not known] with certainty.

**Non-random/Deterministic/Certain variable:** A quantity whose values are .red[known] with certainty.

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## Types of Data &mdash; I

### Population and Sample Data
- **Population:** All elements of interest  
    - Examples: US resident, UNH students, fast food restaurants
- **Sample:** Subset of the population  
     - **Random sampling:** A sampling method to gather a representative sample of the population data.
         - Examples: 100 fast restaurants selected for investigating hygenie standards
---
## Types of Data &mdash; II

### Quantitative and Categorical Data  
- **Quantitative data:** Data on which numeric and arithmetic operations, such as addition, subtraction, multiplication, and division, can be performed.  
    a. *Discrete* (i.e. whole numbers)
	     Examples: student ages (18, 20, 19,…); graduation year (2018, 2021,..)
    b. *Continuous* (i.e. decimal point numbers)
	     Examples: student's GPA (3.4, 2.7, 3.9,…)
- **Categorical data:** Data on which arithmetic operations cannot be performed.  
    a. *Nominal:* For measuring categorical data. There is no order or superiority between values
	      Examples Male/Female, Green/Red/Blue, Yes/No
    b. *Ordinal:* For measuring categorical data that can be somehow ordered
	      Examples: Student grades (A+, A, A-, B+, B, B-,…); Customer satisfaction (Poor, Good, Excellent)

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## Types of Data &mdash; III

### Cross-Sectional and Time Series Data
- **Cross-sectional data:** Data collected from several entities at the same, or approximately the same, point in time.
	      Examples: Average earning per share of 10-K companies in the last 5 years
- **Time series data:** Data collected over several time periods.
    - Graphs of time series data are frequently found in business and economic publications.
    - Graphs help analysts understand what happened in the past, identify trends over time, and project future levels for the time series.
	      Examples: Apple Iphone 14 sales in the last 120 days    
- **Panel/Longitudinal data**: Data involving repeated observations of the same things at different points in time (cross-sectional + time series).
	      Examples: Annual earning per share of IBM, Apple, and Microsoft from 2000 to 2022

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## Cross-sectional data

![:scale 100%](images/lecture2/cross_sectional.jpg)

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## Time series data

![:scale 80%](images/lecture2/time_seris.png)
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## Panel data

![:scale 80%](images/lecture2/panel1.jpg)

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## Panel data

![:scale 80%](images/lecture2/panel2.jpg)

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#End of lecture ... 
#Next topic: Introduction R
#[Mehmet Balcilar](https://www.mbalcilar.net)
#[mbalcilar@newhaven.edu](mailto:mbalcilar@newhaven.edu)