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title =__The-Key-Words-and-Definitions-For-Elementary-Statistics__

*[#red] Lecture-1 Part-1: **The-Key-Words-and-Definitions-For-Elementary-Statistics**
* **Data**
** When we are talking about the **data** in this class (statistics lectures) that means **observations**
** **Definition:** Any **observations** that have been collected

* **Statistics**
** Collect data
***[#pink] You have to collect the data appropriately, so that your data is not **biased data**
***:*If you Collect data and do not know what to do with it, then it's **wasted time and useless data.**
*Why? because you don't know **how to  analyze, interpret and draw conclusions from data.**
*__Then collected data does not matter at all and data itself does not matter.__;

** Analyze data
** Interpret data
** And draw conclusions from data

* **Population**
** Complete set of all elements to be studied (OR being studied)
** **Is it necessary to study each element of the set? No**

*[#pink] **Parameter and statistic**
** **Parameter:** Characteristic (trait) of a **Population**
** **Statistic:** Characteristic (trait) of a **Sample**
**:**Example:** These two **(parameter and statistic) could be talking about the same thing.** It just depends on what **group (population or sample)** you are referring.
E.g. if you find out that everybody's hair color in America is blond then it's **parameter**, everybody's hair color is blond. This will be characteristic of entire population.
__But if you take a **sample** from the **population** and then talk those **characteristics** then we refer them as **statistics**__;

* **Sample**
** is generally smaller than **population** OR **sample** is some subset of the **population**
**[#pink] **Statistics** generally deals with the **samples**
***[#pink] **Population** is usually too big or huge for you to get any information in a timely manner
***[#pink] Your **sample** must be **random** so that it is not **biased data**. In other words, when you collect data make sure that you collect it randomly

* **Consensus**
** is collecting from **every member** of a **population**
**[#pink] **Statistics** generally deals with the **samples**
***[#pink] **Population** is usually too big or huge for you to get any information in a timely manner


*[#red] Lecture-1 Part-2: **Exploring Categories of Data, and levels of Measurement**
*[#pink] **Types of data**
**[#gold] **1. Qualitative Data (is categorical)**
*** **Definition:** Data that is **Non-Numeric**
*** **Examples:** Gender, Color, Race, Religion, Ethnicity, Army Rank, Zip Code (some **data** like **zip code** might have numbers in them but still they are **Quantitative**)
*** **Math operations** are **meaningless on Qualitative data** (or categorical data), you can't add, subtract, multiply, divide qualitative data
**[#gold] **2. Quantitative Data**
*** **Definition:** its numerical data
*** **Examples:** Height, weight, wages, MPH (miles per hour), Temperature, Time
*** **Math operations** are **meaningful on Quantitative data**, you can add, subtract, multiply, divide **quantitative data**
***[#lime] **2-Types of Quantitative Data**
****[#crimson] **1. Discrete Data**
***** **Definition:** **Countable OR finite** (meaning there are only certain number of elements/values you can pick from)
***** **Example:** does a chicken lays half eggs, would you like to buy half watching machines, rolling a dice, etc.
***** Usually a **count**

****[#crimson] **2. Continuous Data**
***** **Definition:** **Infinite number of values or elements** (not countable)
***** **Example:** Temperature, Length, etc.
***** Usually a **__Measurement__**
****** Measurements are not easy, depends on tool used and how specific you want to be

* **4-Levels of Measurement**
**[#white] **1- Nominal Level/Data**
*** **Definition:** Can be **categorized** but **__not__ ordered**
*** **Example:** Religion, Race, etc.

**[#pink] **2- Ordinal Level/Data**
*** **Definition:** Can be **__Ordered__**, but the **differences (subtraction)** are meaningless
**** Subtraction meaningless: Saying one is better than another does not make a lot  of sense
*** **Example:** Rank, Color (spectrum, you can order colors but can't subtract them because differences are meaningless )

**[#lime] **3- interval Level/Data**
*** **Definition:** Can be **__Ordered__** and **differences (subtraction)** are meaningful, but there is no **__"Natural Zero"__**
****:**"Natural Zero:"** A natural zero means if you get a **__zero,__**
you have absolutely none of that quantity
(whatever that quantity is) you are talking about.;
****:**Example:** --Bank balance, Age,-- etc. But not
<color #lime>**Temperature**</color> (if your thermometer reads zero temperature
does that mean **extinction of temperature?** No, it means
something on the scale). So, <color #lime>**Temperature**</color>  does not have
**"Natural Zero"**;
*** **Example:** --Bank balance, Age,-- etc. But not **temperature**, So, **temperature is interval data**

**[#gold] **4- Ratio Level/Data**
*** **Definition:** just like **interval** but with a **__"Natural Zero"__**
****:**"Natural Zero:"** A natural zero means if you get a **__zero,__**
you have absolutely none of that quantity
(whatever that quantity is) you are talking about.;
****:**Example:** Bank balance, Age, etc. But not
<color #lime>**Temperature**</color> (if your thermometer reads zero temperature
does that mean **extinction of temperature?** No, it means
something on the scale). So, <color #lime>**Temperature**</color>  does not have
**"Natural Zero"**;
*** **Example:** Bank balance, Age, etc.\n are **ratio data** But not --**temperature**--

*[#red] Lecture-1 Part-3: **Sampling Techniques: How to Develop a __random Sample__**


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