Module 1.1 - Introduction to Statistics PDF

Introduction to Statistics LESSON 1 – PART 1 Outline: Importance of Statistics Overview and Definition of Statistics Division of Statistics Data Sets Constant vs Variable Classification of Data Classification of Variables What is the importance of statistics? What is the use of st...

Introduction to Statistics LESSON 1 – PART 1 Outline: Importance of Statistics Overview and Definition of Statistics Division of Statistics Data Sets Constant vs Variable Classification of Data Classification of Variables What is the importance of statistics? What is the use of statistics, will I ever use it, will my employer really care if I did statistics when I was seventeen, or will statistics come up as a question in my job interview? I don't like numbers, why should I like numbers just for the sake of liking numbers and liking statistics. I like art, I like music, I like partying, I like hanging out with my friends doing absolutely nothing, statistics is just not me, period. Statistics plays an important role in your day to day life without you noticing it. What you usually refer to as probability is very important, maybe the reason you don't like is only simple miscommunication and misunderstanding.  statistics plays a major role in your DNA gene gambling your life savings playing the lottery choosing a career making decisions such as where to live who to marry or even stay single Statistics can help define disease such as cancer, diabetics, and even COVID Statistics are everywhere from presidential run, to election, to the simple things such as where to go on holiday. Statistics is the art of mathematics. Statistics is a weapon when used correctly and fatal when used incorrectly. We are bombarded day to day with numbers from jobless numbers to inflation to the number of deaths from a specific disease, but they are just numbers. However, analyzing these numbers can unlock hidden information and that is the beauty of statistics. If you want to understand the number beyond the news, and the growing number of data such as Facebook ads, number of followers, how to be an influencer, advertisement cost and effect definitely you need to understand statistics. Overview and Definition of Statistics What is Statistics? Science of data Data are numbers with context It can be broken down to three branches;  Data analysis  Probability  Statistical Inference Overview and Definition of Statistics What is Statistics? Data - It is a collection of facts - Consists of information coming from observations, counts, measurements or responses. Statistics - Uses data to gain insight and draw conclusions - It is the science of collecting, organizing, analyzing and interpreting data in order to make decisions Overview and Definition of Statistics What is Statistics? A branch of mathematics that examines and investigates ways to process and analyze the data gathered. Provides procedure in data collection, presentation, organization, and interpretation to have a meaningful idea that is useful to decision- makers. Overview and Definition of Statistics What is Statistics?  A summary of collection of numerical facts  A general body of techniques for Collecting / Gathering Organizing / Assembling Presenting Analyzing Interpreting Making conclusions and generalizations  Division of Statistics Descriptive Statistics is the totality of methods and treatments employed in the collection, description, and analysis of numerical data. The purpose of a descriptive statistics is to tell something about the particular group of observation. Inferential Statistics is the logical process from sample analysis to a generalization or conclusion about a population also called statistical inference or inductive statistics.  Data Sets Population - It is the collection of all outcomes, responses, measurements or counts that are of interest. Sample - It is a subset of the populations.  Data Sets Population and Sample A Population consists of all the members of the group about which we want to draw a conclusion, while sample is a portion, or part of the population of interest selected for analysis.  Data Sets Population and Sample Example: Population: All students taking Statistics classes at UE-Caloocan Sample: All students in CIT2201 section IT4C  Data Sets Parameter - It is a description of a population characteristic. (numerical descriptive measures of a population) Statistic - It is a description of a sample characteristic. (numerical descriptive measures from a sample)  Constant and Variable Constant is a characteristics of objects, people, or events that does not vary. ◦ For example: the temperature at which water boils (100 degrees Celsius) is a constant. Variable is a characteristics of objects, people, or events that can take of different values. ◦ It can vary in quantity ◦ For example: Weight of people ◦ or in quality ◦ For example: Hair color of people  Classification of Data How do we classify data? Types of Data Qualitative Qualitative Variable also known as categorical variable; are measurements for which there are no natural numerical scale, but which consists of attributes, labels, or other non- numerical characteristics. Quantitative Quantitative Variable also known as numerical variable; are numerical measurements that arise from natural numerical scale. Types of Data Example:  Classification of Variables Experimental Classification A researcher may classify variables according to the function they serve in the experiment. ◦ Independent variables are controlled by the researcher, and expected to have an effect on the behavior of the subjects. The independent variable is also called explanatory variable. ◦ Dependent variables is some measure of the behavior of subjects and expected to be influenced by the independent variable. The dependent variable is also called outcome variable. Example: Example:  Classification of Variables Mathematical Classification Variables may also be classified in terms of the mathematical values may take within a given interval. ◦ Continuous Variable can assume any of an infinite number of values, and can be associated with points on a continuous line interval. ◦ For example: height, weight, volume, etc. ◦ Discrete Variable consist of either a finite number of values or countable number of values. ◦ For example: gender, courses, Olympic games, etc. Example: END