Introduction to Data (Data Analytics)...

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1. Data Analytics 128-Corridor Office, 1st Floor, LBS Instructor: Dr. Ayesha Iftikhar [email protected]

2. Reference Books Statistics for Business and Economics, 6 th edition by Paul Newbold, William L. Carlson & Betty Throne. Business Statistics for Contemporary Decision Making, 8 th Edition, by Ken Black Statistics for Business: Decision Making and Analysis, 2 nd Edition by Robert Stine & Dean Foster

3. Introduction to Data Chapter 1

4. Chapter no1. Introduction to Statistics The word Statistics occurs frequently in our everyday lives. Statistics as a science dealing with the collection , analysis , interpretation , and presentation of numerical data . However, drawing meaningful conclusions from data is not an easy task. This requires thorough knowledge of statistics. Statistics helps managers and decision makers to think scientifically .

5. Importance of statistics Unemployment Population growth rate Housing, schooling, medical, research etc… Example : In Submitting a bid for a contract, a manufacturer will not be completely certain of the total future costs involved and will not have knowledge about bids to be submitted by competitors.

6. Why Study Statistics? The world abounds with data. News paper articles and t.v news reports includes statements such as “The McDonald’s average fell 6 points today” or “ 98% of patients in a clinical study did not experience any significant side effects to a new breast cancer drug”. It is becoming the case that in order to obtain an intelligent appreciation of current developments, we need to absorb and interpret substantial amounts of data.

7. An interesting Story Read this interesting blog related to Statistics https://statisticsbyjim.com/fun/statistics-old-love-letters-changing-times/

8. Statistics helps in decision making in a scientific way. The decision-making process consists of the following steps: Collecting relevant information that is as reliable as possible. Selecting the parts of the available information that are most helpful to making rational decisions. Making the actual decisions as sensibly as possible on the basis of the available evidence. Perceiving(observing) the risks in particular decision making.

9. Importance and practice of Statistics Accountancy: auditing, operations research Biology: quantitative investigation of racial identity Economics: design and analysis of experiments: drastic changes in economy Business Administration: system analysis; market research; decision theory Management: quality management; risk management Literature: identification of different styles of author

10. Think about it! Careful wording is very important “The price of IBM stock will be higher in six months than it is now.” “The annual income of a college graduate will be greater than the annual income of an individual without a college education”. Both statements contain a spurious amount of certainty. The price of IBM stock is likely to be higher in six months than it is now.” “The annual income of a college graduate probably will be greater than the annual income of an individual without a college education”.

11. Statistics and business Statistics play an important role in business. A successful businessman must be very quick and accurate in decision making. He knows that what his customers wants, he should therefore, know what to produce and sell and in what quantities. Statistics helps businessman to plan production according to the taste of the customers , the quality of the products can also be checked more efficiently by using statistical methods. So all the activities of the businessman based on statistical information. He can make correct decision about the location of business, marketing of the products, financial resources etc…

12. Statistics can be broadly split into two categories: 2. Statistical Inference Relates to decision making Estimating the values of unknown numerical quantities Future prediction Descriptive Statistics Graphical or pictorial display Condensation or summarization of large set of data into a form that is more readily understood i.e. Tables, summary measures, patterns exhibitions

13. Data &Data Collection Data: facts or figures from which conclusions can be drawn . The worth of both descriptive and inferential statistics in any situation depends on the worth of the available data. Government, business, and scientific researchers spend billions of dollars collecting data.

14. Example of Raw (unsorted) Data

15. Why need Data? The vice president (marketing) has to make a decision about a new product launch. The media manager has to decide about the advertising campaign. When we need to provide more inputs to a given phenomenon under study. Research is always meant for finding out the unknown, and data is the only tool that can provide a platform to find out this unknown.

16. What do the data tell you? How can you use the information? What additional information would make these data more informative?

17. Quantitative (Measurement) data: Data which can be counted or measured is called numerical data because the answer is a number. Quantitative data can be: Discrete data: can only take on individual values e.g. Number of goals scored by a football team on a Saturday, the number of desks in a classroom of a school, marks achieved in a test. Continuous data: measured on some sort of scale e.g. Heights of students in your class, speeds of cars passing a certain point, time taken to complete a 100m sprint. Types of data

18. The number of coins in your pocket The number of tickets sold for a concert The time taken to complete a puzzle The weights of students in your class Dress sizes Number of cars in car park Temperatures of patients in hospital Height of a plant Number of rooms in a house Discrete or Continuous...?!

19. 2. Categorical Data/Qualitative Data: Data which fits into a group or category is called categorical data e.g. “What colour is your car?”. Other examples are: Gender (male/female) Zip codes Country of birth (Ireland/France .....) Favourite sport (soccer/hurling/football...) Types of car (Honda, Toyota....) Credit card numbers The examples of categorical data given are generally referred to as nominal/unordered categorical data.

20. Categorical data in which the categories have an obvious order such as first division, second division etc, is called ordinal data (Think of order) Other examples of ordered/ordinal data are: Income groups Opinion Scales (strongly disagree, disagree, neutral, agree, strongly agree)

21. Determine if the variable is Quantitative or Categorical(Qualitative) Height Arm span Whether or not the individual went to sleep before 12:00am Month of birth Distance from home Whether the individual has a cell phone How many e-mail messages a person has sent in the last 24 hours The age of a bride on her wedding day The number of letters in a person’s last name

22. Data Type Definition Examples Discrete Category data-without order (also called Nominal) Data has a name only Street, road, way, male, female, Category data- ordered (also called ordinal) Data has order, but does not have a numerical scale Very happy, happy, unhappy, very unhappy Whole number data Data can have any whole number value Day 1, Day 2, … Number of Books (0,1,2,3,…) Continuous Measurement data Data can take any numerical value Length of a pencil. (it can be in cm) Time in seconds.

23. Level of measurement There are four level of measurement Nominal Ordinal Interval Ratio

24. Source of Data Primary First hand information Secondary Already collected data, processed and published.