Jan 14
Statistics
1.​ Collecting data (most important step)
2.​ Organizing data
3.​ Analyzing data
4.​ Interpreting results
5.​ Presenting results
Data
Concepts: purposefully collected information
Types: Qualitative (non-numerical)
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Quantitative (numerical)
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Statistical Process
-​ Analysis
-​ Objectives
-​ Goals
-​ Purpose
Jan 16
Review
- Statistical Process
- Information vs Data
- Data Types
- Data Collection
- Understanding the Data Set
Statistical Process
-​ Determine the Analysis objectives/goals
-​ Data Collection
-​ Data Organizing
-​ Data Analysis
-​ Data Results Interpretation
-​ Data Results Presentation
Data = purposefully collected information
Types:
Qualitative: Non numerical (ex: age categories: senior, teen, baby, etc.)
Quantitative: Numerical Data in meaning (ex: phone numbers, age: 11, 23, 46)
Qualitative:
Nominal - labels, terms
Ordinal - ordered, rankings
Data collection
-​ Review the analysis goals/objectives
-​ Data Identification → -What? (Excel Column) -Types
-​ Data Sources → -Primary -Secondary
-​ Data tools → depends on what time of data you are trying to collect
-​ Budget + time
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Respondents
Key Terms
-​ Population
-​ Sample
Sub-group
-​ Sample unit
-​ Column: a data field
-​ Row: a observation/case/student
Jan 21
Key principles
Basic tasks
-​ Sample size
-​ Data filter
-​ Add-in
Key: Data without
-​ Concept
-​ Context
-​ Where?
-​ Whom?
-​ When?
-​ How?
-​ Label
-​ Name of each column
Is
-​ Useless
-​ Meaningless
-​ Worthless
Statistical Process
-​ Analysis/Research purposes
-​ Collecting Data
-​ Organizing Data
-​ Analyzing Data
-​ Reporting
-​ Presenting
Jan 28
Statistical Process
-​ Identify the analysis objectives
-​ Data Collection
… Etc.
Data Collection
-​ Data Identification
-​ What?
-​ Sources
-​ Primary
-​ Secondary
Types
-​ Quantitative
-​ Qualitative
-​ Ordinal
-​ Nominal
Creating Data Structure (scale legend)
Creating Input Template
Designing/Developing the survey/form/tools
Collect a pilot sample
Designing sample size
Finalizing surveys/forms/tools
Distributing survey/forms/tools
Delivering survey/forms to the respondents
Receiving survey/forms and inputting data/start organizing
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Nominal
Ordinal
Example:
Samsung Mobile Phone
Analysis Objectives
-​ Where do customers buy?
-​ Why do they buy?
-​ How satisfied do the customers feel?
-​ Customers' salaries?
Jan 31
Survey Development
-​ Survey Structure
-​ Question Sections
-​ Notes
-​ Respondents
-​ Survey Length
Survey Structure
-​ Introduction
-​ Question Sections
-​ Ending Section
Introduction (How?, Why?, Fixed)
-​ Purpose
-​ Who?
-​ Confidentiality
-​ Assurance
-​ Instruction
-​ Incentive
Question Sections
-​ Open end
-​ Numerical
-​ Qualitative
-​ Close
-​ Rating Scale
-​ Choices: Nominal
-​ Multiple answers
Ending
-​ Thanks
-​ Incentives
Feb 4
Business Environment
-​ External (Customers)
-​ Internal (Employees)
Feb 6
Sample
-​ Population
-​ Entire group
-​ Population size (N)
-​ Sample
-​ Subgroup of the population
-​ Sample does NOT represent the population
-​ Sampling methods
-​ Used to avoid sampling errors
-​ The process to identify who the respondents will be
-​ Voluntary
-​ Convenient
-​ Random
-​ Simple
-​ Stratified
-​ Cluster
-​ Sampling errors
-​ Ex: using data collected from a sample to represent the population
-​ Ex: selecting the wrong respondents
-​ Non-sampling errors
-​ Ex: selecting the right respondents but wrong data collection administration
-​ Sample size
-​ Statistical methods
-​ Experiences
-​ 100
-​ 10%
-​ 1000
50 MCQs
Statistical Process (2 questions)
Data + Data Types
-​ Distinguish between primary and secondary data
-​ Where and when to collect each type of data
-​ DIstinguish between qualitative and quantitative data (concept and
example)
-​ Qualitative data: Nominal (ex: gender) vs Ordinal (ex: levels… education,
income, etc.)
-​ Survey (structure, types of questions, when to use the surveys)
-​ Sample