LearnWithMey
  • 👩‍💻Getting started
  • Introduction
    • What and Why Data Analytics?
    • Data Analyst Roadmap
    • Data Analysis Steps
    • Type of Data Analytics
    • Data Analytics Use Cases
    • Data Analytics Tools
    • Skills Requirement
    • Data Analyst vs Data Scientist
      • Key notes
  • STATISTICAL TECHNIQUES IN BUSINESS & ECONOMICS
    • Chapter 1: What is Statistics?
      • 1.1 What is meant by Statistics?
      • 1.2 Types of Statistics?
      • 1.3 Types of Variables
      • 1.4 Levels of Meaurement
      • Chapter Summary
    • Chapter 2: Describing Data
      • 2.1 Introduction
      • 2.2 Constructing a Frequency Table
      • 2.3 Constructing the Frequency Distributions
    • Chapter 3: Describing Data - Numerical Measures
      • 3.1 Introduction
      • 3.2 The population Mean
      • 3.3 The Sample Mean
      • 3.4 Properties of the Arithmetic Mean
      • 3.5 The Weighted Mean
      • 3.6 The Median
      • 3.7 The Mode
      • 3.8 The Geometric Mean
      • 3.9 Why Study Dispersion?
      • 3.10 Measures of Dispersion
      • 3.11 Interpretation and Uses of the Standard Deviation
      • 3.12 The Mean and Standard Deviation of Grouped Data
    • Chapter 4: Describing Data - Display and Explore Data
      • 4.1 Introduction
      • 4.2 Dot Plots
      • 4.3 Stem-and-Leaf Displays
      • 4.4 Measures of Position
      • 4.5 Skewness
      • 4.6 Describing the Relationship between Two Variables
    • Chapter 5: A Survey of Probability Concepts
    • Chapter 6: Discrete Probability Distribution
    • Chapter 7: Continuous Probability Distribution
    • Chapter 8: Sampling Methods and Central Limit Theorem
    • Chapter 9: Estimation and Confidence Intervals
      • 9.1 Introduction
    • Chapter 10: One-Sample Tests of Hypothesis
      • 10.1 Introduction
      • 10.2 Hypothesis Testing
    • Chapter 11: Two-Sample Tests of Hypothesis
    • Chapter 12: Analysis of Variance
    • Chapter 13: Correlation and Linear Regression
    • Chapter 14: Multiple Regression Analysis
    • Chapter 15: Index Number
    • Chapter 16: Time Series and Forcasting
    • Chapter 17: Nonparametric Methods: Goodness-of-Fit Tests
    • Chapter 18: Nonparametric Methods: Analysis of Ranked Data
    • Chapter 19: Statistical Process Control and Quality Management
    • Chapter 20: Decision Theory
    • Tables ( Z and Student's T )
  • Python for Data Science
    • Handbook
    • Introduction
    • Installation
    • Basic Syntax
    • 2. Introduction to Numpy
  • Excel for Data Analysis
    • Introduction
  • Data Warehousing
    • Introduction
    • Data Warehouse Architecture
  • Data Visualization with Power BI
    • Introduction
  • Data Analysis with Python
    • Importing Datasets
      • Understanding Data
      • Python Packages for Data Sciences
    • Data Wrangling
      • Pre-processing Data in Python
      • Dealing with Missing Values in Python
      • Data Formatting in Python
  • Machine Learning
    • Page
    • 1. Introduction
    • Machine Learning Exercises
  • React
    • Lazy Loading
  • Search Tips
  • Secure Product Design
  • Google like a pro
  • Project Management
  • SEO in React.js
Powered by GitBook
On this page
  1. STATISTICAL TECHNIQUES IN BUSINESS & ECONOMICS
  2. Chapter 1: What is Statistics?

Chapter Summary

Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions.

II. There are two types of statistics.

  1. Descriptive statistics are procedures used to organize and summarize data.

  2. Inferential statistics involve taking a sample from a population and making estimates

    about a population based on the sample results.

    1. A population is an entire set of individuals or objects of interest or the measure-

      ments obtained from all individuals or objects of interest.

    2. A sample is a part of the population.

III. There are two types of variables.

  1. A qualitative variable is nonnumeric.

    1. Usually we are interested in the number or percent of the observations in each category.

    2. Qualitative data are usually summarized in graphs and bar charts.

  2. There are two types of quantitative variables and they are usually reported numerically.

    1. Discrete variables can assume only certain values, and there are usually gaps

      between values.

    2. A continuous variable can assume any value within a specified range.

IV. There are four levels of measurement.

  1. With the nominal level, the data are sorted into categories with no particular order to

    the categories.

  2. The ordinal level of measurement presumes that one classification is ranked higher than

    another.

  3. The interval level of measurement has the ranking characteristic of the ordinal level

    of measurement plus the characteristic that the distance between values is a constant size.

  4. The ratio level of measurement has all the characteristics of the interval level, plus there

    is a 0 point and the ratio of two values is meaningful.

Previous1.4 Levels of MeaurementNextChapter 2: Describing Data

Last updated 1 year ago