This document provides an introduction to data analysis, including definitions of key terms and concepts. It discusses that data analysis is the process of collecting, modeling, and analyzing data to extract insights that support decision-making. It also outlines the two basic types of data - quantitative and qualitative data - and describes different levels and examples of each type. Finally, it discusses the two levels of statistical analysis that can be performed on data - descriptive statistics, which describes and summarizes a sample, and inferential statistics, which is used to make inferences about a broader population.

1. Introduction to Data Analysis 1
 Statistics deals with Data.
 Data (Raw data) is a collection of numbers and attributes.
 When data is processed / manipulated using a statistical
software, it becomes information.

2. Data Analysis
• In our data-rich age, understanding how to analyze and extract true
meaning from our business’s digital insights is one of the primary drivers
of success
What Is Data Analysis?
• Data analysis is the process of collecting, modelling, and analyzing data to
extract insights that support decision-making. There are several methods
and techniques to perform analysis depending on the industry and the aim
of the analysis

3. Types of Data
 Knowing the type of data is important because the nature of data determines
the kind of statistical analysis that can be performed.
 Two Basic Types of data:
1. Quantitative Data
2. Qualitative Data

4. Quantitative Data
 Has to do with quantity or number
 It is obtained from a numerical scale.
 It is split into two; Discrete and Continuous
 Discrete data; Refers to finite countable, listable or exact values.
E.g. number of years one spends in school, number of vehicles passing in a
given time interval, etc.

5. Quantitative Data Cont’d
• Continuous Data; Refers to set of values falling on an interval scale of real
numbers. The set is infinite. I.e. values can not be listed exhaustively. E.g.
weights, heights, Age, temperature measures, Distances etc.

6. Levels of Quantitative Data
Interval Scale
 Has no natural zero
 Addition and subtraction make sense
 Multiplication and Division don’t make
sense
 E.g. just limited to temperatures(moving
from 10 to 20 does not mean doubling the
temperature), dates(Based on arbitrary date
e.g. BC or AD, we have no year zero) etc
Ratio Scale
 Has a natural zero
 It is both discrete and continuous
 Addition, Subtraction, multiplication
and division make sense. E.g. Weights,
heights etc

7. Qualitative Data
 Has to do with measure of quality of object of services.
 It consist of a set of attributes and their levels or categories, sometimes, referred to
as categorical data.
 May be numbers but without specific/real meaning, hence mathematical
manipulations may not make sense.
 It has two levels. i.e. Nominal and Ordinal
 Nominal data; refers to names of categories only. It is data with out rank or
inherent order. E.g. colour, sex, clubs etc

8. Qualitative Data Cont’d
 Ordinal Data; refers to categories that have an inherent order.it is a higher
level of nominal data.
 It has no absolute numerical scale.
 E.g. Likert Scale(Strongly Agree, Agree, Neutral, Disagree, Strongly
Disagree), Ranks in forces, school grades(Div. I, II, III, IV), classes of
degree awards(1st, 2nd lower, 2nd upper, pass or 3rd class) etc.

9. Types of Statistical analyses
• After collecting data there are two levels of Analyses we can run.
1. Descriptive Statistics
2. Inferential statistics

10. Descriptive Statistics
• This is used to describe the sample or summarize the information about
the sample. The common statistics computed are;
• Measures of central tendency(mean, mode and median)
• Measures of dispersion/variability (range, standard deviation)
• Also to organize the data using tables
• Presenting the data using graphs and Pie-charts

11. Inferential Statistics
 This is used to make inferences/conclusions or generalizations about a
broader population.
 Common statistics computed include;
 Measures of correlations(correlation coefficient by Pearson or Spearman)
 Measures of causation/effects (statistical significance)

12. END
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