Introduction to Statistics

Introduction to

STATISTICS

Objectives: Define terms Identify types and kinds

of data Infuse the relevance of

statistics

Statistics

Statistics

Statistics is a branch of mathematics concerned with the techniques by which information is collected, organized, analyzed, and interpreted.

Two Major Divisions of Statistics1. Descriptive Statistics – is concerned with the collection, classification, and presentation of data to be able to summarize and describe the group characteristics of the data.

Ex: measures of central tendency, measures of variability, skewness, etc.

2. Inferential Statistics – refers to the drawing of conclusion or judgment about the population based on a representative sample taken from the same population

Ex: hypothesis testing using z-test, t-test, analysis of variance, etc.

Steps in Statistical Investigation

1. Collection of data2. Processing of data3. Presentation of data4. Analysis of data5. Interpretation of data

Steps in Statistical Investigation1. Collection of data – process of

obtaining or gathering numerical data

2. Processing of data – organizing data to show significant characteristics

3. Presentation – in the form of tables, graphs, and charts

4. Analysis of data – method of drawing from the given data relevant information from which numerical description can be formulated.

5. Interpretation of data – refers to the task of drawing conclusions from the analyzed data.

Data or information are obtained through interview or surveys, researches, experiments, and a lot more. It is the measured variable from a set of experimental units, or a set of measurements

Types of Data1. Primary data – information

gathered directly from an original source

ex: autobiographies, diaries, business entities and private and public agencies

Types of Data2. Secondary data –

information taken from existing records

ex: published books, newspapers, magazines, theses and dissertations

Classification of Statistical Data1. Nominal data – are numerical in

name only because they do not share the properties of numbers we deal with in ordinary arithmetic.

ex: designation of marital status as 1, 2, 3, or 4 for single, married, widowed or divorced

Classification of Statistical Data2. Ordinal data – numbers indicate

rank order of measurements but they do not indicate the magnitude of interval between the measures.

ex: order of finish in races, grades for achievement, body frames (small, medium, large)

Classification of Statistical Data3. Interval data – numbers represent

equal units between measurements

ex: temperature readings

Classification of Statistical Data4. Ratio data – numbers represent

equal units between measurements and there is an absolute zero point. The easiest to find and they include all the usual measurements.

ex: income (measured in pesos, with zero equal to no income at all)

Other Classification of Statistical Data

1. Discrete data – quantifiable expressed by a whole number, an end result of counting- can only assume a finite or countable number of values

ex: number of students, number of days

Other Classification of Statistical Data

2. Continuous data – usually results of measurements- can assume infinitely many values that correspond to the points on a line or interval

ex: height, weight, winning time

Variable is the characteristic that is being studied.Variable is observable characteristic that can be measured or classified.ex. height, grade of students, time, hair color

Two types of variables1. Qualitative variable –

assumes values that can be categorized according to some distinct characteristics or attribute. - it has no numerical value

Ex: color, type of car

Two types of variables

2. Quantitative variable – includes variables that assume numerical values.

Ex. height, weight, length, monthly income