Statistics Lecture 1: Introduction

Lecture 1: Introduction Definition of Statistics: the collection, organization, analysis, and interpretation of data Variable: a characteristic being measured that varies among the persons, places, or objects being studied. Data: the values of observations recorded for variables Types of Variables: 1.​ Discrete Data a.​ Can be nominal, ordinal, or categorized interval or ratio b.​ Eg. income categories 2.​ Continuous Data a.​ Can be interval or ratio b.​ Can be grouped into discrete data c.​ Eg. age Independent Variable: the variable being changed by the experimenter Dependent Variable: the variable that changes as a result of the independent variable N.O.I.R. ❖​ Nominal ➢​ Categories, labels, names ➢​ No implied ordering ➢​ Eg. eye colour, gender, marital status ❖​ Ordinal ➢​ Data has categories that can be ordered ➢​ Eg. Likert scale (pain scale), years of study ❖​ Interval ➢​ Has categories, has order, but data is also evenly spaced. ➢​ There is no absolute zero, meaning that you can have values below zero ➢​ CAN take an average ➢​ Eg. Temperature, IQ scores ❖​ Ratio ➢​ Has categories, has order, data is evenly spaced BUT there is an absolute zero, meaning that zero is the absence of something ➢​ CAN take an average ➢​ Eg. weight, height, measurements Presenting Data -​ Frequency Distribution: the number of times an outcome occurs for each outcome -​ Percentages -​ Relative Frequency: the number of times an outcome occurs divided by total number of observations -​ There are 3 basic ways of presenting data: 1.​ Textual - writing 2.​ Tabular - tables 3.​ Graphical - pie chart, bar chart, histogram, line graphs, scatterplots