Class Notes (835,304)
Canada (509,083)
Geography (975)
GGR270H1 (38)
Lecture

GGR270H1-2.docx

6 Pages
99 Views
Unlock Document

Department
Geography
Course
GGR270H1
Professor
Damian Dupuy
Semester
Fall

Description
Variables and Data Variable  Characteristic of the population that changes or varies overtime  Examples include temperature, income, education, etc. o Look at the varying income over a period of time, or look at the varying in different neighborhoods, cities, countries, regions o Compare something that happens in one location compared to an another location, ex. Looking at census tracts o Ex. looking at two variables, looking at education and income how those connect and vary in different locations  Observe and measure variables – before you start testing them  Two Key Categories o Quantitative – numerical e.g. number of students who.. o Discrete (1,2,3,4…) or Conitious (1.5, 2.5,6.76, 3.89) o Qualitative – Non Numerical e.g. male/female, plant species  You can count the number of males or females in class Data  Results from measuring variables – set of measurements  Different Categories – Univariate, Bivariate , Multivariate Variables – Scales of Measurement I  Scale defines amount of information a variable contains and what statistical techniques can be used o EXAM: give a statistical problem and ask what statistical tests should be used. Two key pieces: what is the scale of in cremations are the variables measured at and how many sample am I dealing with? These two questions will allow you narrow down which test you can use.  Four Scales o Nominal o Ordinal o Interval o Ratio o ( ^ those are lowest to highest)  lowest has the least amount of information  you collect your data at the highest scale of information , just because you are able to compress them later on, you choose the highest scale based on what you are looking for o Nominal  Lowest scale of measurement , no numerical value attached  Classifies observations into mutually exclusive ( when grouing, they can only fit in one group and only one group alone) and collectively exhaustive ( there must be a group, where the values can fall under) groups  Simply the name or category of the variable – you make categories and give a numerical value , you see the frequency of your observation  E.g. Occupation Type, gender , place of birth ( these are categories, and you just count how many people are in each of those categories )  Ex. for Occupation Type – how many people are in management, how many people are in general labour? o Ordinal  Stronger scale as it allows data to be ordered or ranked  E.g. look at the 12 largest towns in a region, income by group (high, middle, low) – the process of ranking is ordinal level  The counts yield more information , because you are able to weight each value o Interval  Unit distance separating numbers is important  You can have a unit scale , each number has a lot more weight attached to them  E.g. Temperature (C or F)  But does not allow ratios and does not have a “true” Zero . ex. 10 degrees is warmer than 5 degrees , but its not necessary double the temperature. The only time you can have a zero with temperature is when you use the Kelvin Scale – this would not be a interval scale because there is a definite zero. o Ratio  Strongest scale of measurement  Ratios of distances on a number scale – you can say something is double something  Presence of an absolute “Zero”  E.g. how much a individual pay for rent a month, is a ratio scale  Anytime you can have zero as a value, then it’s a ratio scale o In practice, we consider interval/ratio scales together o Ex. You get the value of rent per month from a person that is ratio , and then you can convert to Ordinal by saying what category they fall under Describing Data I Graphs  Pie charts o Circular graphs where measurement are distributed among categories o You slice based on the frequency of the observation o E.g. counting the number of people that use different types of transit – Distribution of Transit Use : cars, bikes, public transit  Bar Graph o Graph where measurements are distributed among categories o Ex. arranging how many students got a A ,B , C etc, in a course Relative Frequency Histogram I  Graphs quantitate, rather than qualitative data  Vertical axis (y) shows “how often” measurements (frequency ) fall into a particular class or subinterval.  Classes are plotted on the horizontal (x) axis  Rules of thumb o 5 to 12 intervals or categories (Anything more, gets too complex) o 1 + 3.3 Log 10 of observations) – to figure out how many groups you need o Must be mutually exclusive and collectively exhaustive (every element in your data set must be able to fit in a class ) – if they don’t fit , then
More Less

Related notes for GGR270H1

Log In


OR

Join OneClass

Access over 10 million pages of study
documents for 1.3 million courses.

Sign up

Join to view


OR

By registering, I agree to the Terms and Privacy Policies
Already have an account?
Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.

Add your courses

Get notes from the top students in your class.


Submit