Final Flashcards
Canadian Census
Conducted to gain an idea of the population
- done every 5 years
- random
Primary sources for Census data
Socioeconomic
- familes
- income
- status
-education
- minorities/ immigrants
Geographic units in census data
1: Dissemination block: bound by roads or other boundaries (the smallest)
2: Dissemination area: one or more dissemination blocks with avrg. population of 400-700
3: Census tract: Larger areas with populations between 2500-8000 or centres with 50 000+ peoples
Statistics
Collection, classification, presentation and analysis of numerical data to draw valid conclusions and decisions
Sats in Geography
Describe and summarize spatial data, asses general patterns, and make inferences about population
Populations can be:
Finite: Bounds of the population are known
Infinite: Bounds of the population are unknown
Varibles
Properties or characteristics of each given phenomenon/object to be measured
- can be Continous (fall between 2 values) and discrete (determined by counting)
Descriptive statistics
Provide easy-to-understand characteristics for particular data
- measure central tendency (represent the centre/ typical frequency value) (mean, median, mode)
Measure of dispersion and varibility
Provide an indication of the spread of variability of data
- range: difference between high and low
- deviation: difference between each value and mean
Hypothesis testing
Infromed explanation or prediction about something
- informed
- has to be testable (to see if true/false)
- has to be falsifiable (possibility that it can be proven wrong)
Steps to hypothesis testing
1: State the null and alternate hypothesis
2: Select the appropriate test
3: select level of significance
4: Delineate regions of rejection and non-rejection of null
5: Calculate test
6: Make decision regarding null and alternate hypothesis
types of stat errors
Type I: Decision is made to reject the hypothesis and false when it is true
Type II: Decision is made not to reject a null hypothesis as false when it is false
Correlations
the relationship between 2 or more variables
- scatterplot typically used to express
- correlation doesn’t equal causation
Scatterplot direction
Positive:
-Increase values in one variable corresponds to increasing values in others
- Decrease values in one variable corresponds to decreasing values in other
Negative/Inverse:
-Increasing value is one variable corresponding with decreasing values of another
- Decreasing values in one variable corresponds to increasing values in another
Covariation
The degree to which covary/vary together
- if covary similarly = data has large covaration and strong correlation
- if show little consistency = weak correlation