Class Notes (808,548)
Canada (493,287)
Geography (945)
GGR270H1 (38)
Lecture 6

Lecture 6 - October 24

5 Pages
Unlock Document

University of Toronto St. George
Damian Dupuy

th Date: October 24 , 2012 SAMPLING –SAMPLING DESIGNS  Number of different sampling designs exist: -simple random (most basic, use some random generation method i.e. a random number table -systematic sampling (take a system approach and rather than randomly select people through random number tables, a system says every 10 person we will choose, i.e. a phonebook) -Stratified (organize sample based on organization of the population, the organization of the sample represents the organization of the population)  Everything has an equal chance of being chosen  Can also have spatial sampling designs (looking at where things happen- i.e. the concentration of lead on a site, involves applying some sort of physical grid) -use Cartesian Coordinates (lines of latitude/longitude) -simple -stratified random (you have your whole map which is then divided into squares, and then randomly select a set of points form those squaresensures coverage of the map –i.e. the deposits of lead) -transect (randomly select lines along a map, and you sample along the lines) Sampling Distribution  Sample statistics will change or vary for each random sample selected  Probability distributions for statistics are called Sampling Distributions (if you take multiple samples and calculate the mean, and plot the means and generate a distribution of those statistics, what you have is a distribution of statistics which is the difference between a sample distribution and a sampling distribution) sample distribution-distribution of actual scores, drawn from one sample sampling distribution- distribution of a statistics, drawn from multiple samples often ask about this difference in EXAMS  A sampling distribution is the distribution of a statistic that is drawn from all possible samples of a given size n  Can be developed for any statistics, not just the mean (but we tend to use the mean) Central Limit Theorem  Sampling Distribution will have its own mean and standard deviation  But…the mean of a sampling distribution has important properties –summarized by Central Limit Theorem  If all samples are randomly drawn, and are independent, then the mean of the sampling distribution of sample means will be the population mean mu -If our sample is large enough, we can use it to predict our population  The frequency distribution of sample means will be normally distributed  What this means for us is that… when the sample size is large, the sample mean is likely to be quite close to the population mean  A large sample is more likely to be closer to the true population mean than a smaller sample  Theoretically the difference between a large sample and a small sample is n=30 (the minimum number of observations to carry out a test) Central Limit Theorem – Variability  Standard deviation of the sampling distribution is equal to the sample standard deviation divided by the square root of the sample size  This is called the Standard Error of the Mean  Indicates how much a typical sample mean is likely to differ from the true population mean  Measures the amount of sampling error  The larger the n the smaller the amount of sampling error (smaller sampling error indicates less variability) the larger the n, the less variability there is= the more peaked the curve is (leptokurtic) Standard Error Standard error of the mean Standard error of a proportion SEp= , Note: q=1-p Central Limit Theorem III  How large is large???  If we have a normal population, it doesn’t matter what our size is, it’ll still be normal  When the population is skewed, the sample size must be large (n greater than 30) before the sampling distribution will become normal Sample Estimation  Statistical inference is concerned about making decisions or predictions about population parameters, using samples  Two ways we do this: -estimation -hypothesis testing (make decisions of the parameter based on preconceived notions, draw conclusions about the literature that might to our research scenario, statements about what we think might be the case, and these hypothesis are then tested)  Estimators are calculated using information from samples  Usually expressed as a formula  Two different types -Point (use the info in our sample to select a value that predicts
More Less

Related notes for GGR270H1

Log In


Don't have an account?

Join OneClass

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

Sign up

Join to view


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.