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Statistics (248)
STAT151 (157)
Lecture

# Ch1and4.pdf

2 Pages
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School
Department
Statistics
Course
STAT151
Professor
Paul Cartledge
Semester
Fall

Description
Section 1.1 Def’n: Statistics: 1) are commonly known as numerical facts 2) is a field of discipline or study Htets,tics is the art and science of learning from data. 3 main aspects of statistics: 1) Design: Planning how to obtain data to answer questions. 2) Description: Summarizing the obtained data. 3) Inference: Making decisions and predictions based on data. 1.2 - Population vs. Sample Def’n: A population consists of all elements whose characteristics are being studied. ste.e.nts samAple is a portion of the population selected for study. e.g. UofA students in this section A parameter is a summary measure calculated for population data. A statisticis a summary measure calculated for sample data. Types of statistics: Descriptive: methods to view a given dataset. e.g. averages, histograms Inferential: methods using sample results to infer conclusions about a larger population. e.g. t-tests, simple linear regression 4.1 Observation and Experimentation Def’n: An observational study is a study where a researcher observes characteristics of subjects in samples from populations of interest. expAeriment is a study where a researcher applies different treatments to different subjects and observes the outcomes. Drawing conclusions: 1. Infer to a larger population (Population) 2. Factor causes change in response (Causal) Both types of study allow for #1, but only a properly designed (and randomized) experiment allows for #2 to be valid. Experiments are not always feasible. 4.2 Sampling Def’n: A sampling frame is the list of subjects in the population from which the sample is taken. A random sample is a sample drawn in such a way that each element of the population has a chance of being selected. If chances are all the same Æ SRS of size n - e.g. A deck of cards: picking a card is a simple random sample. Moreover, placing the card back in the deck is a sample with replacement. Otherwise, there is sampling without replacement. Bias: - sampling bias: samples differ due to sys
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