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Unformatted text preview: Statistics is the collection, analysis, and interpretation of data. Statistical package : software used to describe data and perform statistical tests on data. Commonly used statistical packages include SAS, SPSS, MINITAB, Excel, STATA The study of Statistics is divided into 2 areas: A)Descriptive Statistics and B) Inferential Statistics. A) Descriptive Statistics deals with the organizing and summary of data. Ex: A parameter is a number that describes some characteristic of a population. A statistic is a number that we can calculate purely from a sample. B) Inferential Statistics : Uses sample statistics to draw inferences about population parameters. Populations are usually too large, and it is not practical to measure their parameters directly. We therefore calculate a statistic from a representative sample drawn from the population, and use it to obtain information about the population parameter. The population is the complete set of observations, patients, measurements, etc. about which we would like to draw conclusions. In a census , measurements from the entire population are used. N is used to designate population size n is used to designate sample size A variable is any characteristic of an object that can be measured. Its values may change. (Ex: blood pressure, heart rate) An independent variable (x) or explanatory variable or predictor variable is one that is manipulated in an experiment. It is thought to influence the dependent variable (y) which is the resulting outcome. Sampling Techniques : Simple Random Sample : Every possible sample of the same size has the same chance of being selected. 1 Stratified Random Sampling: Depending on the focus of the study, members of the population are divided into two or more different subsets, called strata, that share a similar characteristic such as age, gender, ethnicity, political preference. A random sample is then selected from each of the strata. Usually, we select a number from each stratum that is proportional to the breakdown of these groups in the population. Using a stratified sample ensures that each segment of the population is represented. Ex: To collect a stratified sample of the people who live in Suffolk County, you could divide the households into 3 groups: Low Income, Middle Income, High Income. Then we can randomly select households from each level. In addition, if you know that 50% of the households are low income, 40% are middle income and 10% are high income, then it makes sense to choose your sample such that it has approximately the same proportions. Ex: Problem 2.32, Pg. 118, Aliaga and Gunderson A sociologist wants to learn about the opinions of employed adult women on government funding for daycare. He has a list of 520 employed women, 20% of whom are members of a local business and 80% of whom are members of a professional club. He decides to randomly select 25 women members of the local business and 75 women members of the professional club to form a sample of 100 employed women. the professional club to form a sample of 100 employed women....
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 Fall '08
 AdrienneChu
 Standard Deviation, Probability theory

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