Review of Chapters 1-5 - Review of Chapters 1 5 We review...

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Review of Chapters 1- 5 We review some important themes from the first 5 chapters 1. Introduction Statistics - Set of methods for collecting/analyzing data (the art and science of learning from data). Provides methods for Design – Planning / Implementing a study Description – Graphical and numerical methods for summarizing the data Inference – Methods for making predictions about a population (total set of subjects of interest, real or conceptual ), based on a sample
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2. Sampling and Measurement Variable – a characteristic that can vary in value among subjects in a sample or a population. Types of variables Categorical Quantitative Categorical variables can be ordinal (ordered categories) or nominal (unordered categories) Quantitative variables can be continuous or discrete Classifications affect the analysis; e.g., for categorical variables we make inferences about proportions and for quantitative variables we make inferences about means
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Randomization – obtaining reliable data by reducing potential bias Simple random sample: In a sample survey, each possible sample of size n has the same chance of being selected. Randomization in a survey used to get a good cross- section of population. With such probability sampling methods, standard errors tell us how close sample statistics tend to be to population parameters. (Otherwise, the sampling error is unpredictable.)
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Experimental vs. observational studies Sample surveys are examples of observational studies (merely observe subjects without any experimental manipulation) Experimental studies : Researcher assigns subjects to experimental conditions. Subjects should be assigned at random to the conditions (“ treatments ”) Randomization “balances” treatment groups with respect to lurking variables that could affect response (e.g., demographic characteristics, SES), makes it easier to assess cause and effect
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3. Descriptive Statistics Numerical descriptions of center (mean and median), variability (standard deviation – typical distance from mean ), position ( quartiles, percentiles ) Bivariate description uses regression/correlation (quantitative variable), contingency table analysis (categorical variables). Graphics include histogram, box plot, scatterplot
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