STAT 1051, Balaji, Week 1 Notes
STAT 1051, Balaji, Week 1 Notes STAT 1051
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This 2 page Class Notes was uploaded by skenan on Thursday September 8, 2016. The Class Notes belongs to STAT 1051 at George Washington University taught by Dr. Srinivasan Balaji in Fall 2016. Since its upload, it has received 22 views.
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Date Created: 09/08/16
STATISTICS 1051 PROF. BALAJI WEEK 1 NOTES CHAPTER 1: INTRODUCTION What is Statistics? Statistics is the science of collection and analysis of data. Various aspects: Descriptive statistics: o Classifying o Summarizing and organizing Inferential o Analyzing o Interpreting Descriptive Statistics: Numerical and graphical methods are utilized to look for patterns in a dataset and to present that information in a convenient form. No analysis involved. Inferential Statistics: Sample data is used to make estimates, decisions, predictions about the population. Probability theory is a key tool for this analysis. Population Average (unknown) μ Sample mean Use x to estimate μ Fundamental Elements: Population: A collection of individuals or objects that is under study. Sample: A subset of the population. Variable: A characteristic of interest about each individual element of a population or sample. Data: numbers or information with a context. Different characteristics are measured for different individuals in the population or in the sample. There are two types of variables. o Qualitative / Categorical o Quantitative /Numerical Quantitative Data: variables that can assume numerical values (in the true sense) are quantitative variable. Addition/subtraction are meaningful for quantitative variable. Ex; number of students enrolled in GWU since 1990. Waiting time at the Foggy Bottom metro station. SSN of students is not quantitative even thought they are numerical. Qualitative Data: that is not numerical (in the true sense. Observations are categorized into various groups or categories. Ex; blood types of students. Letter grades of students in a class. ZIP codes are qualitative even though they are numerical. Data collection: data can be obtained in a number of ways depending on the situation. It can be obtained from A published source Designated experiment A survey An observational study Sampling: a sample should be representative of a population. It should have characteristics of the typical target population. Simple random sample: a sample in which each element in the population has an equal chance of being selected. Simple random samples are obtained by either; o Sampling with replacement OR o Sampling without replacement. Systematic Sample: a sample in which the first element is picked at random, and then th every k element is picked. Note that there should be an ordering among the elements for us to use this. Stratified Random Sample: A sample obtained by stratifying the sampling frame and then selecting a fixed number of elements from each strata by simple random sampling. Cluster Sample: A sample obtained by sampling some of, but not all of, the possible subdivisions within the population. These subdivisions, called clusters often occur naturally within the population.
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