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STAT 2103 Class Topics Chapter 1 thru Chapter 3 McGlave_Benson_Sincich(3)

# STAT 2103 Class Topics Chapter 1 thru Chapter 3 McGlave_Benson_Sincich(3)

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STAT 2103 Class Topics Chapter 1 What is Statistics? Statistics is the science of conducting studies to collect, organize and summarize data and to draw conclusions from the data. Definitions: An experimental unit is an object (e.g., person, thing, transaction, or event) upon which we collect data. A population is the collection of all objects, animals or persons that are of interest. A variable is a characteristic under study that assumes different values for different elements of the population. Data are the measured values of(a) variable(s). Variables whose values are determined by chance are called random variables . A collection of data values is called a data set . Descriptive statistics is the branch of statistics concerned with collection, organization, summation, and presentation of data. A parameter is a numerical characteristic of a population. A sample is a subset of a population. A statistic is a numerical characteristic of a sample. Inferential statistics is the branch of statistics concerned with inferring the characteristics of populations (i.e., parameter values) based on information contained in sample data sets. Inferential statistics includes estimation of parameters and hypothesis testing. A measure of reliability is a statement (usually quantified) about the degree of uncertainty associated with a statistical inference. Two Types of Variables: Categorical (attribute or qualitative) variable : When a variable name categories and answers questions about how cases fall into those categories, we call it a categorical variable. Example: eye color, race, gender, nationality, species of tree, success/failure. Quantitative variable is numerical in nature. It represents a count or a measurement made on elements of a population or sample. A quantitative variable is discrete if it can assume at most a finite or countable number of possible values. Otherwise it is continuous. 1

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Measurement Scales 1) The nominal level of a measurement classifies data into mutually exclusive (non-overlapping) and exhaustive categories, in which no order or ranking can be imposed on the data. Examples: gender, race, religious affiliation, car color etc. 2) The ordinal level of measurement classifies data into categories that can be ranked; however precise differences between the ranks do not exist. Examples: course grades, ranking of American cities according to livability. 3) The interval level of measurement ranks data, and precise differences between units of measurements do exist: Examples: Measurement of time; the difference between the year 2000 and the year 2001 is the same as the difference between the year 1950 and the year 1951. Fahrenheit temperatures; the difference between 32 degrees F and 40 degree F is the same as the difference between 90 degrees F and 98 degrees F. Data at this level may lack an inherent zero starting point. At this level, differences are meaningful, but ratios are not.
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