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Length (in minutes) of a country song
Continuous
Temperature on a randomly selected day in Memphis, TN
Continuous
Temperature on a randomly selected day in Memphis, TN
Continuous
Points scored in an NCAA basketball game
Discrete
How varying the amount of an explanatory variable affect the value of response variable
Explanation
Observational study
Measures the value of the response variable without attempting to influence the value of either the response or explanatory variables. That is, in an observational study, the researcher observes the behavior of the individuals without trying to influence the outcome of the study.
Designed Experiment
if researcher assigns the individuals in a study to a certain group, intentionally changes the value of an explanatory variable, and then records the value of the response variable for each group
Confounding
occurs in a study when the effects of two or more explanatory variable are not separated. Any relation that may exist between an explanatary variable and the response variable may be due to some othervariable or variables not accounted for in the study.
Ex: Desire for a type of food and how hungry the subject is at the time of the study
Lurking variable
is an explanatory variable that was not considered in a study, but that affects the value of the response variable in the study. Lurking variables are typically related to explanatory variables considered in the study.
3 major categories of observational studies:
1) Cross-sectional studies
2) Case-control studies
3) Cohort studies
1) collect information about individuals at a specific point in time or over a very short period of time
2) retrospective they require individuals to look back in time or require the researcher to look at existing records. In case control studies individuals who have a certain characteristics may be matched with those who do not.
3) identifies a group of individuals to participate in the study(cohort). Observed over a long period of time. Characteristics about individuals are recorded and come individuals will be exposed to certain factors and others will not. Downside since longer study people drop out leading to scewed results.
Census
list of individuals in a population along with certain characteristics of each individual.
Random Sampling
Subjects are selected using a chance method or a random number.
Systematic Sampling
Subjects are selected using a system of every kth number after the beginning number is selected randomly.
Stratified Sampling
Subjects are selected by first dividing up the population into groups (called strata) and subjects within each group are selected randomly.
Cluster Sampling
Subjects are selected by using "intact" groups that are representative of the population. Used
when the population is large OR population resides over a large geographic area.
Statistics
Data
numeric values that are gathered through measurements and/or observations.
Data Set
a collection of data values
Data Value (datum)
an individual value in a data set
Population
Entire group to be studied
Ex: people, animals, foods,
Individual
person or object that is a member of the population being studied
Sample
is a subset of the population that is being studied
Statistic
numerical summary of a sample
Descriptive statistics
consist of organizing and summarizing data. Described data through numerical summaries, tables, and graphes.
Examples include: the U.S. Census, Special Interest, Group Studies, Marketing Studies, Consumer
Habits, etc
Inferential Statisitics
uses methods that take a result from a sample, extend it to the population, and measure the reliability of the result.
Ex: Games of chance, insurance industry, simulations, consumer taste,
Parameter
numerical summary of population
Process of Statistics
1. Identify the research objective: A researcher must determine the questions he or she wants answered. The questions must be detailed so that it identifies the population that is to be studied.
2. Collect the data needed to answer the questions posed in #1
3. Describe the data
4. Perform Inference: Apply appropriate techniques to extend the results obtained from the sample to the population and report a level of reliability of the results
Variables
are the characteristics of the individuals within the population
Qualitative, or categorical, variables
allow for classification of individuals based on some attribute or characteristic
Examples are: gender, geographic area, religious affiliation, membership in a group, etc
Quantitative variables
provide numerical measures of individuals. The values of a quantitative variable can be added or subtracted and provide meaningful results.
Examples : age, weight, height, GPA, IQ, distance, payments, tax owed, etc.