Basic Statistics

is a subset of the population that is being studied

numerical summary of a sample

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

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, 

numerical summary of population

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

are the characteristics of the individuals within the population

allow for classification of individuals based on some attribute or characteristic

Examples are: gender,  geographic area, religious affiliation, membership in a group, etc

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.

way to look at and organize a problem so that it can be solved 

quantitative variable that has either a finite number of possible values or a countable number of possible values. The term countable means that the values result from counting, 1,2,3. A discrete variable cannot take on every possible value between any teo possible values. 

Examples:  # of Baseball Games won  by the Cardinals ,  # of members of a family,  # of  students in a class, # of  leaves on a tree,  # of  digits in a zip code,  # of  stars in the galaxy,  # of people who wear glasses, etc.

quantitative variable that has an infinite number of possible values that are not countable. A continuous variable may take on every possible value between any two values. 

Examples:   a person's temperature, distance between two places,  the speed of an object,  time,  amount of force applied,  etc

if the values of the variable name, label, or categorize. In addition, the naming sceme does not allow for the values of the variable to be arranged in a ranked or specific order. 

EX: Grouping people by their nationality, gender, their religion, zip code of residence, marital  status, etc.

if it has the properties of the nominal level of measurement, however the naming scheme allows for the values of the variable to be arranged in a ranked or specific order 

Ex: Letter Grades  ( A, B, C, D, & F); Rating Scales ( Outstanding, Excellent,  Good, Average,  Poor, Unsatisfactory); Rankings ( General, Colonel, Captain, Sergeant , Corporal, Private)

if it has the properties of the ordinal level of measurement and the differences in the values of the variable have meaning. A value of zero foes not mean the absence of the quanitity. Arthmetic operations such as addition and subtraction can be performed on values of the variable. 

EX: IQ  may be  100 or  110 ( An  IQ of zero does not exist), Temperature may be  90  or  91 degrees ( 0  temperature does not mean  no heat)

if it has the properties of the interval level of measurement and the ratios of the values of the variable have meaning. A value of zero means the absence of the quantity. Arithmetic operations such as multiplication and division can be performed on the values of the varibale. 

Ex: (Examples include height, weight,  area,  # of phone calls, etc.)

is one whose values are determined by chance

is one whose value is chosen free of any influence by the value of other variables or any given situation

is one whose value is determined by the value held by another variable at a given time. 

Quantitative

Quantitative 

Qualitative 

Discrete

Continuous

Continuous

Continuous

Discrete

Explanation

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. 

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

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

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. 

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. 

list of individuals in a population along with certain characteristics of each individual.