Statistics
Home > Flashcards > Print Preview
The flashcards below were created by user
khonka
on
FreezingBlue Flashcards. What would you like to do?

Descriptive Statistics
 cosists of metods for organizing and summarizing information
 ex. construction of graphs, charts, tables...

population
The collection of all individuals or items under cosideration in a statistical study

Sample
That part of the population from which inforamtion is obtained

Inferential Statistics
consists of methods for drawing and measuring the reliability of conclusions about a population based on information obtained from a sample of the population

Classifying Statistical Studies (descr or statis)?
 The study is descriptive  If the purpose of the study is to examine and explore information for its own intrinsic interest only. ( can be performed on both sample and pupulation)
 The study is inferential  if information is obtained from a sample of a population and the purpose of the study is to use that information to draw conclusions about the population.

Observational Study
observing of characteristics and take measurements, as ina sample survey.Can reveal only association.

Designed Experiments
researchers impose treatments and controls and then observe characteristics and take measurements. Can help establish causation

Census
Obtaining information for the entire pupulation of interest. However, it is time consuming and might be impossible.

The two methods for obtaining infromation other than census
Sampling and experimentation

Simple Random Sampling(sample)
 Sim. Ran. Sampling  A sampling procedure for which each possible sample of a given size is equally likely to be the one obtained.
 S.. R. Sample  A sample obtained by simple random sampling
 There are 2 types of Simple Random Sampling
 1. Simple random sampling with replacement  a member of pup. can be selected more, than once
 2. without the replacement  can be selected at most once.

Systemic Random Samping
 Step 1: Divide the populati1on size by the sample size and round the result down to the nearest whole number, m.
 Step 2: Use a random  number table (or a similar devise) to obtain a number, k, between 1m.
 Step 3: Select for the sample those members of the population that are numbered, k, k+m, k+2m, k+3m..

Cluster Sampling
 useful when the members of the population are widely scattered geographically.
 Step1: Divide the population into groups (clusters)
 Step 2: Obtain a simple random sample of the clusters
 Step 3: Use all the members of the clusters obtained in step 2 as the sample.

Stratified Sampling
 more reliable than cluster sampling. First divided into subpopulations, called strata. Then sampling is done from each sratum. Uses proportions called stratifeid random Sampling with proportional allocation
 Step 1: Divide the population into subpopulations (srata)
 Step 2: From each sratum, obtain a simple random sample of size proportional to the size of the stratum; that is, the sample size for a stratum equals the total sample size times the stratum size divided by the populations size.
 Step 3: Use all the members obtained in Step 2 as the sample.

Principles of Experimental Design
 Experimental Units or subjects  those on whom performed the experiment. IF on humans experimental units, if non humans  subjects.
 Control  who is given the placebo
 treatment group  who is given the treatment
  Randomization  The exper. units shold be randomly divided into groups to avoid unintentional selection bias in constituting the groups
  Replication  A sufficient number of experimental units should be used to ensure that randomization creates groups that resemble each other closely and to increase the chances of detecting any differences among the treatments.
 Treatment  experimental condition

Terminology of Experimental Design
 response variable  experimental outcome that is to be measured or observed.
 Factor  types of variables whose effect on the response variable in the experiment (ex. Ph and irrigation regime)
 Levels  The possible values of a factor ( ex. with p4, no P4, light, none, med...)
 Treatment  Each experimental condition. For onefactor experiments, the treatments are the levels of the single factor. (ex. 10 treatments, no p4/none, no p4/light, with p4/none....)

Completely Randomized Design
all the experimental units are assigned randomly amon all the treatments.

Randomized Block Design
experiemtanl units that are similar in ways that are expected to affect the response variable are grouped in blocks. Then the random assignment of experimetanl units to the treatments is made block by block. The experimental units are assigned randomly among all the treatments separately withing each block.

Variable
A characteristic that varies from one person or thing to another

Qualitative variables
A nonnumerically vallued variable

Quantitative variable
 A numerical valued variable
 Two types:
 1. Discrete
 2.Continuous

Discrete variable (prerivistiy)
a quantitative variable whose possible values can be listed (ex. # of siblings,)

Continuous variable
A quantitative variable whose possible values form some einterval of numbers. A measurement of something, height of a person, weight of a newborn, lengh of time a battery lasts.

Data
values of variable (info collected, organized, and analyzed by statisticians is data)

Observation
each individual piece of data

Data set
collection of all observations fora paritcular variable.

Classes
 categories of grouping methods.
 ex. group data by 10s, 1st class 30< 40 from 30 days, but not including 40 days.
 < simble means "up to, but not including"
 number of classes should be between 520

Frequency
The number of observations that fall in a particular class. Ex. the frequency of the class 50<60 is 8.

Frequency distribution
a listing of all classes and their frequencies.

Relative frequency
 the ratio of the frequency of a class to the total number of observations. To find the pecentage, frequency of a class devide by total number of observations and then multiply the result by 100.
 8/40=0.20 or 20% . The relative frequencies should add up to 1 (100%)

Relativefrequency distribution
a list of all classes and their relative frequencies.

Lower cutpoint
the smallest value that could go into a class

Upper cutpoint
the smallest value that could go in the next higher class(or the lower cutpoint of the next higher class)

Midpoint
the middle of a class, found by averaging its cutpoints ex. 50<60 (50+60)/2=55

Width
The difference between the cutpoints of a class. 6050=10

Groupeddata table
A table that provides the classes, frequencies, relative frequencies and midpoints of a data set.

How to make curtain width to make particular amount of classes
 Make 8 classes
 1. Find max value (45)
 2. Min value ( 155)
 3. Range between Max and min =MaxMin (15545) =110
 4. Width  110/8=13.75~14 is the width between lower and upper cutpoints 45<59

Frequency Histogram
A graph that displays the classes on the horizonal axis and the frequencies of the classes on the vertical axis. The frequency of each class is represented by a vertical bar whose height is qual to the frequency of the class.

Relativefrequency histogram
A graph that displays the classes on the horizontal axis and the relativefrequencies of the classes on the vertical axis. The relative frequency of each class is represented by a vertical bar whose height is equal to the relative frequency of the class.