Stats

• statistic


  a number that describes a sample such as mean, median, mode, range, variance, sd
• parameter


  a number that describes a population such as age, sex
• n


  the sample size (number of individuals in the sample)
• N


  the population size (number of individuals in the population)
• x


  the count of successes (or a data value)
• p-hat


  the sample proportion
• formula of p-hat


  p-hat = x/n
• p


  the population proportion
• formula of p


  p= x/N
• variables


  characteristics of the individuals being studied (can take on various values)
• qualitative variables


  allows for classification of individuals according to their characteristics as they have no meaningful numbers
• quantitative variables


  have meaningful numbers, addition and subtraction of the data values is meaningful
• discrete variables


  variables may only be counted; there are no meaningful fractions or decimals

  ex. number of students in class, points scored in a game
• continuous variables


  variables have meaningful fractions and decimals

  ex. temperature, weight, height
• data


  the list of observation a variable assumes

  ex. gender is a variable, the observations, male or female, are data
• goal of sampling


  to obtain as much information as possible about the population at the least "cost"
• sampling without replacement


  the individual is removed from the population once selected and cannot be chosen again
• sampling with replacement


  the selected individual is placed back in the population and CAN be chosen a second time
• random sampling


  the process of using chance to select individuals from a population to be included in the sample (each individual member has an equal chance of being selected)
• simple random sampling


  a sample of size n from a population of size N is obtained where every possible sample of size n has an equally likely chance of occurring

  ex. selecting names from a hat
• stratified sampling


  separate the population into homogeneous, non-overlapping groups (strata)
• process of stratified sampling


  the population should be divided into at least 2 different non-overlapping (strata) so that subjects within thew same group share the same characteristics (such as gender, age bracket, political party) and then obtain a simple random sample from EACH group (stratum)
• In 2008, the US Senate had 47 Republicans, 51 Democrats and 2 Independents. The president wants to have a luncheon with 4 Republicans, 4 Democrats, and 1 Other. This is an example of what kind of sampling

  stratified sampling
• systematic sampling


  select every k^th individual from the population
• in systematic sampling, the first individual selected should correspond to a random number between


  1 and k
• A quality control engineer wants to obtain a sample of 25 bottles coming off a filling machine to verify the machine is working properly. He selects every 16th bottle filled by the machine. What method of sampling is being used?

  systematic sampling
• cluster sampling


  separate the population into sections (clusters), then randomly select some of those clusters, and then choose ALL of the members from the selected clusters
• A school administrator wants to obtain a sample of the sudents in order to conduct a survey. She randomly selects 10 classes and administers the survey to all students in the class. What sampling method was used?

  cluster sampling
• convenience sampling


  obtaining the individuals easily and not randomly
• voluntary response sampling


  a sample where the respondents decide whether or not to be included (self-selected; not likely to be representative of a population)
• internet surveys, mail-in voting, and telephone polls are all methods of what kind of sampling?

  voluntary response sampling
• multistage sampling


  using a combination of sampling techniques
• the two main sources of data are

  1. observational study
  2. designed experiment
• observational study


  measures specific characteristics of the individuals in the study, but does not attempt to manipulate or influence the outcome of the study
• designed experiment


  when a researcher assigns the individuals in a study to certain groups, intentionally changes the value of the variable, and records the value of the response variable in each group
• bias


  to give preference to selecting some individuals over others OR that certain responses are more likely to occur in the sample than in the population
• sampling error


  error that results from using a sample to estimate information about a population
• sampling error occurs when


  a sample gives incomplete information about the population
• non-sampling error


  error that results from sampling bias, nonresponse bias, response bias, or data-entry error