Stack #12211 Hangman

 
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Term Define
Data  Observations (such as measurements, genders, survey responses) that have been collected  
Statistics  A collection of methods for planning experiments, obtaining data and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data  
Population  The complete collection of all elements (scores, people, measurements, and so on) to be studied  
Population  it includes all subjects to be studied  
Census  The collection of data from every member of the population  
Sample  A subcollection of members selected from a population  
Sample data  must be collected in an appropriate way, such as through a process of random selection  
If sample data are not collected in a appropriate way,  the data may be so completely useless that no amount of statistical torturing can salvage them  
TYPES OF DATA???  Parameter,Statistic ,  
Parameter  A numerical measurement describing some characteristic of a population.  
Parameter  all of those votes to be the population considered, When Lincoln was first elected, he received 39.82% of the 1,865,908 votes cast which is 29.825.  
Statistic  A numerical measurement describing some characteristic of a sample  
Based on a sample  of 877 surveyed executives, it was found that 45% of them would not hire someone with a typographical error on their job application  
Quantitative data  Data consists of numbers representing counts or measurement  
Quantitative data  weights of supermodels  
Interval level of measurement  Temperature, Years  
Interval level of measurement  Data that can be arranged in order and for which differences between data values are meaningful  
Interval level  shoe sizes [US vs Europe] and temperature  
Interval data  can be either discreet or continuous  
Ratio level of measurement  Data that can be arranged in order, for which differences between data values are meaningful, and there is an inherent zero starting point.  
Ratio level of measurement  differences and ratios are meaningful  
Ratio level of measurement  prices of textbooks; $50 is half of $100  
Ratio level of measurement  Height of students  
Ratio data  is continuous.  
Ratio level of measurement  HIGHEST LEVEL OF DATA  
Ratio level of measurement  can be either discreet or continuous.  
Quantitative (interval and ratio) data  can be further distinguished between discrete and continuous.  
Discrete Data  Data that results when the number of possible values is either a finite number or a “countable” numbers.  
Discrete Data  Counting-type things  
Discrete Data  number of eggs that hens lay  
Continuous (numerical) Data  thickness of paper; measurement of weight  
Continuous (numerical) Data  Results from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps, interruptions, or jumps.  
Continuous (numerical) Data  amounts of milk from cows  
Qualitative (or categorical or attribute) data  Can be separated into different categories that are distinguished by some nonnumerical characteristics.  
Continuous (numerical) Data  The genders (male/female) of professional athletes  
Nominal Level of Measurement  Characterized by data that consists of names, labels, or categories only  
Nominal Level of Measurement  Lowest form of data. Has groups, but no ordering to the groups  
Nominal Level of Measurement  cannot be arranged in an ordering scheme (such as low to high)  
Nominal Level of Measurement  Survey responses of yes, no, and undecided  
Nominal Level of Measurement  Colors of cars driven by college students (red, black, blue, etc.  
Ordinal Level of Measurement  (Categories & groups, but with some natural order to the groups.)  
Ordinal Level of Measurement  Course grades – Grades of A, B, C, D, or F  
Ordinal Level of Measurement  Ranking cities; those ranked 1st, 2nd, 3rd, etcBut, the differences between ranks are meaningless  
Money and Counting are  Discrete  
Temperature is  Interval  
Ages are  usually Discrete  
Man on the street samples are  always convenience sampling  
Misuses of Statistics  self-selected surveyBad samplesSmall samplesMisleading graphsPictographsLoaded Questions  
Misuses of Statistics  Order of Questions  
Misuses of Statistics  Precise NumbersPartial picturesDeliberate Distortions  
Randomness  typically plays a critical role in determining which data to collect.  
Observational Study  Observing and measuring specific characteristics without attempting to modify the subjects being studied. (Control group)  
Cross Sectional Study  Data are observed, measured, and collected at one point in time.  
Retrospective (or Case Control) Study  Data are collected from the past by going back in time.  
Prospective (or Longitudinal or Cohort) Study  Data are collected in the future from groups (called cohorts) sharing common factors.  
Experimental  Apply some treatment and then observe its effects on the subjects. (Experimental group.) Doing something to affect what happens.  
Experimental Key Elements  Control, Replication, Randomization  
control  Effects of variables through: blinding, blocks, completely randomized, experimental design, rigorously controlled experimental design  
Confounding  Occurs in an experiment when the experimenter is not able to distinguish between the effects of different factors.  
Blinding  Subject doesn't know if he or she is receiving a treatment or placebo  
Double-blind  Neither the subject nor the experimenter knows whether treatment or placebo is being administered  
Blocks  Groups of subjects with similar characteristics  
Completely Randomized Experimental Design  Subjects are put into blocks through a process of random selection  
Rigorously Controlled Design  Subjects are very carefully chosen so that those in each block are similar in the ways that are important to the experiment.  
Random Sample  Selection so that each has an equal chance of being selected  
Simple Random Sample  of size n  
Systematic Sampling  Select some starting point and then select every Kth element in the population  
systematic sampling  7th person of a group of 10; i.e., 7, 17, 27, 37, etc., OR every 7th person i.e., 7, 14, 21, 28, etc  
Convenience Sampling  Use results that are easy to get, choosing the first 10 people who get off work  
Stratified Sampling  Subdivide the population into at least two different subgroups that share the same characteristics, then draw a sample from each subgroup (or stratum  
Cluster Sampling  Divide the population into sections (or clusters); randomly select some of those clusters; choose all members from selected clusters  
Sampling Error  The difference between a sample result and the true population result; such an error results from chance sample fluctuations  
Nonsampling Error  Sample data that are incorrectly collected, recorded, or analyzed (such as by selecting a biased sample, using a defective instrument, or copying the data incorrectly