math 2

• • John Graunt- 1662, an Englishman observed the percentages of death from suicides, accidents, and various diseases.
• • Abraham Moivre- discovered the equation of the normal distribution in 1773.
• • Carl Gauss- made its derivation from study errors in repeated measurements which he called it Gaussian distribution.
• • Francis Galton and Karl Pearson- developed the theory of regression and correlation.
• • Adolf Quetelet- Belgian, referred as the Father of Modern Statistics, considered statistics as Queen of the Sciences, established a Commission of Statistics that became a model for many organizations of statisticians.
• • William S. Gosset- developed small sample theory that was further developed by Ronald Fisher, Statistician and geneticist. Fisher was the most influential statistician of the C20.
• Statistics

  • science of conducting studies that collect, organize, summarize, analyze, and draw conclusion from data.
  • same meaning as a Latin word datum which means fact or information.
  • refer to a mere tabulation of numeric information.
• Descriptive Statistics

  • tries to describe situation.
  • includes data collection, data classification, data display, and data processing.
  • First invent or create by G.T Fechner (Gustav Theodor Fechner) during the latter half of the 19th century.
• Inferential Statistics

  • generalizing from sample to populations, performing hypothesis testing, determining relationships among variables, and making prediction.
  • main concern is to analyze the organize data leading prediction or inferences.
  • before carrying out an inference, appropriate and correct descriptive measures or methods are employed to bring good results.
• Key terms

  • data- raw material which statisticians works. Found trough surveys, experiments, numerical records, and other modes of research.
  • Population- refer to the group of aggregates of people, object, materials, events, or things of any form.
  • Sample- subgroup of population to represent population characteristics or traits.
  • Parameter- measures of the population.
  • Estimates- measures of the sample.
• • Statistical Data or information- gathered trough interviewing people, observing or inspecting items using questionnaires and checklists. The characteristics being studied is called a variable.
  • Variable- characteristics that takes two or more values which varies across individuals.
• Qualitative variables

  • represent difference in quality, character, or kind but not in amount. Examples that yield non numeric variables are sex, birthplace or geographic locations, religious preference, marital status, and eye color.
  • Purpose- answer "why" question.
  • Data type- observation, symbol, words.
  • Approach- observe and interpret.
  • Analysis- grouping of common data/non-statistical analysis.
• Quantitative variables

  • numerical in nature and can be ordered or ranked. Examples that yield numeric variables are weight, height, age, test scores, speed, and body temperature.
  • Purpose- answer "how many/much" question.
  • Data type- number/statistical result.
  • Approach- measure and test.
  • Analysis- statistical analysis.
• Classification of Quantitative Variables

  • Discrete variables- values can be counted using integral values such as the number of enrollees, drop-outs, deaths, employees.
  • Continuous variables- values can assume any numerical value over an interval/s such as height, weight, temperature, time.
• Nominal data

  • uses number for the purpose of identifying name or membership in a group or category.
  • All qualitative variables are measured on a nominal scale.
  • Observation can be classified and counted without particular order or ranking imposed on the data.
  • Examples: Gender, Hair color, Ethnicity, Marital status.
• Ordinal data

  • connote ranking or inequalities.
  • One category is higher than the other one.
  • numbers represents "greater than" or "less than" measurements such as preferences or rankings.
• Interval data

  • indicate an actual amount and there is equal unit of measurement separating each score, specifically equal intervals.
  • do not include greater than or less than relationships, but also has a limit of measurement that permits us to describe how much more or less one object possesses than another.
• Ratio data

  • similar to interval data, but absolute zero and multiples are meaningful.
  • includes all usual measurements of length, height, weight, area, volume, etc.