Understanding Statistics: Techniques, Methods, and Analysis


S: refers to the body of techniques used for collecting, organizing & interpreting data. Data may be quantitative, with values expressed numerically or qualitative with characteristics being tabulated. S is used in bs to help make better decisions by understanding variation & relationships in data.

Descriptive Statistics

Techniques that are used to summarize & describe numerical data for the purpose of easier interpretation (can be graphical or involve computational analysis).

Inferential Statistics

Include techniques by which decisions about a statistical pop or process are made based only on a sample having been observed (use of probability is required).

Sampling Methods

  • Random Sampling: Every item in a target pop has a known, & usually equal, chance of being chosen for inclusion in the sample.
  • Non-Random Sampling: Where an individual selects the items to be included in the sample based on judgment.

Quantitative Research

Quantifying the collection & analysis of data, the objective is to develop & employ mathematical models, theories & hypotheses pertaining to phenomena.

Levels of Measurement

  • Nominal
  • Ordinal
  • Interval
  • Continuous/Discrete

Internal Validity

Relationship between the selected variables & cause-effect association.

External Validity

How well does the conducted study or used sample relate to the general pop?

Frequency Distribution

Is a table in which possible values for a variable are grouped into classes & the number of observed values which fall into each class is recorded.

Measures of Location

  • Arithmetic
  • Weighted
  • Median
  • Mode

Measures of Dispersion

  • Deviation
  • Variance
  • Standard Deviation

Regression Analysis

Objective is to estimate the value of a random variable (dependent v) given that the value of an associated v (independent v) is known.

Regression Statistics

Multiple R: Coefficient of correlation (0.0995) 95% of variability in Y is connected with 9.95% of the variability in age.

R Square: Coefficient of determination (0.00990) 0.99% of variance in Y is explained by our regression model.

Standard Error: The prediction of the Y made using our model will differ from reality by around (number of S.error).

Observations: In our model, there are () units.

Intercept (B0)

Age (B1)

Regression Equation: 29.0653 + 0.039190x = RE: 29.0653 + 0.039190x