Understanding Statistics: Techniques, Methods, and Analysis
Statistics
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