# 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**