Basic Concepts Flashcards
deals with the scientific method of collecting, organizing, summarizing, presenting, and analyzing data, as well as drawing valid conclusions and making reasonable decision on the bases of this analysis
Statistics
What is the basic concern in the study of statistics
presentation and interpretation of chance outcomes occurred in a planned or scientific investigation
values that the variables can assume
Data
Values whose values are determined by chance
Random variables
Two types of variables
- Qualitative variables
- Quantitative variables
words or codes that represent a class or category
Qualitative variables
numbers that represent an amount or a count
Quantitative variables
Classification of Quantitative variables
- Discrete variables
- Continuous variables
- can be assigned values as 0, 1, 2, 3, …
- said to be countable
Discrete variables
can assume all values between any two specific values like 0.5, 1.2, etc.
Continuous variables
Ex. of continuous variable
length of a wire
Ex. of discrete variable
number of persons in a room
procedures used in the collection, presentation, analysis, and interpretation of data
Statistical Methods
Two (2) major areas where statistical methods may belong to
- Descriptive statistics
- Statistical inference
comprises those methods concerned with COLLECTING and DESCRIBING a set of data so as to yield MEANINGFUL information
Descriptive Statistics
Descriptive statistics provides information only about the __ __ and in no way draws inferences or conclusions concerning a larger set of data.
collected data
What are constructed under Descriptive Statistics
- tables
- graphs
- charts
- other relevant computations
comprises those methods concerned with the ANALYSIS of a subset of data leading to PREDICTIONS or INFERENCES about the entire set of data
Statistical Inference
consists of methods that are used to INFER CHARACTERICSTICS of a population from observations on sample or formulate general laws on the basis of repeated observations
Inferential Statistics
Two (2) types of problems statistical inference is concerned with
- Estimation of population parameters
- Tests of hypotheses
consists of the totality of the observations with which we are concerned
Populations
subset of a population
Sample
any numerical value describing a characteristic of a population
Parameter
any numerical value describing a characteristic of a sample
Statistic
Level of Measurement
- Nominal level
- Ordinal level
- Interval level
- Ratio level
- characterized by data that consists names, labels, or categories only
- data cannot be arranged in an ordering scheme
- no criterion as to which values can be identified as greater than or less than other values
Nominal level
- involves data that may be arranged in some order
- differences between data values either cannot be determined or meaningless
- means in order
Ordinal level
- can determine meaningful amounts of differences between data
- data may lack an inherent zero starting point
- has values of equal intervals that mean something
Interval level
- modified to include inherent zero starting point
- differences and ratios of data are meaningful
- highest level of measurement
- zero on the scale means it does not exist
Ratio level
Example of Nominal level
- gender
- hair color
Example of Ordinal level
- high school class ranking
- social economic class
- Likert scale
Example of Interval level
- Celsius temperature
- IQ
- Time on a clock with hands
Example of Ratio level
- weight
- height
- ruler measurements
Classifications of Statistical Techniques
- univariate
- bivariate
- multivariate
technique applies to a single variable
univariate
technique applies to two variables
bivariate
technique applies to more than two variables
multivariate
technique involves estimation of population parameters and test hypothesis
inferential
Two (2) basic types of sampling procedures
- Nonprobability sampling
- Probability sampling
there is no way of estimating the probability that each individual or element will be included in the sample
Nonprobability sampling
each individual has an equal chance of becoming a part of the sample
Probability sampling
Examples of nonprobability sampling
- accidental or incidental samples
- quota sampling
- purposive sampling
may constitute the entire sample
accidental or incidental samples
proportions of various subgroups in the population are determined and the sample is drawn to have the same percentages in it
quota sampling
researchers rely on their own judgment when choosing members of the population to participate in their surveys
purposive sampling
Examples of probability sampling
- simple random sampling
- systematic sampling
- cluster sampling
- stratified random sampling
each individual in the population has an equal chance of being drawn into the sample
simple random sampling
selects every kth element in the population for the sample, with the starting point to be determined at random from the first k elements
systemic sampling
selects a sample containing either all, or a random selection, of the elements from clusters that have themselves been selected randomly from the population
cluster sampling
selects simple random samples from mutually exclusive subpopulations, or strata, of the population
stratified random sampling
characteristic or entity that can assume different values
variable
What is the primary concern of statistical description
variation in values for a given characteristic
total set of values for a particular characteristic
distribution of the variable
variable that can theoretically assume any value between two given value
continuous
