Terminologies - ENENDA30

Statistics

science that deals with the collection to draw judgements or conclusions

Descriptive Statistics

deals with the procedures

Inferential Statistics

deals with making a judgement

Population or Universe

refers to overall number of subjects

Sample

any subset of population

Data

information collected

Ungrouped Data

data which are not organized

Grouped Data

raw data organized into groups

Parameter

descriptive measure of a population

Statistic

measure of a sample

Constant

characteristic which is common to all

Variable

characteristic that can assume different values

Surveys

most common method in collecting data

Random Sampling

selecting by using random numbers

Systematic Sampling

selecting by numbering each subject and selecting every nth subject

Stratified Sampling

dividing the population into groups called strata

Cluster Sampling

dividing the population into groups called cluster [cluster samples]

Statistical analysis

means investigating trends, patterns and relationships using quantitative data.

Statistical Hypothesis

a formal way of writing a prediction about a population

Null hypothesis

no relationship between variables

Alternative hypothesis

has relationship between variables

Research Design

overall strategy for data collection and analysis

Experiment Design

assess a cause-and-effect relationship

Correlational Design

explore relationships between variables

Descriptive Design

study the characteristics of of a population or phenomenon

Categorical Data

represents groupings

Quantitative Data

represents amounts

Probability Sampling

every member has a chance of being selected

Non-probability Sampling

some members are more likely than others to be selected

Population

entire group that you want to draw conclusions about

Sample

specific group that will collect data from

Normal Distribution

data are symmetrically distributed around a center

Skewed Distribution

asymmetric and has more values on one end

Measures of Central Tendency

describe where most of the values in a data set lie. The center of a data set.

Mode

most popular response

Median

value in the exact middle

Mean

average of all the value

Measures of Variability

how spread out the values in a data set

Range

highest value minus lowest value

Interquartile Range

range of the middle half

Standard Deviation

average distance between each value

Variance

square of the standard deviation

Descriptive Statistics

summarize and organize characteristics of a data set

Distribution

frequency of each value

Central Tendency

averages of the values

Variability

how spread out the values are

Dataset

collection of responses to the survey

Frequency Distribution

summary of the frequency of every possible value in tables or graphs

Univariate Descriptive Statistics

focus on only one variable at a time

Bivariate Descriptive Statistics

to explore whether there are relationships between more than one variable

Multivariate Analysis

same as bivariate but with more than two variables

Contingency Table

each cell represents intersection of two variables

Scatter Plot

shows the relationship between two or three variables. Visual representation of the strength of a relationship

Estimation

calculating population parameters

Hypothesis testing

for testing research predictions

Point estimate

best guess of the exact parameter

Interval estimate

best guess of where the parameter lies

Statistical tests

determine where your sample data would lie on an expected distribution of sample data

Test statistics

how much your data differs from the null hypothesis

P value

the likelihood of obtaining your results if the null hypothesis is actually true

Comparison tests

assess group differences

Regression test

assess cause-and-effect

Correlation tests

assess relationships

Parametric tests

make powerful inferences about the population

Simple linear regression

includes one predictor

Multiple linear regression

includes two or more predictor

T test

for 1 or 2 groups if small

Z test

for 1 or 2 groups if large

ANOVA

for 3 or more groups

Pearson’s R

the only parametric correlation test

Correlation coefficient (R

tells the strength of a linear relationship

Statistical significance

main criterion for forming conclusions

Effect size

indicates the practical significance of the results

Type I error

rejecting the null hypothesis

Type II error

failing to reject the null hypothesis

Frequency Distribution

tabular summary of data showing the frequency of items – to provide insights about the data

Relative Frequency

the proportion of the total number of items

Relative Frequency Distribution

tabular summary of the relative frequency

Percent Frequency

the relative frequency multiplied by 100

Percent Frequency Distribution

tabular summary of the percent frequency

Bar graph

graphical device for qualitative data

Pie Chart

commonly used graphical device for presenting relative frequency distributions for qualitative data

Dot Plot

horizontal axis shows the range of data values

Histogram

has no natural separation between rectangles of adjacent classes

Cumulative Frequency Distributions

shows the number of items with values less than or equal to the upper limit

Cumulative Relative Frequency Distributions

shows the proportion of items

Cumulative Percentage Frequency Distributions

shows the percentage of items

Ogive

graph of cumulative distribution

Exploratory Data Analysis

consist of simple arithmetic and easy-to-draw pictures that can be used to summarize data quickly – stem-and-leaf display

Stem-and-Leaf Display

shows both the rank order and shape of the distribution of the data – like histogram

Leaf Units

single digit is used to define each leaf

Crosstabulation

tabular method for summarizing the data for two variables simultaneously

Scatter Diagram

graphical representation of the relationship between two quantitative variables

Experiment

any activity whose outcome is subject to uncertainty

Random Variable

different numerical values are possible and random – in mathematical language, it is a function whose domain is the sample space and whose range is the set of real numbers.

Discrete Random Variable

possible values either constitute a finite set or else can be listed in an infinite sequence – whole numbers

Continuous Random Variable

consists either of all numbers in a single interval (decimals) on the number line or all numbers in a disjoint union of such intervals