Statistics

Data Analysis With Graph

Raw Data - The crude informations collected
from a qualitative analysis

Variable - The quota being calculated
range

Continuous variable - Any value within a given

Discrete variable - Is restricted to having certain
separate number, usually integers

Histogram - Bar graph at which bars are
proportional to the frequencies of the values
of the variable

Frequency polygon - Plot frequency vs. variable
and join the two lines. It shows the same information
as a histogram

Intervals - When the given values are large numbers,
they are broken in to classes or intervals

Relative-frequency - Shows the frequency data groups
as a fraction or precent of the whole data set

Indices

Index - Relates the value of variables to a base level,
which is often the value of on a particular date

Time-series Graph - They show changes over time

Consumer Price Index (CPI) - Measures changes in the price level of a weighted average market basket of consumer goods. It is an important measure of inflations

Inflations - An overall increase in pice, which
corresponds to a decrease in value of money

Subtopic

Sampling Techniques

Simple Random Sample - A subset of individuals chosen from a larger set.

Systematic Sample - Selection of elements from an ordered sampling frame.

Cluster Sample- The researcher divides the population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population. The researcher conducts his analysis on data from the sampled clusters

Multi-stage Sample - Use several levels of random
sampling

Voluntary-response Sampling - A sample made up of volunteers. Compared to a random sample, these types of samples are always biased

Convenience Sample - a type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand

Bias in Survey

Non-Response Bias - When a particular group is
under-represented due to choice

Response Bias - When the participants provide
false or misleading answers

Measure of Central Tendancy

Mean - Sum of the values of a variable divided by
the number of values

Median - The middle value of the data when they are ranked
from highest to lowest

Mode - Is the value that occurs most often in a distribution

Outliers - Values that are distant from the majority of the data

Measures of Spread

Dispersion - Set quantities that show how closely a set
data clusters around the center

Population Standard Deviation -
σ = sqrt[ Σ ( Xi – μ )2 / N ]

Population Variance -
σ2 = Σ ( Xi – μ )2 / N

Quantities And Interquartile Ranges

Interquartile Ranges - Q3 : Q1

Semi-interquartile Range - Is one half of the
Interquartile ranges

Scatter Plots and Linear Correlation

Linear Correlation - Changes in one variable tend to
be proportional to changes in the other

Perfect Positive- If Y increases at a constant rate
as X increases

Perfect Negative - If Y decreases at a constant rate
as X increases

Scatter Plot - Shows the relations mentioned above
graphically

Line of Best Fit - A straight line that passes as close
as possible to all points on a scatter plot

Linear Regression

Regression- Analytic technique for determining
the relationship between independent and dependent
variables

Least-Square Fit - statistical procedure to find the best fit for a set of data points by minimizing the sum of the offsets or residuals of points from the plotted curve.

Non-linear Regression

Non-linear Regression - Analytic technique for finding
a curve of best fit for data from relationships

Coefficient of Determination -
R2 = { ( 1 / N ) * Σ [ (xi - x) * (yi - y) ] / (σx * σy ) }2

Exponential Regression - the process of finding the equation of the exponential function that fits best for a set of data. As a result, we get an equation of the form y=abx
y=ab^x where
a≠0

Cause and Effect

Cause-and-Effect Relationship - A change in X produces
a change in Y

Reverse Cause-and-Effect Relationship - The dependent
and independent variables are reversed in the process
of establishing causality

Common-Cause Factor - An external variable causes
two variables to change in the same way

Accidental Relationship - A correction exists without
any causal relationship between variables

Presumed Relationship - A correction does not seem
to be accidental even though no cause-and-effect relationship or common-cause factor is apparent