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Probability and Statistic, Population(N), Sample(n) - Coggle Diagram
Probability and Statistic
Model
Descriptive Statistics
summarize data
change data into information
Inferential Statistics
Base for forecast, prediction and estimate
transform information into knowledge
Estimate a parameter
Test a hypothesis
Forecast
difference
statistic
Properties of data like avg, variance
defines for Samples only
statistics
Art and science of extracting information from data
Answers questions using data
Helps in decision making
Data
with data we compare
Columns are called variables
Rows are called Cases or observation
Types of data
Categorical Data
likert data
Mode
is way to represent
Median
(ordinal)
Nominal
No implied order
ex Name, Gender, car type,
represent data
bar chart
(pareto chart)
actual number
pie chart
used for fractions and proportions
Ordinal
Numerical Data
ex salary of 20 ppl,
Qualitative Data
Ordinal
order or rank
ex Percentile marks, size of cloths(S,M,L,XL),
Nominal
ex education of ppl, pin codes
Quantitative Data
critical to distinguish them
Interval
Add/subtract
ex years of experience
Ratio
Multiply/Divide/add/sub
ex-Age, absent student in a class
Represent Data
Histogram
Cumulative frequency
Stem and leaf
Scatter plot
Measure of central tendency
Mean, Median, Mode
Median
middle value
50th % of data
Second quartile of data
Percentile
%P of observation below given % value
PxN/100
Quartile
25%, 55%,75%,100%
75%-25% is inter-quartile range(
IQR
)
Five number summary of data
min value, first quart, median, third quart, max value, IQR
Variance
SD
Coeff of variance (SD/avg)
Process variation involved, we want to reduce that. Six Sigma works on it
Other measurements
skewness
-measure of asymmetry of data
Kurtosis Kurt
- measure of tailedness of data
Covariance
Correlation Coefficients
Time Series
Cross sectional data
looking at data at certain instance in time
Population(N)
Parameters-represent whole data or population
Sample(n)
