in stat-i, we described data by three different ways. qualitative vs quantitative discrete vs...
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In Stat-I, we described data by three different ways.
Qualitative vs QuantitativeDiscrete vs ContinuousMeasurement Scales
Describing Data Types
Qualitative Data - Sometimes referred to as Attribute or Categorical Data.Describes a non-numeric characteristic.Examples -
Poor, Fair, ExcellentRed, Blue, GreenShort, Medium, TallMale, FemaleGroup One, Group Two, Group Three,
etc
Qualitative vs Quantitative
Quantitative Data is something that can be quantified,that is to say, something that can can becounted or measured.
Discrete Data represent countable items. Continuous Data usually apply to measurements.
To quantify qualitative data - apply a number scale.Example #1: Poor Fair Excellent
1 3 5
Example #2: Female = 1 Male = 2
Quantitative Data
Nominal - Name only (arbitrary)Examples: Area Codes, ZIP Codes, Sports Jerseys
Ordinal - Order (but no defined interval)Example: Horse race - 1st, 2nd, 3rd, etc
Interval - Equal IntervalsExamples: Thermometer, Meter Stick, Speedometer
Ratio - Absolute ZeroExamples: Celsius Scale has negative values.Yardstick and weight scales have absolute zero.
Scales of Measurement
JMP uses two somewhat differing categories.
Data Types Modeling Types Numeric Continuous Character Ordinal Row Nominal
Note the possible confusion with our previous definitions.
JMP Data and Modeling Types
Numeric Data refers to quantitative data (numbers),may be discrete or continuous values.JMP treats all numeric data as continuous.
Character Data applies to alphanumeric text.If classified as character data, then “numbers” aretreated as text characters.
Row Data applies to row characteristics.Affects appearance of graphical displays.We will not be concerned with row data.
JMP Data Types
Continuous refers to data measurements.Must be numeric data type.Used in arithmetic calculations.
Ordinal refers to discrete categorical data.May be either numeric or character data type.If numeric, the order is the numeric magnitude.If character, the order is the sorting sequence.
Nominal refers to discrete categorical data.May be either numeric or character data type.Treated as discrete values without implicit order.
JMP Modeling Types
As if the foregoing was not confusing enough,we also have to deal with Modeling Platforms.
The Modeling Platforms are used for statistical analyses.
Depending on the platform model, JMP uses different algorithms and sets of assumptions to arrive at the final calculated results.
JMP Modeling (Analysis) Platforms
Response Models Factors Models (Y Variable) (X Variable)Continuous Response Continuous FactorsNominal Response Nominal FactorsOrdinal Response Ordinal Factors
Analysis Models
Distribution of Y (Univariate)Fit Y by XMatched PairsFit ModelNon-Linear FitNeural NetsTime SeriesCorrelation (Bivariate & Multivariate)Survival & Reliability
Analysis Platforms
Univariate (One Variable)DistributionsHistogramsScatterplotsNormality TestingOne Sample Hypothesis Testing
Distribution of Y
Bivariate (Two Variables)Scatterplot with Regression CurveOne Way ANOVAContingency Table AnalysisLogistic Regression
Fit Y by X
For Fit Y by X
XContinuous Nominal
Continuous B i v a r i a t e t - T e s t s
S c a t t e r P l o t M e a n s
R e g r e s s i o n L i n e O n e - W a y A N O V A
Y L i n e F i t t i n g C o m p a r i s o n T e s t s
N o n - P a r a m e t r i c T e s t s
P o w e r s T e s t i n g
L S N & L S V
Nominal L o g i s t i c R e g r e s s i o n C o n t i n g e n c y T a b l e
C r o s s T a b s
The roles of X and Y (nominal & continuous)
determine the type of analysis.
Paired t - test
Matched Pairs
General Linear Models Multiple Regression Two and Three Way ANOVA’s Analysis of Covariance Fixed and Random Effects Nested and Repeated Measures
Fit Model
Requires user generated predictor equation, using iterative procedures.
Non-Linear Fit
Implements and analyzes standard types of neural networks.
Neural Nets
Analyzes univariate time series taken over equally spaced time periods.
Plots autocorrelations
Fits ARIMA and Seasonal (Cyclic) ARIMA’s
Incorporates smoothing models
Times Series
Bivariate and MultivariateScatterplot MatricesMultivariate OutliersPrinciple Components
Correlations
Models time until an event.Used in - Reliability Engineering Survival Analysis
Survival & Reliability