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JMP IN v 5.1

JMP IN v 5.1. Describing Data Types. In Stat-I, we described data by three different ways. Qualitative vs Quantitative Discrete vs Continuous Measurement Scales. Qualitative vs Quantitative. Qualitative Data - Sometimes referred to as Attribute or Categorical Data.

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JMP IN v 5.1

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  1. JMP IN v 5.1

  2. Describing Data Types • In Stat-I, we described data by three different ways. • Qualitative vs Quantitative • Discrete vs Continuous • Measurement Scales

  3. Qualitative vs Quantitative • Qualitative Data - Sometimes referred to as • Attribute or Categorical Data. • Describes a non-numeric characteristic. • Examples - • Poor, Fair, Excellent • Red, Blue, Green • Short, Medium, Tall • Male, Female • Group One, Group Two, Group Three, etc

  4. Quantitative Data • 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 quantifyqualitative data - apply a number scale. • Example #1: Poor Fair Excellent • 1 3 5 • Example #2: Female = 1 Male = 2

  5. Scales of Measurement • 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.

  6. JMP Data and Modeling Types • JMP uses two somewhat differing categories. • Data TypesModeling Types • Numeric Continuous • Character Ordinal • Row Nominal • Note the possible confusion with our previous definitions.

  7. JMP Data 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.

  8. JMP Modeling 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.

  9. JMP Modeling (Analysis) Platforms • 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.

  10. Analysis Models • Response Models Factors Models (Y Variable) (X Variable) • Continuous Response Continuous Factors • Nominal Response Nominal Factors • Ordinal Response Ordinal Factors

  11. Analysis Platforms • Distribution of Y (Univariate) • Fit Y by X • Matched Pairs • Fit Model • Non-Linear Fit • Neural Nets • Time Series • Correlation (Bivariate & Multivariate) • Survival & Reliability

  12. Distribution of Y • Univariate (One Variable) • Distributions • Histograms • Scatterplots • Normality Testing • One Sample Hypothesis Testing

  13. Fit Y by X • Bivariate (Two Variables) • Scatterplot with Regression Curve • One Way ANOVA • Contingency Table Analysis • Logistic Regression

  14. For Fit Y by X The roles of X and Y (nominal & continuous) determine the type of analysis.

  15. Matched Pairs • Paired t-test

  16. Fit Model • General Linear Models • Multiple Regression • Two and Three Way ANOVA’s • Analysis of Covariance • Fixed and Random Effects • Nested and Repeated Measures

  17. Non-Linear Fit • Requires user generated predictor equation, using iterative procedures.

  18. Neural Nets • Implements and analyzes standard types of neural networks.

  19. Times Series • Analyzes univariate time series taken over equally spaced time periods. • Plots autocorrelations • Fits ARIMA and Seasonal (Cyclic) ARIMA’s • Incorporates smoothing models

  20. Correlations • Bivariate and Multivariate • Scatterplot Matrices • Multivariate Outliers • Principle Components

  21. Survival & Reliability • Models time until an event. • Used in - • Reliability Engineering • Survival Analysis

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