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

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Presentation on theme: "In Stat-I, we described data by three different ways. Qualitative vs Quantitative Discrete vs Continuous Measurement Scales Describing Data Types."— Presentation transcript:

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

3 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 Qualitative vs Quantitative

4 Quantitative Data is something that can be quantified, that is to say, something that can can be counted or measured. Discrete Data represent countable items. Continuous Data usually apply to measurements. To quantify qualitative data - apply a number scale. Example #1: PoorFairExcellent 1 35 Example #2: Female = 1Male = 2 Quantitative Data

5 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 Intervals Examples: Thermometer, Meter Stick, Speedometer Ratio - Absolute Zero Examples: Celsius Scale has negative values. Yardstick and weight scales have absolute zero. Scales of Measurement

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

7 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” are treated 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

8 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

9 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

10 Response ModelsFactors Models (Y Variable) (X Variable) Continuous ResponseContinuous Factors Nominal Response Nominal Factors Ordinal ResponseOrdinal Factors Analysis Models

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

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

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

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

15 Paired t - test Matched Pairs

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

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

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

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

20 Bivariate and Multivariate Scatterplot Matrices Multivariate Outliers Principle Components Correlations

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


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