7.2.2 - Upon Which Variable do the Swiss Bank Notes Differ? - Two Sample Mean Problem.
7.2.1 - Profile Analysis for One Sample Hotelling's T-Square.
7.1.15 - The Two-Sample Hotelling's T-Square Test Statistic.
7.1.12 - Two-Sample Hotelling's T-Square.
7.1.11 - Question 2: Matching Perceptions.
7.1.8 - Multivariate Paired Hotelling's T-Square.
7.1.7 - Question 1: The Univariate Case.
7.1.4 - Example: Women’s Survey Data and Associated Confidence Intervals.
7.1.1 - An Application of One-Sample Hotelling’s T-Square.
Lesson 7: Inferences Regarding Multivariate Population Mean.
6.2 - Example: Wechsler Adult Intelligence Scale.
Lesson 6: Multivariate Conditional Distribution and Partial Correlation.
5.2 - Interval Estimate of Population Mean.
5.1 - Distribution of Sample Mean Vector.
Lesson 5: Sample Mean Vector and Sample Correlation and Related Inference Problems.
4.7 - Example: Wechsler Adult Intelligence Scale.
4.6 - Geometry of the Multivariate Normal Distribution.
4.4 - Multivariate Normality and Outliers.
4.3 - Exponent of Multivariate Normal Distribution.
Lesson 4: Multivariate Normal Distribution.
Lesson 3: Graphical Display of Multivariate Data.
Lesson 2: Linear Combinations of Random Variables.
1.5 - Additional Measures of Dispersion.
Lesson 1: Measures of Central Tendency, Dispersion and Association.
Next 7.1.16 - Summary of Basic Material ».
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The counterfeit notes can be distinguished from the genuine notes on at least one of the measurements.Īfter concluding that the counterfeit notes can be distinguished from the genuine notes the next step in our analysis is to determine upon which variables they are different. The sample variance-covariance matrix for the real or genuine notes appears below: Leeper for permission to adapt and distribute this page from our site.The sample mean vectors are copied into the table below: This page was adapted from Choosing the Correct Statistic developed by James D. Additionally, many of these models produce estimates that are robust to violation of the assumption of normality, particularly in large samples. These errors are unobservable, since we usually do not know the true values, but we can estimate them with residuals, the deviation of the observed values from the model-predicted values. Statistical errors are the deviations of the observed values of the dependent variable from their true or expected values. *Technically, assumptions of normality concern the errors rather than the dependent variable itself. Number of Dependent Variablesġ IV with 2 or more levels (independent groups)ġ IV with 2 levels (dependent/matched groups)ġ IV with 2 or more levels (dependent/matched groups)ġ or more interval IVs and/or 1 or more categorical IVs Necessarily the only type of test that could be used) and links showing how toĭo such tests using SAS, Stata and SPSS. Statistical tests commonly used given these types of variables (but not Variable, and whether it is normally distributed (see What is the difference between categorical, ordinal and interval variables?įor more information on this). Variable, namely whether it is an interval variable, ordinal or categorical You also want to consider the nature of your dependent Nature of your independent variables (sometimes referred to as Number of dependent variables (sometimes referred to as outcome variables), the The table belowĬovers a number of common analyses and helps you choose among them based on the Multiple ways, each of which could yield legitimate answers. We emphasize that these are general guidelines and should not beĬonstrued as hard and fast rules. The following table shows general guidelines for choosing a statisticalĪnalysis.