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T-Test for 2 Independent Samples, Correlation and Regression, Analysis of…

- - - - A research design that uses a separate group of participants for each treatment condition (or for each population)
    - - To evaluate the mean difference between two populations
    - - Interpreting the Estimated Standard Error
        
        It measures the standard distance between
        
        When the null hypothesis is true, it measures the standard, or average size of . That is, it measures how much difference is reasonable to expect between the two sample means
        
        One method for correcting the bias in the standard error is to combine the two sample variances into a single value called the pooled variance
  - - - The two sets of data are obtained from the same group of participants
- - - - The Direction of the Relationship. The sign of the correlation, positive or negative, describes the direction of the relationship.
        
        Positive correlation , the two variables tend to change in the same direction
        
        Negative correlation , the two variables tend to go in opposite directions
      - The Form of the Relationship
      - The Strength or Consistency of the Relationship
        
        A perfect correlation always is identified by a correlation of 1.00 and indicates a perfectly consistent relationship.
- - - - The value of a in the general equation is called the Y-intercept because it determines the value of Y when
- - - - Tukey’s HSD test allows you to compute a single value that determines the minimum difference between treatment means that is necessary for significance.
      - The Scheffé test uses an F-ratio to evaluate the significance of the difference between any two treatment conditions.
        
        Scheffé test uses the value of k from the original experiment to compute df between treatments. Thus, df for the numerator of the F-ratio is .
        
        The critical value for the Scheffé F-ratio is the same as was used to evaluate the F-ratio from the overall ANOVA.
  - - - The populations from which the samples are selected must be normal.
        
        The populations from which the samples are selected must have equal variances (homogeneity of variance).
- - - - The test statistic for ANOVA uses this fact to compute an F-ratio