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### 7 Cards in this Set

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 WHat are the sources of variance that go into the numerator and the denominator of the f-stat? F = treatment effects + individual differences + experimental error------ individual differences + experimental error the only difference between the numerator and the denominator is the presence of treatment effects in the numerator F stat equation • So, the only difference between the numerator and the denominator is the presence of treatment effects in the numerator • If the treatment effect is large, F will be large. • If there is no treatment effect (treatment effect = 0) the F-ratio will be close to 1.0 • The denominator of the F-ratio only has unsystematic sources of variation, and is therefore often referred to as the error term • If the null hypothesis is true, then both the numerator and denominator measure the same thing – unsystematic error due to sampling error What does the f-distribution look like? What should the value of F be if the null hypothesis is true? • Because the F-ratio is the ratio of two variance (and variance is always positive), the F-ratio will always be positive. • Under the null hypothesis, the F-ratio should be close to 1. • The F-distribution is always greater than 0, and has a mode at 1. • The exact shape of F depends on the df.  df for the numerator of the F-ratio = dfB  dffor the denominator of the F-ratio = dfW • The critical value for F can be found in the F table (to be discussed in class) Regression Equation and what it tells you: • Linear Relationships • Can be expressed by Y = bX +a • “a” is the intercept – the point at which the lines hits the Y-axis on a graph. o When X=0, Y=a or when x=0, y = the intercept. • “b” is the slope – the steepness of the line. Tells you how much Y will change when X increases by 1 unit • The equation for a line, however, does not address the data scattered around the line. Regression, allows us to draw the best fitting line through the data keeping that “error” in mind. •Four types of scales •Nominal -- includes labels or categories; no size or magnitude information •Ordinal -- includes ranking information; ordered by magnitude •Interval -- equal intervals between numbers reflect equal differences in magnitude; no absolute zero •Ratio -- like interval scales, but there IS an absolute zero. •Discrete vs. Continuous Variables •Discrete -- implies that the numbers are separate, individual, indivisible categories. •Continuous -- continuous scales are not limited to a fixed set of categories. **Real Limits for continuous variables: for a continuous variable, each score really corresponds to an interval on a scale. The boundaries that separate the intervals are called the real limits. The real limits are located exactly half-way between the scores. The upper real limit is half way above the score, and the lower real limit is half way below the score. oSkewness oKurtosis oSkewness – the degree to which the distribution departs from being symmetrical Positively skewed – tail extends in the positive direction Negatively skewed—tail extends in the negative direction Kurtosis – the degree to which a distribution is pointy or flat and spread out. Leptokurtic-- pointy Platykurtic – flat and spread out