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INTRODUCTION TO PRINCIPLES OF EXPERIMENTAL DESIGN

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... 2 two-sided sheets of notes. There are 8 problems, with point values as shown. If you want to receive partial credit for wrong answers, show your work. Don’t spend too much time on any one problem. 1. (30 pts.) The proportion of high school seniors who are married is 0.02. Suppose we take a random s ...
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... b. Suppose I ignore the known fact that the constant term in my model equals 0, and fit the model with a constant term anyway. Does this cause my estimator of β to be biased? a. The sum of squares is Σi (yi - βxi) . The least squares estimator of β minimizes this sum of squares. The derivative is ...
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... general approach to the multiple sample analysis of group differences in that it makes few assumptions on the data. If one is willing to make more assumptions about the data, other methods exist for analyzing the data which if the assumptions are valid is more “powerful” than the Bonferroni procedur ...
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... Statistics 251 – Uebersax 07 Box Plots, Variance and Standard Deviation This formula simply means: (1) take the sum, of (2) the squared difference between each value minus the mean, and (3) divide the sum by N. There is, however, one minor complication. The formula for the variance is different dep ...
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... Why is the median a better representation than the mean? Because outliers don’t affect it as much. If we look at the same example above regarding the people who want to begin a new town, the median is a much better indicator… Step 1: List the incomes from low to high: $29,831; $34,291; $38,112; $41, ...
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... complicated formula. Remember that you can use Microsoft Excel to calculate a sample standard deviation. The sample standard deviation is the preferred way to measure spread or variability in a distribution of data. The sample standard deviation uses all the values in a distribution of data which is ...
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Data presentation and descriptive statistics

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Misuse of statistics

Statistics are supposed to make something easier to understand but when used in a misleading fashion can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator. When the statistical reason involved is false or misapplied, this constitutes a statistical fallacy.The false statistics trap can be quite damaging to the quest for knowledge. For example, in medical science, correcting a falsehood may take decades and cost lives.Misuses can be easy to fall into. Professional scientists, even mathematicians and professional statisticians, can be fooled by even some simple methods, even if they are careful to check everything. Scientists have been known to fool themselves with statistics due to lack of knowledge of probability theory and lack of standardization of their tests.
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