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26 Cards in this Set
- Front
- Back
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False Negative Rate |
# of real effects (1-power) |
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False Positives |
#of fake effects (alpha) |
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Given Signif Chance its True |
True positives- false negatives/ ((True-false neg)+False) |
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Confidence Interval |
Mean +- Margin of error Margin of error: zcrit(sd/sqrt(n)) |
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Cohens D |
Measure of effect size X1+X2/SDpooled X1+X2/sqrt((v1+v2/2)) low, med, high -> 0.2,0.5,0.8 |
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Latin Square |
Each treatment occurs once in each column and once in each row |
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Williams Square |
Latin square balanced for first order carry over effects |
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What alpha for grubbs test means |
On average 1 in 20 SAMPLES (not observations) will be identified as having an outlier |
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Displaceable Binding |
Total - excess cold condition Also called specific binding |
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Why you dont use same ligand in the excess cold condition |
because it will have the same semi-affinity for non-specific targets |
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When displaceable doesnt equal specific |
When there are two saturable receptors for one drug |
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Normal Plot |
x = ligand concentration y= Cpm bound Contains total binding and excess cold |
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Saturation Binding Isotherm |
x = concentration y = Displaceable binding Estimate kd and Bmax through non linear regresion |
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Scatchard Plot |
x = specific binding y = bound/free bmax is x intercept kd is -1/slope |
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Issues with scatchard |
Breaks all the damn assumptions y variances largest near y axis x variable is measured not manipulated x has error associated with it x and y are mathematically intertwined |
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Issues with scatchard |
Breaks all the damn assumptions y variances largest near y axis x variable is measured not manipulated x has error associated with it x and y are mathematically intertwined |
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when there is an excess cold |
Do normal non linear regression using displaceable binding get bmax and kd |
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If there is no excess cold |
Tell the program there is a linear component and it will automatically take it out |
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Two site model |
Makes two different non linear regressions for different binding sites |
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Sigmoidal Fits |
When the x axis (agonist concentration) is logged provides kd and bmax Can compare different curve prameters using t test |
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Sigmoidal Fits |
When the x axis (agonist concentration) is logged provides kd and bmax Can compare different curve prameters using t test |
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Hill number |
1 - perfect fit less than 1 - shallow, two sites with dif affinities greater than 1 - positive cooperativity |
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Schild plots |
x = log antagonist y = log(DR-1) Plots kd at different concentrations of antagonist Plit the |
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Schild equation |
Log(Dr-1) = log[B] - log kB |
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When is schild slope 1 |
When binding is competitive and reversible |
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Schild Plot |
x axis - kb - equilibrium dissociation constant pa2 = (-log(kb)) |
