# Stats 8

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1. What is strict replicability?
The same conditions will yield exactly the same measurement value
2. What is the formula for strict replicability?
• Y(cap)=constant
• The cap indicates 'the predicted value of'
• Conventionally, the constant is denoted by the symbol µ (mu)
3. How might one describe the signal component of data?
• Y(cap)=µ
• U is a parameter of the model
• This does not account for variability in the data, however
4. What is a parameter?
A numerical characteristic of a population that is only constant for specified conditions
5. What does e denote?
The residual data
6. How might one find the signal in a noisy data set?
• (observation= model prediction+residuals)
7. What is the double challenge facing statistical data analysis?
• Each measurement yields a different value so even if we know the value of µ, predictions will always be imperfect
• The value of µ is not known
8. What are the four steps to fitting a model to data?
• 1: That data are used to estimate any unknown parameters
• 2: The estimates are used to obtain a fit between model and data
• 3: Using this fitted model, the residuals are calculated
• 4: The residuals are used to calculate the 'goodness of fit' of the model
9. What is the best way to estimate the parameter (µ)?
Use the mean of observations (Y-bar)
10. How might one go about finding a fit for the model?
• Substitute the estimated value of the unknown parameter into the model
• Y=Y(bar)+e
• (observation=model fit+residuals)
11. How might one find the residuals for a data set?
Find the difference between each result and the mean
12. How do we test goodness of fit?
• Use the sum of squares (SS)
• SS are the same for observations and residuals as the residuals are simply the original scores minus a constraint (the mean). This linear transformation does not affect the SS
• When referring to residuals, the SS=SSe
13. What is the least squares estimate?
• The estimate of the parameter which minimises the SS of the residuals
• The sample mean (Y-bar) is the least squares estimate of the population mean (µ)
14. What is the population?
The set of all possible observations that might be taken under a specified set of conditions
15. What is the equation for the null hypothesis?
µ1=µ2=µ
16. What is the symbol that denotes the null hypothesis?
• HO
17. What is the symbol that denotes the experimental hypothesis?
HA
18. What is the symbol equation for the experimental hypothesis?
HA: µ1≠µ2
19. How might one find an estimate of µ
By finding the mean of observed data from all conditions
20. What is the total sum of squares?
• The sum of squares for all residuals of all conditions
• Referred to as the SStotal
21. How might one quantify the variability accounted for by the full model?
• Find the difference between the SStotal and the SSe
• This can be written as:
• SSmodel=SStotal-SSe
22. What is the size of the SSmodel determined by?
The difference between the means of the different conditions
23. What does a large SSmodel mean?
• There is a large difference between total SS and the SS for residuals.
• There is a large difference between the means
• The null models are inadequate and the full model is required
24. How are the SS values usually expressed?
R2= SSModel/SSTotal
 Author: camturnbull ID: 287109 Card Set: Stats 8 Updated: 2014-10-26 16:52:16 Tags: Psychology Stats Folders: Psychology,Statistics Description: BSc Stats Show Answers: