# 400a - Statistical Analysis Midterm

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1. ANOVA design
• IV manipulated
• DV measured
2. Regression design
• IV selected
• DV measured
3. Correlation design
• IV measured
• DV measured
4. ANCOVA design
IV and DV are a mix of manipulated, selected, and measured
5. ANOVA analysis
• "analysis of variance"
• categorical IV
• continuous DV
6. Regression analysis
• "regression analysis"
• continuous IV
• continuous DV
7. ANCOVA analysis
• "analysis of covariance"
• combination of continuous and categorical DV and IV
8. simple model
• predicts the same value for everyone
• unconditionally
9. complex model
• includes more than the constant in the model
• predicts a different value for everyone
• conditional
10. bivariate regression analysis
• DV and IV are continuous
• only a single predictor
11. multiple regression analysis
• DV and IV are are continuous
• more than one predictor
12. factorial ANOVA analysis
• IV is categorical, DV is continuous
• a categorical variable is linked to another categorical variable
13. multivariate ANOVA analysis
• IV is categorical, DV is continuous
• there is more than one DV
14. measurement error
error associated with unreliable and invalid measures
15. design error
error associated with a poor design and therefore the data is inaccurate and non-representative
16. sampling error:
• error associated with non-representative sampling
• always expected to exist because a sample will never truly represent a population
• problematic for analysis if it results from design error
17. commission
inclusion of variables to a model that should not be there
18. omission
• exclusion of variables to a model that should be there
• usually realized post hoc
19. PRE - proportional reduction of error
• [ERROR(C) - ERROR(A)]/ERROR(C)
• effect size
20. SAE - sum of absolute errors
• ∑|Y-ˆY|
• use the median to minimize
21. SSE - sum of squared errors
• ∑(Y-ˆY)²
• use the mean to minimize
22. MAE - mean of absolute errors
mdn|y-mdn|
23. MSE - mean of squared errors
• ∑(y-ˆy)²/[N-(p+1)]
• dividing by the number of parameters that can still be reduced
• b₀ cannot be reduced more
24. standard error of estimate
square root of the MSE
25. SD - standard deviation
square root of the variance (MSE in simple models)
26. variables
get scores from a sample in a certain area (X)
27. parameters
how much of the variable predicts y
28. unbiased frequency sampling distribution
the mean value of the sampling distribution is close to β₀
29. efficient frequency sampling distribution
• the sampling distribution is skinny
• more likely to be close to β₀
30. consistent frequency sampling distribution
efficiency of sampling distribution increases as sample size increases
31. assumptions regarding error
• normal distribution
• unbiased
• independent
• homeoscedasticity
32. homeoscedasticity
error distributions of y are the same across different values of x
33. standard error of the mean
the size of the difference between the population mean and expected mean
34. central limit theorem
the distribution of errors is normal
35. sampling distribution
distribution taken from a population where the null hypothesis, MODEL©, is true
36. sample distribution
distribution based on the data
37. SSR - sum of squares reduced
SSE(C) - SSE(A)
38. SSR (remaining)
• regression analysis: SS(regression)
• ANOVA analysis: SS(between)
39. SSE(A)
• regression analysis: SS(residual)
• ANOVA analysis: SS(within)
40. SSE(C)
• regression analysis: SS(total)
• ANOVA analysis: SS(total)
41. PA
number of parameters in model A
42. PC number of parameters in model C
43. F
• transformation of PRE to standardized form
• obtained proportion of reduction divided by the amount of known parameter changes, all divided by the remaining unexplained error divided by the number of unknown parameters
• ratio of the amount of explained error to unexplained error
• typically sig if greater than 4 or 5
• equal to t²
44. η²
• estimate of PRE
• expected to be 0 when null hypothesis is true
45. ἣ²
• accounts for ada squared always being positive
• critical value which cuts off the most extreme 5%
46. Type 1 Error
• wrong conclusion that the null hypothesis is true
• equal to alpha (usually 5%)
47. Type 2 Error
• wrong conclusion the null hypothesis is correct, but is actually false in reality
• β is the probability of this type of error
48. Power
• the probability of actually detecting an effect that is there
• depends of the effect size (PRE - smaller means harder to detect), the level of alpha (but only ever made more stringent and decrease power), and sample size (N)
49. β₁
• how much changer there is on the y-axis for every one unit of change on the x-axis
• slope
50. r(xy)
relationship of x and y
51. s(y)
scaling factor
52. standard error of estimate
• square root of the MSE(A)
• also known as the standard error of prediction
53. variance of y
MSE(C)

### Card Set Information

 Author: athorne ID: 241818 Filename: 400a - Statistical Analysis Midterm Updated: 2013-10-21 08:21:56 Tags: psyc 400a statistics psychology Folders: Description: theoretical introduction to the general linear model, model comparison approach, and regression analysis Show Answers:

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