400a - Statistical Analysis Midterm

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athorne
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241818
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400a - Statistical Analysis Midterm
Updated:
2013-10-21 04:21:56
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psyc 400a statistics psychology
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theoretical introduction to the general linear model, model comparison approach, and regression analysis
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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)
    • estimate of ada squared
    • 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
    • ada squared
    • estimate of PRE
    • expected to be 0 when null hypothesis is true
  45. ἣ²
    • unbiased ada squared
    • 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)

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