Statistical Methods MCQ'S ANCOVA, MANOVA and Regression/Correlation

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1. Which of these do NOT effect correlations?

  • Negative skew and Kurtosis
  • Outliers and influential points
  • Homo/Hetero Scedasticity
  • Singularity and Multi - Collinearity
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2. In order to work out the f-ratio for a regression, we need MSm and MSr. How do we work out MSm?

  • MSm = dfm/MSm (Degree's of Freedom Model/ Model Mean Squares) Remember, model mean squares is the amount of variables in the model.
  • MSm = dfm/SSr (Degree's of Freedom Model/ Residual Sum of Squares) Remember, the degree's of freedom for the model are the amount of variables in the model.
  • MSm = SSm/dfm (Model Sum of Squares / Degree's of Freedom Model) Remember, dam is simply the number of variables in the model.
  • MSm = SSr/dfr (Residual Sum of Squares / Residual Degree's of Freedom) Remember, residual degrees of freedom is the amount of variables in the model.

3. The general equation for multiple regression is as follows...

  • y ̂ = b0+b1x1+b2x2+......bpxp
  • y ̂ = b1+b1+b2+b2+....bpxp
  • y ̂ =b1xb2xb3xb6
  • y ̂ = b1x2+b0x2+b2x3

4. 'Anscombe's Quartet' reminds us to check what in terms of the distribution of variables before analysing?

  • Mean, Variance, Correlation, Regression line
  • Mean, F-score, Variance and Correlation scores
  • Variance, Mean, Regression Line and MSe
  • Mean, Variance, Homogeneity of scores, and spread of data

5. What does a good regression do?

  • Leads to easily interpretable graphs, and creates a small difference between estimated type I error rate and actual observed Type I error rate
  • Leads to a large improvement in prediction due to the mode, and there is a small difference between the model and the data
  • Leads to a large improvement of the detection of error, and improves the ability to recognise strong correlations
  • Leads to a small improvement in prediction due to error, and there is a small difference between the regression line and the error

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