g. (2pt) Interpret the intercept for this model

h. (2pt) Provide an interpretation for the slope coefficients of the model.

Coefficients

a

Unstandardized Coefficients

Standardized

Coefficients

B Std. Error Beta t Sig.

(Constant) .015

Repairperson -.860 .642 -.426 - .222

monthslastservice .191 .130 .467 .18

STEP 3: Use MONTHS SINCE LAST SERVICE to capture the curvature explaining REPAIRTIME

1) (2pt) From all models bellow, which you think is best?

Model Summary and Parameter Estimates

Dependent Variable:Repairtime

Equation

Model Summary Parameter Estimates

R Square F df1 df2 Sig. Constant b1 b2 b3

Linear .629 1 8 .006 .325

Quadratic .709 2 7 .013 .213 -.072

Cubic .765 3 6 .026 - .405 -.029

The independent variable is monthslastservice.

The cubic model has a good fit as % so it represents a

better fit for the model

2) (10pt) Given the following estimated regression equation and SPSS output from regression, fill in the

missing values. Show your calculations.

Statistical inference for Pearson's correlation coefficient is sensitive to the data distribution. Exact tests, and asymptotic tests based on the Fisher transformation can be applied if the data are approximately normally distributed, but may be misleading otherwise. In some situations, the bootstrap can be applied to construct confidence intervals, and permutation tests can be applied to carry out hypothesis tests. These non-parametric approaches may give more meaningful results in some situations where bivariate normality does not hold. However the standard versions of these approaches rely on exchangeability of the data, meaning that there is no ordering or grouping of the data pairs being analyzed that might affect the behavior of the correlation estimate.