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Directions for future work more exotic transformations? additional variables to throw into the model?

INSTRUCTIONS TO CANDIDATES
ANSWER ALL QUESTIONS

1 INTRODUCTION/MOTIVATION

Elaborate on:

the relevance of the topic

cite previous work

the necessity of these analyses.

a road-map (i.e. section 2 does this, section 3 does that, etc.)

Avoid formulas and graphs here.

 

2 THE DATA SET

Touch upon:

The source of the data set (be sure to cite!)

The method employed (cross-sectional, time series, etc.)

Any peculiarities (missing observations, outliers, small/large size of the set, the costly nature of it (sen- sitive information, large sets necessitate more deaths, financial crashes, or company failures), and possible ways to circumnavigate the cost (bootstrapping?))

Simple graphs (like histograms, boxplots, bars, pies) are okay here. Save your serious graphs (scatter- clouds, model fits, network/connection diagrams) for a later section.

 

3 THEORY  AND  METHODS

Recap:

The premise and the promise of regression.

The general model (also, what each piece does):

y = β0 + β1x1 + β2x2 + ... + βkxk + ϵ (1)

The fitted model (also, what each piece does):

yˆi = βˆ0  + βˆ1x1i + βˆ2x2i + ... + βˆkxki (2)

Assumptions.

Estimation and hypothesis testing principles tailored to your needs.

 

 

Author 1 is a ... (mention major), Bentley University. Email:

Similarly, for the other authors. E-mail:

 

4 DATA ANALYSES

Describe:

Causal relationship(s) between the effect and the cause(s) and also, possibly, among the causes (think mediation or interaction-type models). Lurking vari- ables, if any.

Scatter-cloud matrices, check the form. If needed, apply transformations (think “Liquor Sales”) or do piecewise linear regression (think “Charity Contri- butions”, the broken-stick model).

Simple pairwise correlation coefficients. Do they make sense? If not (think about the psychotherapy- suicidal tendencies example), find a way to explain the fallacy (think “Heartbreak of L.O.V.E”).

Check the assumptions (normality, X Y both quan-

titative, etc.). If you want to proceed despite some of these not being satisfied, please justify why.

The model fitting exercise (i.e. get parameter esti- mates from SPSS/R other softwares) and its implica- tions. Interpretation(s) of the slope(s) (careful under interaction models!), do they make sense? Related tasks: variable selection (if any), multicollinearity (if any).

Fitted line/curve through the cloud (if under 3-D).

The quality of the fit: R2, adj R2 values, tests on β parameters, confidence intervals, PRESS statistic, etc. Do they make sense? Their implications?

 

5 CONCLUSIONS AND FUTURE WORK

Summarize (think of this as an elaborate Abstract):

The importance of the problem.

The methods used, troubles encountered.

Main achievements.

How do they relate to the original story (i.e. avoid technical  terms  like  p-vales,  R2, βˆ1,  etc.  in  this  sec-

tion. Try instead: “We found that one unit increase in the size of the ring is expected to hike its price up by such-and-such amount”).

Directions for future work (more exotic transformations? additional variables to throw into the model? questioning the form?)

 

 

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