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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