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Table 3: SPSS Correlation Coefficients
Kendall's tau_b
BodyOnFrame
Correlation Coefficient
Sig. (2-tailed)
UnibodyCrossover
Correlation Coefficient
Sig. (2-tailed)
Spearman's rho
BodyOnFrame
Correlation Coefficient
Sig. (2-tailed)
UnibodyCrossover
Correlation Coefficient
Sig. (2-tailed)
With the statistical analysis showing reasonably strong predictability, the next step is to evaluate the specific 14-month time series for greater insights into the variability and predictability of the data. What emerges from completing a Linear Regression along with every exponential smoothing techniques for curve fitting is further evidence of linear (.702 regression) in addition to quadratic (.737 regression) shows that variations in Body-on-Frame demand are explained through these statistical techniques.
Table 4: Model Summary and Parameter Estimates
Dependent Variable: BodyOnFrame
Equation
Model Summary
Square
Linear
Logarithmic
Inverse
Quadratic
Cubic
Compound
Power
Growth
Exponential
Logistic
The independent variable is UnibodyCrossover.
Forecasting Results by Type of Method
The most basic of forecasting techniques, the moving average, yields the following table for the unibody (crossover) and Body-on-Frame unit sales through the remainder of 2007 with the following table showing the results.
Table 5: Three-Month Moving Average Forecast of Body on Frame SUVs based on Unibody as the Independent Variable
Body on Frame SUVs
Unibody SUVs
Using Correlation for the Forecasting Technique
Taking the correlation analysis and creating a linear regression to the 14-month data, the following forecast is generated. SPSS Version 13 generates the following cross-correlation analysis shows a more significant gap emerging over time for Body-on-Frame vs. Unibody (crossover) vehicle comparisons. The following graphic generated in SPSS Version 13 shows the progression of the forecast graphically.
Table 6: Correlation Forecast of Body on Frame SUVs based on Unibody as the Independent Variable
Body on Frame SUVs
Unibody SUVs
Summary
Appendices a and B. provide the background data for this paper, and the extensive use of statistical analysis to first prove that gas prices are not as much of a predictor to the sales of mid-size Body-on-Frame SUVs as the rapid growth of unibody (crossover) SUVs are. Gas is a contributing factor in the forecasts of unibody SUVs (.32) yet with a quadratic correlation of.737 the impact of this vehicle class itself is even more of a powerful predictor of Body-on-Frame SUV demand. Using three-month moving averages, the results are smoothed through those fundamental forecasting techniques. Conversely, when more robust statistical analysis tools and techniques are used, the data shows a much more significant divergence of these two variables, when unibody (crossover) SUVs is used as part of the analysis. Using correlation analysis show this wider divergence of values in the sample set, including a wider confidence interval for the forecasts.
Moving averages and correlation analyses, cross-correlation, and regression analyses have been used in this paper to predict both the influence of gas prices on both unibody (crossover) and Body-on-frame SUVs. While the commonly held belief that gas prices directly influence the sales of these larger vehicles, they do have an influence. Yet the number...
The results of this paper support that finding and conclusion, and the forecasts quantify it.
References
Monthly Energy Review (2006) - Posted: August 28, 2006 from the Energy Information Administration at Website: http://www.eia.doe.gov/emeu/mer/petro.html. Here is the specific data table used for the analysis:
http://tonto.eia.doe.gov/merquery/mer_data.asp?table=T09.04
Appendix a: Comparison Analysis for 14-month Analysis
COMPARING BODY on FRAME SUV vs. CROSS-OVER (UNIBODY) SUV SALES
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