Sales of Mid-Size Sport Utility Term Paper

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With.573 correlation of Unibody directly influencing Body-on-Frame sales in the years sampled. Table 3 provides the results of the query made in SPSS Version 13.

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.

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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 of new unibody (crossover) SUVs and the rapid growth of import car manufacturers proves the hypothesis of the second phase of the research, which is the test of whether unibody (crossover) SUVs are the next generation of mid-sized SUVs. The results of this paper support that finding.....

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