Verified Document

Regression Analysis Of Auto Sales Term Paper

As one would expect, the greater the variability in a given variable the higher the elasticity, especially when the variables either measure purchasing power as pi does directly or how the variables stock, and index of consumer sentiment also are shown as a result of their large variances. Taking a step back from the statistical analysis and thinking logically about this, elasticity would be defined by the level of car stocks or inventories on hand, customer attitudes and behaviors and the amount of money they had to spend. These three variables delivered a 75% R2 correlation coefficient. Elasticity is a function of price and demand so these series of relationships make sense. Forecasting

The first step in completing a forecast is to define the confidence intervals. Both 90% and 95% confidence intervals are typically chosen, for purposes of this exercise the latter figure, 95% has been selected. A 95% confidence interval ensures statistically significant results and a higher level of reliability when applying the results. Presented below are the results of a one-tailed Z-Test and t-test for confidence intervals based on the dependent variable unitsales.

Confidence Interval for Mean using Z. from Infinite Population or with Replacement

Lower limit =

Upper Limit =

Margin for Error (Half Width) =

Sample Mean =

Standard Deviation =

Confidence Interval =

Sample Size =

Confidence Interval for Mean using t from Infinite Population or with Replacement

Lower limit =

Upper Limit =

Margin for Error (Half Width) =

Sample Mean =

Sample Standard Deviation =

Confidence Interval =

Sample Size =

The fact that both of these tests show minimal differences between Upper and Lower Limits indicates the data has slight variability, as is shown by the standard deviation being 1.1976. This shows little variability across the sample data.

Forecasting unit sales of automobiles can be accomplished using a wide variety of techniques. Using Winter's Model for exponential smoothing as interpreted by the KADD Microsoft Excel add-on statistical tool generates the following forecast for the variable unitsales.

UnitSales forecast for the next four quarters using the Winters Method of Exponent Smoothing from KADD using a smoothing constraint of.2 for alpha,.2 for beta,.2 for gamma, for four periods delivers the following forecast:

UnitSales (Projected for 2005)

Q1, 2005 11,453,000

Q2, 2005 11,290,200

Q3, 2005 11,203,200

Q4, 2005 10,772,300

Looking for those variables that define variability first is critical, hence the completion of a thorough correlation analysis. Following this, a stepwise regression was completed to show the combined effects of the most correlated variables, which illustrated the strong effects of personal income (pi), cars in stock (stock) and the implications of excess inventory driving down price, and consumer sentiment (sentiment) impacting elasticity of unitsales. Forecasting unitsales using exponential smoothing under the Winters Method proved the most reliable in defining the next four quarters of unitsales given the sample data.
Appendix a: Correlation Matrix of all variables

Unitsales Pi sentiment Unemr costcarown mpg strike cardeprate avgprice finrate Stock Pearson Correlation unitsales 1.000.624.005.176.422.361 -.304.244.512.248.541 Pi.624 1.000 -.530.592.924.578 -.257 -.262.938.808.976 sentiment.005 -.530 1.000 -.482 -.443.128.240.723 -.373 -.737 -.571 Unemr.176.592 -.482 1.000.654.285 -.201 -.618.642.614.738 Costcarown.422.924 -.443.654 1.000.744 -.216 -.275.986.793.939 Mpg.361.578.128.285.744 1.000 -.080.207.780.303.559 Strike -.304 -.257.240 -.201 -.216 -.080 1.000.045 -.227 -.122 -.266 cardeprate.244 -.262.723 -.618 -.275.207.045 1.000 -.229 -.498 -.396 avgprice.512.938 -.373.642.986.780 -.227 -.229 1.000.735.947 Finrate.248.808 -.737.614.793.303 -.122 -.498.735 1.000.820 Stock.541.976 -.571.738.939.559 -.266 -.396.947.820 1.000 Sig. (1-tailed) unitsales..000.484.089.000.002.009.030.000.028.000 Pi.000..000.000.000.000.024.022.000.000.000 sentiment.484.000..000.000.165.032.000.002.000.000 Unemr.089.000.000..000.014.062.000.000.000.000 Costcarown.000.000.000.000..000.049.017.000.000.000 Mpg.002.000.165.014.000..273.057.000.009.000 Strike.009.024.032.062.049.273..367.040.176.020 cardeprate.030.022.000.000.017.057.367..039.000.001 avgprice.000.000.002.000.000.000.040.039..000.000 Finrate.028.000.000.000.000.009.176.000.000..000 Stock.000.000.000.000.000.000.020.001.000.000.

Variable Name

Variable Description

Unitsales

Unit sales of automobiles. SAAR in millions of cars.

Pi

Personal Income less transfer payments. SAAR in billions of $

Sentiment

Index of consumer sentiment. 1980 Q1 = 100.

Unemr

Unemployment rate. Percent.

Costcarown

Index of cost of car ownership. 1987 = 1.0.

Mpg

Average miles per gallon of current model year cars.

Strike

Indicator variable for UAW strikes. 1=strike.

Cardeprate

Stock of cars depreciation rate.

Avgprice

Index of average price of a new car.

Stock

Stock of cars. Millions.

Finrate

Finance rate on automobile loans. Percent.

Cite this Document:
Copy Bibliography Citation

Related Documents

How Higher Interest Rates Limit New Car Sales
Words: 565 Length: 2 Document Type: Case Study

Car Sales and Interest Scenario Analysis A finance manager employed by an automobile dealership believes that the number of cars sold in his local market can be predicted by the interest rate charged for a loan. The finance manager performed a regression analysis of the number of cars sold and corresponding interest rates and identified a direct correlation, with fewer cars sold as the interest rates increased. This case study assesses

Sales of Mid-Size Sport Utility
Words: 2528 Length: 9 Document Type: Term Paper

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

Economic Model for Monopoly Analysis
Words: 14390 Length: 30 Document Type: Term Paper

The deal was immediately criticized as anti-competitive by William Kennard, the chairman of the Federal Communications Commission, and by the Communications Workers of America, which represents some workers at both of the merged companies. But neither government regulators nor union bureaucrats will have the slightest impact on the latest merger. They have neither the power nor the desire to oppose the plans of the giant telecommunications monopolies. More substantial opposition

Business Problem Proposal Proposed Company:
Words: 3942 Length: 14 Document Type: Business Proposal

2009 2008 ART 8.54 8.84 ACP 42.74 41.27 Iturnover 15.13 14.23 Inventory Age 24.12 25.65 Comments: Ford shows unfavorable activity ratios, which is indicative of the fact that the company is using its assets efficiently to meet financial requirements. All measures, except ART improved over time (from 2008 to 2009). 2009 2008 Debt/Equity 2.04 1.62 Debt/Assets 0.40 0.36 TIE -2.35 2.25 Comments: Ford uses debt heavily to finance the growth of the company. Overall the company is servicing the debt well and is stable over time, even though the loss in 2009 has affected the capital

Managerial Economics
Words: 4051 Length: 12 Document Type: Term Paper

Managerial Economics Get the financial data for a company or organization for five years. From the balance sheet and the income statement for the company or organization develop regression line formulae for each line item and predict those line item revenues and costs over the next five years. Don't do prediction for any item in the statement less than 10% of the total sales on the incomes statement or 10% of

Management Distributed Order Management Systems
Words: 4856 Length: 11 Document Type: Term Paper

AMR Research (2005) believes that companies must begin developing and redeploying current order management architectures with the focus on delivering more flexibility rather than a strategy that delivers far less. The move toward customer-driven fulfillment processes requires the ability to build and adapt channel-specific, product-specific, and customer-specific order flows quickly without an army of developers creating custom code. However, the days of big bang, rip-and-replace implementations are over, and any

Sign Up for Unlimited Study Help

Our semester plans gives you unlimited, unrestricted access to our entire library of resources —writing tools, guides, example essays, tutorials, class notes, and more.

Get Started Now