STATISTICS

Statistics

Final Project

Introduction

The author of the report is currently a manager at a delivery service. The major tasks of the company are dependent upon the fuel prices. They decide on how to plan their deliveries and manage cost effectiveness as the prices of the fuel fluctuates. Being the manager, the author has to make a report analyzing the past trends and predict the gas prices for the coming ten years. Using the statistical expertise the manager needs to reflect on the tests carried out and explain them to the higher levels of hierarchy in a more understandable manner.

Significance of the project

The project holds a very critical nature for the company. The delivery business has a lot to do with the gasoline price fluctuations. The company needs to enter the next ten years with a preparation for the increases in the fuel prices. The past trends are often a very good indicator of the future of the price fluctuations. If the company can obtain a prediction of the future prices, they can better arrange their finances for future business. The overall future activities of the business are dependent on these predictions and their accuracy.

Details of the Analysis

The data for project discussed in the paper is about the gas cost every month during the years 1982 to 2009. The values were averaged out to have a mean value of gas cost every year. Expert Statistical Analysis was applied to find out the gas prices for the upcoming years.

Year

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean Values

1982

1.3580

1.3340

1.2840

1.2250

1.2370

1.3090

1.3310

1.3230

1.3070

1.2950

1.2830

1.2600

1.2955

1983

1.2300

1.1870

1.1520

1.2150

1.2590

1.2770

1.2880

1.2850

1.2740

1.2550

1.2410

1.2310

1.2412

1984

1.2160

1.2090

1.2100

1.2270

1.2360

1.2290

1.2120

1.1960

1.2030

1.2090

1.2070

1.2080

1.2135

1985

1.1480

1.1310

1.1590

1.2050

1.2310

1.2410

1.2420

1.2290

1.2160

1.2040

1.2070

1.2080

1.2017

1986

1.1940

1.1200

0.9810

0.8880

0.9230

0.9550

0.8900

0.8430

0.8600

0.8310

0.8210

0.8230

0.9274

1987

0.8620

0.9050

0.9120

0.9340

0.9410

0.9580

0.9710

0.9550

0.9900

0.9760

0.9760

0.9610

0.9451

1988

0.9330

0.9130

0.9040

0.9300

0.9550

0.9550

0.9670

0.9870

0.9740

0.9570

0.9490

0.9300

0.9462

1989

0.9180

0.9260

0.9400

1.0650

1.1190

1.1140

1.0920

1.0570

1.0290

1.0270

0.9990

0.9800

1.0222

1990

1.0420

1.0370

1.0230

1.0440

1.0610

1.0880

1.0840

1.1900

1.2940

1.3780

1.3770

1.3540

1.1643

1991

1.2470

1.1430

1.0820

1.1040

1.1560

1.1600

1.1270

1.1400

1.1430

1.1220

1.1340

1.1230

1.1401

1992

1.0730

1.0540

1.0580

1.0790

1.1360

1.1790

1.1740

1.1580

1.1580

1.1540

1.1590

1.1360

1.1265

1993

1.1170

1.1080

1.0980

1.1120

1.1290

1.1300

1.1090

1.0970

1.0850

1.1270

1.1130

1.0700

1.1079

1994

1.0430

1.0510

1.0450

1.0640

1.0800

1.1060

1.1360

1.1820

1.1770

1.1520

1.1630

1.1430

1.1118

1995

1.1290

1.1200

1.1150

1.1400

1.2000

1.2260

1.1950

1.1640

1.1480

1.1270

1.1010

1.1010

1.1472

1996

1.1290

1.1240

1.1620

1.2510

1.3230

1.2990

1.2720

1.2400

1.2340

1.2270

1.2500

1.2600

1.2309

1997

1.2610

1.2550

1.2350

1.2310

1.2260

1.2290

1.2050

1.2530

1.2770

1.2420

1.2130

1.1770

1.2337

1998

1.1310

1.0820

1.0410

1.0520

1.0920

1.0940

1.0790

1.0520

1.0330

1.0420

1.0280

0.9860

1.0593

1999

0.9720

0.9550

0.9910

1.1770

1.1780

1.1480

1.1890

1.2550

1.2800

1.2740

1.2640

1.2980

1.1651

2000

1.3010

1.3690

1.5410

1.5060

1.4980

1.6170

1.5930

1.5100

1.5820

1.5590

1.5550

1.4890

1.5100

2001

1.4720

1.4840

1.4470

1.5640

1.7290

1.6400

1.4820

1.4270

1.5310

1.3620

1.2630

1.1310

1.4610

2002

1.1390

1.1300

1.2410

1.4070

1.4210

1.4040

1.4120

1.4230

1.4220

1.4490

1.4480

1.3940

1.3575

2003

1.4730

1.6410

1.7480

1.6590

1.5420

1.5140

1.5240

1.6280

1.7280

1.6030

1.5350

1.4940

1.5907

2004

1.5920

1.6720

1.7660

1.8330

2.0090

2.0410

1.9390

1.8980

1.8910

2.0290

2.0100

1.8820

1.8802

2005

1.8230

1.9180

2.0650

2.2830

2.2160

2.1760

2.3160

2.5060

2.9270

2.7850

2.3430

2.1860

2.2953

2006

2.3150

2.3100

2.4010

2.7570

2.9470

2.9170

2.9990

2.9850

2.5890

2.2720

2.2410

2.3340

2.5889

2007

2.2740

2.2850

2.5920

2.8600

3.1300

3.0520

2.9610

2.7820

2.7890

2.7930

3.0690

3.0200

2.8006

2008

3.0470

3.0330

3.2580

3.4410

3.7640

4.0650

4.0900

3.7860

3.6980

3.1730

2.1510

1.6890

3.2663

2009

1.7870

1.9280

1.9280

2.0560

2.2650

2.6310

2.5430

2.6270

2.5740

2.5610

2.6600

2.6210

2.3484

A scatter plot was placed taking the years on X axis and the mean values on Y axis, and a regression line was obtained.

Results

The Y intercept was found to be 0.500. The slope of the regression line was found to be 0.577.

The predicted price in the year 2020 was up to 3.50.

The past trends are often a very good indicator of the future of the price fluctuations. If the company can obtain a prediction of the future prices, they can better arrange their finances for future business. The results obtained through this data were a positive slope, indicating a gradual increase in the prices of gas. These prices can go up to 3.500 in 2020 as per the expert analysis.

For such an increase the company requires to be prepared for the business activities to be aligned accordingly. They have to manage funds, cut excess expenditure, diversify in terms of the fuels they use and always look for means that can help them remain competitive in the industry.

The results however represent a reflective prediction of future gas prices. It is based on past trends from the year 1982 to the year 2009. Based on these prices average, the future prices are predicted. These predictions are subject to changes in the political, economical and market conditions of the industry. The prediction of prices always play a important part in any type of businesss. The main reason behind that is the consumer response towards the high and low ...