REGRESSION MODEL THAT COULD BE USED TO PREDICT THE REPAIR TIME IN HOURS OF A PEUGEOT CAR

REGRESSION MODEL THAT COULD BE USED TO PREDICT THE REPAIR TIME IN HOURS OF A PEUGEOT CAR

The Complete Project Research Material is averagely 52 pages long and is in Ms Word Format, it has 1-5 Chapters. Major Attributes are Abstract, All Chapters, Figures, Appendix, References

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CHAPTER ONE

Introduction

1.0  Introduction

Time standards are realistic goals that must be accomplished by operators and technicians. They could be used as performance measures by managers and supervisors. Several studies demonstrate that time management predicts job performance. For example, car salesmen with better time management skills have higher sales (Barling et al., 1996). College students with better time management skills report higher grade point averages (Britton & Tesser, 1991; Macan et al., 1990). There is a voluminous popular literature that lauds the benefits of time management. Examples of some books and magazine articles on the subject are: "Time Is money, so use it productively" (Taylor & Mackenzie, 1986), "Put time on your side" (Emmanuel, 1982), and "How to get control of your time and your life" (Lakein, 1973).

Surprisingly little empirical research, however, has examined time management. Perhaps it is because time management is typically viewed as a fad and not held in very high esteem by researchers in the field. Nonetheless, many organizations promote efficient use of company time and spend a great deal of money on having their employees learn these time management behaviors. Moreover, if a model can be fit in to predict the time of repair of a vehicle based on the informations on other variables that can greatly affect it, would have help in many areas in can be from the owner himself or from the mechanic perspective.

This research discusses time spent on repairing a Peugeot car, Months spent since last service and the type of repair (Electrical/Mechanical). A model will be fitted that can help the owner of the car and the mechanic (repairer) on how to predict repair time of Peugeot cars based on the sample information obtain on months since last service and type of repair. Also the research will classify type of repair as either electrical or mechanical based on the samples obtained, so that if appropriately used will save lots of time from both parts. And lastly it will also test if the averages of vectors of the type of repair (Electrical/mechanical) which will be assumed to be two independent normal populations are the same or otherwise not same.

1.1.            Background of the Study

According to the recent studies, 5 percent of all motor vehicles fatalities are clearly caused by automobile maintenance neglect. And that abiding by the following conditions will reduce the length of repair time of motor vehicle the conditions are:

i.        Always consult your owner’s manual, but have the oil filter and oil changed regularly, every 3,000 to 4,000 miles.

ii.      Check tire inflation, under–inflated tires can result in a loss of fuel efficiency. Tire air pressure should be checked once a month.

iii.    Check battery cables and posts for corrosion and clean them as needed. Check the battery fluids in iron maintenance free batteries

iv.    The air filter should be checked approximately every other oil change for clogging or damage.

v.      Have all the fluids regularly checked, including brake, power steering, transmission/transaxle, windshield washer solvent and antifreeze.

vi.    Tune the engine for peak performance, a fouled spark plug or plugged/restricted fuel injector can reduce fuel efficiency as much as 30 percent.

vii.  Lubricate the chassis often to prevent wear of the moving parts.

Abiding by these conditions will drastically reduce the length of time to be spent in repairing/servicing a car and vice versa.

1.2.            Statement of the Problem

The statement of the problem of this research is to develop a regression model that could be used to predict the repair time in hours a Peugeot car will spend in a workshop before it is repaired or service given the number of months since last service call and type of repair. And test for significance of parameters in the model. To classify the type of repair as Mechanical or Electrical and obtain probability of misclassification based on repair time in hours and months since last service call, and lastly, to test if the vectors of the averages of the two type of repairs (Electrical/Mechanical) are the same.

1.3.            Objectives of the Study

i.   To develop a regression model that could be used to predict the repair time in hours given the number of months since last service call and type of repair. And test for significance of parameters in the model.

ii.  To classify the type of repair as Mechanical or Electrical and obtain probability of misclassification based on repair time in hours and months since last service call.

iii. To test if the vectors of the averages of the two groups (Electrical/Mechanical) are the same.

1.4.            Significance of the study

The principal benefits of this research are as follows:

i.     It will serve as a means of estimating repair time in hours of a Peugeot car given the number of months since last service call and type of repair (i.e. electrical = 0 and mechanical = 1).

ii.   Using this research, the workshop could be able to classify type of repair (mechanical or electrical) based on the information on repair time in hours and months since last service call.

iii. Using the research also, the management of the workshop could be able to know among the two categories of repairs which is the most occurring which help them on proper allocation of staff.

iv. Subsequent researchers might use this study as a reference particularly those that are using Regression analysis, Discriminant analysis and Hotelling T2 Distribution.

1.5.            Scope of the Study

The scope of this research work is limited to Ngulde Motors, No 2 Nagogo Road, Ungwan Sarki Kaduna, and covers only Peugeot cars with sample size of 150 observations on repair time in hours, months since last service and type of repairs in the year 2014.




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