DATA ANALYSIS

Minitab: Data Analysis



Minitab: Data Analysis

Introduction

In this paper we are going to analyze and test the data on Minitab solution. The test that will be run of Minitab is multiple regressions. The objective of the study is to find out “asking price”. In the first part of the report we will present data collection method then in the second part we will be discussing the outputs of the Minitab that was run on the particular data.

Discussion

Data Methodology

For this particular assignment the data we have chosen for analyzing is of cars. This data includes the model, edition, price, mileage and age. The data is collected with the help of the following links:

www.whatcar.co.uk

www.autotrader.co.uk

www.exchangeandmart.co.uk

www.parkers.co.uk/carsforsale

This is the secondary research assignment and the data is collected from these above websites. The tool used for the analysis in Minitab as mentioned in introduction section. The technique used for the proposed study is multiple regressions.

Multiple Regressions

The example developed from two variables allows us to understand the logic of theory of regression, but it cannot be generalized to every kind of multiple regressions. The system of two equations with two unknowns is solved easily presented as have seen. The equations are complicated by several repressors, two distinct methods solves the equations. The first is based on knowledge of linear correlation coefficients of all simple pairs of variables with each other, the arithmetic mean and standard deviations of all variables (Fox, 2000: 45). The second is based on matrix calculations. The regression coefficient can be: Positive, Negative, and Null. It is positive when the variations of the independent variable X are directly proportional to variations in the dependent variable "Y" Is negative, when variations of the independent variable "X" are inversely proportional to variations in the dependent variables "Y" Is null or zero, when between the dependent variables "and" independent "X" there is no relationship.

The numerical results provided by a data analysis are usually simple: It finds the number that describes a typical value and it finds differences among numbers. Data analysis finds averages, like the average income or the average temperature, and it finds differences like the difference in income from group to group or the differences in average temperature from year to year. Fundamentally, the numerical answers provided by data analysis are that simple.

Data Analysis

After collecting data we run test on it all together there were 44 observations which have been categorized by the edition of car launched by the company (46.2 and 16.3).

Descriptive Statistics

 

Mean

Std. Deviation

N

Price

12500

15022.7

44

Age

4

1.843

44

Mileage

24400

3241.71

44

The results in the table above show the descriptive statistics for the data set under study, it can be observed that on average the price of cars was $12500.The magnitude of mileage shows that on average the cars have travelled 24400 miles (Draper & Smith, 1998: 69).The ANOVA table below shows that the model is good fitted as the magnitude of sig. value is less than the level of significance.

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

2.940E8

3

9.800E7

24.832

.000a

Residual

1.579E8

40

3946599.727

Total

4.519E8

43

a. Predictors: (Constant), Mileage, Edition, Age

b. Dependent Variable: Asking Price

Regression coefficients (or B-coefficients) represent the independent contributions of ...