STAT271 Statistical Analysis

Introduction

In current Years, analytical treatment has evolved into one of the world’s mainly important business intelligence works, compelling companies to get used to their strategies based on controlling data-driven insights. Data collecting, transforming, fresh up and modeling data with the aim of discovering the basic information are the necessary steps to analysis a dataset. In this study researcher analyzed the data set to find the demand of Renault car. It critically evaluate the difference application between descriptive, confirmatory and explanatory analysis methods. Data visualization is used to show the data for the easiness of discovering the useful patterns in the dataset. Here Excel tool helps to this analysis. This study is about business and economic data evaluation where we should cortically evaluate the differences in function among descriptive, confirmatory and exploratory examination methods.

Discussion

Part 1

The frequency table for all the attributes of the questionnaires dataset has been shown in the table below. Price compared to other brands, Price compare to the brand quality, neighborhood, Education qualification attributes will help to analysis the demand of Renault. Most of the Renault cars are at cheap price so it is affordable for normal middle-class people.98% of cars are in cheap price and 1% of cars are in very cheap price.50%

peoples vote for low price quality compared with the other brand quality. people how are from Faith neighborhood they like to porches Renault car. Most of the people how are from high school education qualification they are interested in Renault car(Lind, Marchal and Wathen 2019).

 

This table shows descriptive statistics income of individual persons(Winston 2016). The average income is 3008.637 Turkish Lira. Maximum number of people who earn 3000 Turkish Lira they interested to porches Renault car. The peoples who are in [1800TL, 6000TL] income interval they want to buy a Renault car.

 

This regression analysis help to find which variables are significant for demand of Renault car. The above table represents the relation between demand of annual car sales compared to Current Annual GDP per capita, Annual population growth rate. However, it find that Current Annual GDP per capita do not have significant impact on demand of annual sale of cars . R-square equal 0.769, which is a very good fit . 76% of the variation in Annual car sales, is explained by independent variables (Ott and Longnecker 2015).

Figure 1

 

Figure 2

The Figure 1 is a line diagram on number of cars sales Vs Current Annual GDP per capita and Figure 2 is a line diagram on number of cars sales Vs Annual population growth rate. This two diagrams are similar.

Part 2 a)

Descriptive data analysis (Gupta and Kapoor 2020) for car sales data set. Number of car sales is divided into two city Mersin and Bursa.

Average sale in Mersin is greater than average sale in Bursa, Total sale in Mersin > Total sale in Bursa, Maximum number of sale in Mersin > Maximum number of sale in Bursa, and many other comparison between this two city shows that demand of Renault cars in Mersin is higher than the demand of Renault cars in Bursa. Moreover, none of these two data set are normally distributed.    

Part 2 b)

 

This table represents the central tendency and measure of variability on the car sales in Bursa and Mersin data set(Winston 2016).

Most of the variables in questionnaire data contain categorical data set. We cannot measure central tendency and measure of variability for those categorical variables. We can only measure the frequency table for each variable.

 

This tables contain frequency of difference variables compare with various car brands (Winston 2016).

 

This diagram shows that maximum number of peoples prefer to buy a Renault car than the other brands. It show that there is high demand of Renault car .This Renault car gives you in a affordable price.

 

Above table represents the central tendency and measure of variability for individual disposable income data set.

Part 3 a)

Confidence level for 5% level of significance on customer purchases sample is 13.690. Confidence interval of customer purchases sample is [6549.210, 6521.829]. Here 100 customer samples have been taken. (Ott and Longnecker 2015).

Part 3 b)

 

This regression analysis help to find which variables are significant for number of Renault car sale in Mersin city. The above table represents the relation between number of car sales compared to Monthly Training Expenditure for Sales  (Turkish Lira) , Monthly Advertising Expense (Turkish Lira) .However, it find that Monthly Advertising Expense (Turkish Lira) do not have significant impact on number of monthly sale of cars . R-square equal 0.926, which is a very good fit . 92% of the variation in number of monthly car sales in Mersin, is explained by independent variables (Winston 2016).

Conclusion

This report described all the requirements given. All the outputs are briefly described in this report. Various statically techniques applied in this report to analyze the data set. Statistical idea involves the careful plan of a research to gather significant data to answer a focused investigating question, complete analysis of patterns in the data and drawing conclusions that go further than the observed data. This report contains a hypothesis testing (t test), several tables have construed to analyze the dataset, descriptive statistics, and Regression analysis. This analysis concludes that Renault car sales has an increasing trend ,this trend will remain to continue as long as Renault come at an affordable price.  There is also some test and methodology, which will help us to make a better analysis

References

Gupta, S.C. and Kapoor, V.K., 2020. Fundamentals of mathematical statistics.

Lind, D.A., Marchal, W.G. and Wathen, S.A., 2019. Basic statistics for business and economics.

Ott, R.L. and Longnecker, M.T., 2015. An introduction to statistical methods and data analysis.

Winston, W., 2016. Microsoft Excel data analysis and business modeling

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