Unit Name - Statistics for Management

Unit Number - Unit 31

Assignment Title - The Role of Statistics in Evaluating Business and Economic Data

Learning Outcome 1: Evaluate business and economic data/information obtained from published sources

Learning Outcome 2: Analyse and evaluate raw business data using a number of statistical methods

Assignment Brief: In order to open a new plant to expand foreign direct investment, the board of directors in Renault Corporation require a feasibility report to justify the decision. The senior managers need to predict the demand level on Renault cars in the next few years so they can determine the optimal capacity of the new plant. They asked their colleagues in Evaluating and Monitoring department to collect relevant business and economic data.

Employees in Evaluating and Monitoring department collected the following data about National Economy in Turkey : GDP Per Capita Current \$ (annual), Population Growth Rate % (annual), Cars' Sales (annual) (data are available in National Economy Data Excel sheet).

Introduction

The objective of this study is to predict the Renault cars demand over the next few years which would certainly help them to decide on the optimal capacity of a new plant. Renault, established in 1989, a multinational car company manufactures its cars on its own with its headquarters in France. The main advantage of customer preference to Renault cars is that their repair costs are low and thus it makes the customer highly satisfied with the service after sales.

In this study, the management wants to collect relevant business and economic data to decide on opening a new facility in Turkey

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1- What is you preferred car?

Car Selection

The table given below shows the information about the sample of 500 customer's preferred car Brand

 Preferred Car's Brand Frequency Percentage Fiat 90 18.0% Ford 56 11.2% Land Rover 12 2.4% Mercedes- Benz 70 14.0% Peugeot 22 4.4% Renault 157 31.4% Toyota 34 6.8% Volkswagen 59 11.8% Total 500

About 31.4% of the customers prefer Renault Brand Cars, 18% of the customers prefer Fiat Brand Cars and 14.4% of the customers prefer Mercedes Benz Brand cars. Thus, about one third of the customers prefer Renault Brand Cars

2- Your preferred car's price compared to other brands is: very expensive, expensive, neither expensive nor cheap, cheap, or very cheap?

Price Comparison

 Price Compared to Other Brands Frequency Percentage cheap 163 32.6% expensive 152 30.4% neither cheap nor expensive 69 13.8% very cheap 81 16.2% very expensive 35 7.0% Total 500

About 32.6% of the customers feel that Renault cars are cheaper when compared with other brand cars and 16.2% of the customers feel that Renault cars are very cheaper when compared with other brand cars. Thus, about 50% of the customers feel that Renault cars are cheaper when compared with other brand cars

3- Your preferred car's price compared to the brands quality is very high, high, reasonable, low, or very low

Price Compared with Brand Quality

 Price Compared to the Brand Quality Frequency Percentage High Price 109 21.8% Low Price 130 26.0% Reasonable Price 194 38.8% Very High Price 26 5.2% Very Low Price 41 8.2% Total 500

About 39% of the customers feel that the price of the car is reasonable when compared to its brand quality and 26% of the customers feel that the price of the car is low when compared to its brand quality

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4- What is your current disposable income?

Individual Disposable Income

 Sample Size Average Standard Deviation Minimum Maximum Individual Disposable Income (Turkish Lira) 500 5360.378 3490.709 1800 30000

On average, the individual disposable income is 5360.38 (Turkish Lira) with a standard deviation of 3490.71 (Turkish Lira). The recorded minimum and maximum disposable income is 1800 (Turkish Lira) and 30000 (Turkish Lira) respectively

From the above histogram, it is clearly seen that the distribution of individual disposable income is skewed right. This is, almost 59% of the Individual Disposable Income falls below 4620 (Turkish Lira) and 95% of the Individual Disposable Income falls below 10260 (Turkish Lira)

Neighbourhood

 Neighborhood Frequency Percentage Fatih 262 52.4% Nisantasi 73 14.6% Uskudar 165 33.0% Grand Total 500

Regarding neighbourhood, about 52% of them were fatih, 33% of them were Uskudar and 15% of them were Nisantasi

6- What is your educational qualification? (data collected are available in Questionnaire data Excel sheet)

Education Qualification

 Educational Qualification Frequency Percentage Bachelor 161 32.2% High School 191 38.2% HND 90 18.0% Master 58 11.6% Grand Total 500

About 18% of the respondents included in this study were HND, 38.2% of the respondents included in this study said that completed till high school, 32.2% of the respondents included in this study were Bachelors and 11.6% were completed masters

Car Sales between Bursa and Mersin

The mean number of Bursa cars sold is 3390.54 cars with a standard deviation of 395.96 cars with the minimum and maximum car sales fall between 2890 cars and 4200 cars respectively. The mean number of Mersin cars sold is 3825 cars with a standard deviation of 1315.73 cars with the minimum and maximum car sales fall between 2300 cars and 7100 cars respectively. The variation seems to be very high in the Mersin car sales when compared with Bursa car sales where the mean car sales do not show any high difference between the two car sales

The trend on car sales indicate that Mersin cars shows some seasonal trend in the car sales whereas, Bursa car sales are showing more stable growth

The mean values between the two car sales are compared using independent sample t test. The t test statistic was found to be - 1.549 and its p - value is 0.128 > 0.05, indicating that the mean values do not show any high difference between the two car sales

Amount spent per shopping trip
On average, the amount of money customer spent per shopping trip is 6535.52 Turkish Lira with a standard deviation of 2177.72 Turkish Lira.

The 95% confidence interval for the mean amount of money customer spent per shopping trip is (6457.12, 6613.92). Thus, we can say that about 95% of the times, the mean amount of money customer spent per shopping tripin Renault car accessories store during the next two months will fall between 6457.12 Turkish Lira and 6613.92 Turkish Lira

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Regression Model to Predict Mersin Car Sales

In order to determine whether there exists any relationship between Mersin Car Sales with monthly training expenditure and advertising expenditures (Turkish Lira). Initially, the Pearson correlation was computed to determine the relationship among these three variables and the result is given below

 No of Car Sales (Mersin) Vs Training Expenditure Advertising Expenditure Correlation Coefficient 0.9622 0.334 P - Value 0.000 0.111

From the above table, we have
There exists very strong positive linear relationship between Mersin car sales and training expenditure. This indicates that when the monthly training expenditure increases, then the number of Mersin car sales also increases and statistically significant (r = 0.96, p - value = 0.000 < 0.05)
There exists weak positive linear relationship between Mersin car sales and advertising expenditure. This indicates that when the monthly advertising expenditure increases, then the number of Mersin car sales increases to some extent and not statistically significant (r = 0.334, p - value = 0.111> 0.05)

Thus, we can predict the number of car sales using training expenditure as independent variable. The regression output is given below

From the above output, we have
Car Sales = 281.048 + 0.108 * Training Expenditure

The value of F test statistic is 274.45 and its corresponding p - value falls well below 0.05, indicating that the model is good fit in predicting the number of Mersin car sales

The coefficient of determination is 0.9258, indicating that 92.585 of the overall Mersin car sales variation is explained by the regression model while the remaining 7.415% left unexplained
The coefficient of Training expenditure is 0.108, indicating that the number of Mersin car sales increases by 0.108 units for every one Turkish Lira increase in monthly training expenses

Conclusion
The main objective of the study is to predict the Renault cars demand over the next few years which would certainly help them to decide on the optimal capacity of a new plant. About 31.4% of the customers prefer Renault Brand Cars, 18% of the customers prefer Fiat Brand Cars and 14.4% of the customers prefer Mercedes Benz Brand cars. About 32.6% of the customers feel that Renault cars are cheaper when compared with other brand cars and 16.2% of the customers feel that Renault cars are very cheaper when compared with other brand cars.

The regression model provides a clear understanding in the relationship between monthly training expenses and Mersin car sales. That is, on spending 1000 Turkish Lira as monthly training expenditure for sales, it is expected that the number of Mersin car sales will increase by 108 units, which seems to be highly profitable

Thus, we can recommend the management to build an optimal capacity of a new plant

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 Learning Outcomes and Assessment Criteria Learning Outcome Pass Merit Distinction LO1 Evaluate business and economic data/information obtained from published sources P1 Evaluate the nature and process of business and economic data/information from a range of different published sources. P2 Evaluate data from a variety of sources using different methods of analysis. M1 Critically evaluate the methods of analysis used to present business and economic data/information from a range of different published sources. LO1 & 2 D1 Critically evaluate the differences in application between methods of descriptive, exploratory and confirmatory analysis of business and economic data. LO2 Analyse and evaluate raw business data using a number of statistical methods P3 Analyse and evaluate qualitative and quantitative raw business data from a range of examples using appropriate statistical methods. M2 Evaluate the differences in application between descriptive statistics, inferential statistics and measuring association.

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