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UFCFF6-30-1 Programming In C

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UFCFF6-30-1 Programming In C

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Course Code: UFCFF6301
University: University Of West London

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Country: United Kingdom

Question:

There are 8 tasks to complete for this assignment worth a total of 100 marks. Each task needs to be completed before the next one can be attempted (except the first task which can be completed at any time). You have been provided with a sign-off sheet to be completed during tutorial sessions by your tutor. Your tutor may give you partial marks for any incomplete tasks and so please try to complete as many as possible. It is therefore essential that you attend the remaining tutorial sessions. After you have completed a task you must ask a tutor to sign it off on your sheet.
NOTE: You should take extra care to keep your sign-off sheet safe because you will need to provide a scan or photograph of it when you submit your assignment to Blackboard.
Task 1
Huffman Coding is one example of a lossless compression algorithm. Write a short 500-750 word academic report describing the similarities and differences between lossless and lossy compression algorithms and under what circumstances you might favour one kind of compression algorithm over the other. Your report should include brief descriptions of one example algorithm that is used for each kind of compression (excluding Huffman Coding). You should try to use diagrams and illustrations where applicable to help your explanations of your chosen compression algorithms. Marks will be given for good UWE Harvard referencing, good grammar and spelling, and a good report structure.
Task 2
Write a function that loads the contents of a given text file into memory. Once loaded into memory you should output the text to the Console Window. You are expected to use the ‘ToCompress.txt’ file for this part of the assignment (which is provided for you on Blackboard).
Task 3
The following code represents a letter-frequency pair structure:
Using this structure and the lecture slides to help you, develop a Linked List data structure that can be used to store a list of letters-frequency pairs. You should use the following test data to demonstrate to your tutor that you can store letter-frequency pairs in your Linked List:
For this task, you need to write a function to find the frequency of each letter in the text supplied in the ‘ToCompress.txt’ file. Remember this is a lossless algorithm and so your implementation must be casesensitive and include all punctuation. You will need to make use of the code you produced for Task 2 and
Task 3 to complete this task, i.e. a Linked List of letter-frequency pairs. Finally, you should print the letterfrequency pairs you have discovered to the Console Window.
The following code represents a binary tree node that can be used to build a Binary Tree: struct BinaryTreeNode
Write a function that takes two BinaryTreeNode parameters, merges them according to the Huffman Coding Algorithm (i.e. it creates a new node, points the left and right child to the supplied BinaryTreeNode parameters, sets the frequency of the new node to be the sum of the parameter’s frequencies and returns the new node). You should demonstrate to your tutor that this function performs correctly by using the following test data:
Write a function that takes a Linked List of BinaryTreeNode objects as a parameter and returns the BinaryTreeNode with the lowest frequency. This BinaryTreeNode should also be removed from the Linked List. You should demonstrate to your tutor that this function performs correctly by using the following test data:
Write a function that builds a Huffman Tree making full use of the code produced in the previous . You should make use of the algorithm given in the explanation above when building the Huffman Tree for the supplied ‘ToCompress.txt’ file. You should demonstrate to your tutor that yourprogram has correctly built a Huffman Tree for this file.
The final task is to create a recursive function that prints out the Huffman Codes using the Huffman Tree created in Task 7. Your function needs to recursively traverse the Huffman Tree whilst building the Huffman Codes as it goes. All Huffman Codes should be output to the Console Window together with the letter they represent, e.g. A: 001

Answer:

To assess if social media marketing is beneficial for increasing the profitability of the Australian automobile industry
Quantitative analysis
Reliability Analysis
Reliability analysis of the data shows that the internal consistency of the data set increases with addition of one item and the values are still significant. It also shows that value of alpha decreases if one of the items are deleted.  

Reliability Statistics

Cronbach’s Alpha

N of Items

.988

12

 

Item Statistics

 

Mean

Std. Deviation

N

quality advice

2.38

1.413

50

bulk information

2.44

1.232

50

price comparison

2.40

1.355

50

reliable information

2.42

1.247

50

interactive platform

2.46

1.358

50

variety of information

2.34

1.394

50

user reviews

2.38

1.369

50

easy decisions

2.40

1.309

50

waiting time

2.34

1.303

50

quick decision making

2.50

1.329

50

review and compare

2.36

1.336

50

revenue and profit margin

2.28

1.341

50

 

Item-Total Statistics

 

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach’s Alpha if Item Deleted

quality advice

26.32

186.793

.938

.986

bulk information

26.26

191.992

.922

.986

price comparison

26.30

188.663

.926

.986

reliable information

26.28

191.512

.925

.986

interactive platform

26.24

188.594

.926

.986

variety of information

26.36

187.051

.944

.986

user reviews

26.32

188.222

.929

.986

easy decisions

26.30

189.969

.923

.986

waiting time

26.36

189.868

.931

.986

quick decision making

26.20

189.020

.936

.986

review and compare

26.34

189.168

.926

.986

revenue and profit margin

26.42

190.371

.887

.987

Frequency table

revenue and profit margin

 

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Strongly agree     

18

36.0

36.0

36.0

Agree     

16

32.0

32.0

68.0

Indifferent

5

10.0

10.0

78.0

Disagree

6

12.0

12.0

90.0

Strongly disagree

5

10.0

10.0

100.0

Total

50

100.0

100.0

 

Table 40
The question scrutinises whether social media marketing enhances the profit margin and revenue of the organisations in the automobile industry o not. .As per the result, 36% of the respondent have strongly agreed, 32% have agreed to the question and 10% are indifferent. On the contrary, 12% have disagreed and 10% have strongly disagreed to the question. This shows that majority of the respondents have agreed to the fact social media marketing enhances the profitability of organisations. Social media marketing reduces the overall cost of marketing which results in increase in profit margin and effective use of resources.
Correlation table
The correlation is used to check the relationship between the different elements of social media marketing and profitability of the organisation. The correlation among revenue and profit margin and quality advice, bulk information and price comparison shows strong positive relationship where the values ranges within .814 to .837 which is significant at two tailed and 99% confidence interval. This shows that this relationship is highly significant.
Similarly, while evaluating the relationship among revenue and profit margin and reliable information and interactive platform, it shows strong positive relationship. This clearly shows that there is high positive correlation between the different elements of social media marketing and organisational profitability in automobile industry.

Correlations

 

quality advice

bulk information

price comparison

quality advice

Pearson Correlation

1

.876**

.889**

Sig. (2-tailed)

 

.000

.000

N

50

50

50

bulk information

Pearson Correlation

.876**

1

.822**

Sig. (2-tailed)

.000

 

.000

N

50

50

50

price comparison

Pearson Correlation

.889**

.822**

1

Sig. (2-tailed)

.000

.000

 

N

50

50

50

reliable information

Pearson Correlation

.869**

.914**

.841**

Sig. (2-tailed)

.000

.000

.000

N

50

50

50

interactive platform

Pearson Correlation

.885**

.840**

.918**

Sig. (2-tailed)

.000

.000

.000

N

50

50

50

revenue and profit margin

Pearson Correlation

.837**

.814**

.836**

Sig. (2-tailed)

.000

.000

.000

N

50

50

50

Table 41

Correlations

 

reliable information

interactive platform

revenue and profit margin

quality advice

Pearson Correlation

.869**

.885**

.837**

Sig. (2-tailed)

.000

.000

.000

N

50

50

50

bulk information

Pearson Correlation

.914**

.840**

.814**

Sig. (2-tailed)

.000

.000

.000

N

50

50

50

price comparison

Pearson Correlation

.841**

.918**

.836**

Sig. (2-tailed)

.000

.000

.000

N

50

50

50

reliable information

Pearson Correlation

1

.835**

.819**

Sig. (2-tailed)

 

.000

.000

N

50

50

50

interactive platform

Pearson Correlation

.835**

1

.858**

Sig. (2-tailed)

.000

 

.000

N

50

50

50

revenue and profit margin

Pearson Correlation

.819**

.858**

1

Sig. (2-tailed)

.000

.000

 

N

50

50

50

Table 42

Correlations

 

revenue and profit margin

variety of information

user reviews

revenue and profit margin

Pearson Correlation

1

.876**

.842**

Sig. (2-tailed)

 

.000

.000

N

50

50

50

variety of information

Pearson Correlation

.876**

1

.851**

Sig. (2-tailed)

.000

 

.000

N

50

50

50

user reviews

Pearson Correlation

.842**

.851**

1

Sig. (2-tailed)

.000

.000

 

N

50

50

50

easy decisions

Pearson Correlation

.830**

.919**

.859**

Sig. (2-tailed)

.000

.000

.000

N

50

50

50

waiting time

Pearson Correlation

.809**

.890**

.899**

Sig. (2-tailed)

.000

.000

.000

N

50

50

50

quick decision making

Pearson Correlation

.859**

.887**

.870**

Sig. (2-tailed)

.000

.000

.000

N

50

50

50

review and compare

Pearson Correlation

.808**

.886**

.883**

Sig. (2-tailed)

.000

.000

.000

N

50

50

50

Table 43

Correlations

 

easy decisions

waiting time

quick decision making

revenue and profit margin

Pearson Correlation

.830**

.809**

.859**

Sig. (2-tailed)

.000

.000

.000

N

50

50

50

variety of information

Pearson Correlation

.919**

.890**

.887**

Sig. (2-tailed)

.000

.000

.000

N

50

50

50

user reviews

Pearson Correlation

.859**

.899**

.870**

Sig. (2-tailed)

.000

.000

.000

N

50

50

50

easy decisions

Pearson Correlation

1

.852**

.892**

Sig. (2-tailed)

 

.000

.000

N

50

50

50

waiting time

Pearson Correlation

.852**

1

.878**

Sig. (2-tailed)

.000

 

.000

N

50

50

50

quick decision making

Pearson Correlation

.892**

.878**

1

Sig. (2-tailed)

.000

.000

 

N

50

50

50

review and compare

Pearson Correlation

.861**

.866**

.873**

Sig. (2-tailed)

.000

.000

.000

N

50

50

50

Table 44

Correlations

 

review and compare

revenue and profit margin

Pearson Correlation

.808**

Sig. (2-tailed)

.000

N

50

variety of information

Pearson Correlation

.886**

Sig. (2-tailed)

.000

N

50

user reviews

Pearson Correlation

.883**

Sig. (2-tailed)

.000

N

50

easy decisions

Pearson Correlation

.861**

Sig. (2-tailed)

.000

N

50

waiting time

Pearson Correlation

.866**

Sig. (2-tailed)

.000

N

50

quick decision making

Pearson Correlation

.873**

Sig. (2-tailed)

.000

N

50

review and compare

Pearson Correlation

1

Sig. (2-tailed)

 

N

50

**. Correlation is significant at the 0.01 level (2-tailed).

Table 45
Regression analysis
Regression analysis is the method of developing a linear relationship between two variables to identify the trend in relationship so that further predictions can be made about the relationship between the two. It determines the nature, direction and type of relationship between both the variables. In this current study, a multiple linear regression has been used to measure the linear relationship between the elements of social media marketing and organisational profitability. In order to do so, the different elements are named as predictors and the organisational profitability has been represented as revenue and profit margin. This research has developed a predictive model to evaluate the relationship between the two variables and predict future trend lines based on it.
Multiple R defines the correlation between the predictors and the dependent variable. In this research, the value of multiple R is .915 which means that the correlation between the predictors and the dependent variable is high. This also signifies that predictive capability of the model is high.  R square is the coefficient of determination which examines the explaining capability of the independent variables and goodness of fit of the model. The value is acceptable at 0.7 but values less than that have also been accepted in practical studies where small margin in increase results in high profitability. The value of R square in the research is .836 which is greater than 0.7 and this implies that goodness of fit of the model is high. Moreover, it also defines that the explaining capability of the independent variables is 83.6%. The Durbin Watson test has been performed to check the first order autocorrelation among the elements, the value of Durbin Watson is 2.283 which lies within 1.7 to 2.5 and this means that there is no first order autocorrelation among the elements.
The analysis of the Anova table shows that F value in the research is .000. This value is less than the p value which is 0.05. This implies that the null hypothesis can be rejected snd the alternative hypothesis can be accepted. This means that there is significant positive relationship between social media marketing and organisational profitability.

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

1

.915a

.836

.789

.616

2.283

 

a. Predictors: (Constant), review and compare, bulk information, price comparison, easy decisions , waiting time, interactive platform , quality advice , reliable information, user reviews , quick decision making, variety of information

b. Dependent Variable: revenue and profit margin

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

73.671

11

6.697

17.663

.000b

Residual

14.409

38

.379

 

 

Total

88.080

49

 

 

 

a. Dependent Variable: revenue and profit margin

b. Predictors: (Constant), review and compare, bulk information, price comparison, easy decisions , waiting time, interactive platform , quality advice , reliable information, user reviews , quick decision making, variety of information

 

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

.104

.207

 

.505

.617

quality advice

.032

.184

.034

.174

.863

bulk information

-.119

.230

-.110

-.518

.608

price comparison

.015

.201

.015

.073

.942

reliable information

.053

.222

.049

.238

.813

interactive platform

.283

.195

.287

1.451

.155

variety of information

.502

.215

.522

2.333

.025

user reviews

.349

.194

.356

1.794

.081

easy decisions

-.082

.204

-.080

-.400

.692

waiting time

-.271

.200

-.264

-1.354

.184

quick decision making

.368

.207

.364

1.772

.084

review and compare

-.238

.187

-.237

-1.273

.211

 

a. Dependent Variable: revenue and profit margin

Residuals Statisticsa

 

Minimum

Maximum

Mean

Std. Deviation

N

Predicted Value

.69

4.77

2.28

1.226

50

Residual

-1.776

.707

.000

.542

50

Std. Predicted Value

-1.297

2.030

.000

1.000

50

Std. Residual

-2.885

1.149

.000

.881

50

a. Dependent Variable: revenue and profit margin

Qualitative Analysis

Question

Statements

Codes

1. How does social impact the profitability of the organisations in the automobile industry?

“Social definitely positively impacts the profit margin and revenue.”
“Social media marketing reduces the overall cost of marketing which increases the overall profit margin of organisations in the automobile industry.”
“Social media marketing is one of the significant tool for increasing market share and growth by reducing the overall cost of operation. The use of innovation and technology effective can be used to improve the organisational marketing processes and policies.”
“Social media marketing facilitates in using different techniques seamlessly without much increase in the budget. Social media marketing facilitate in effective management of capital and it is essential for organisational profitability.”
“Well, social media marketing provides better quality services to the consumers and helps in retaining existing consumers.  It also facilitates in acquiring new consumers. This has strong positive impact on organisational profitability.

Positive impact, enhances consumer service quality, reduces cost of marketing

Methods
The research has used mixed method analysis to collect data from surveys and interviews where 50 customers and 5 managers working in the automobile industry in Australia were surveyed and interviewed respectively near the Brisbane area. The quantitative data has been analysed by using SPSS software (statistical tool for social sciences) and qualitative data has been analysed using coding. The survey data has been represented in tables and charts along with descriptive statistics. This data has been analysed based on the response frequency and the pattern in the data. On the other hand qualitative data has been analysed using open coding and axial coding. Open coding has been used to analyse the content and identify the patterns or key words that are common themes to the research. These common themes have been considered and compared with the quantitative analysis. The qualitative analysis aimed to highlight dimensions not highlighted using quantitative analysis.
4.0 Results
The results from the research shows that there is positive relationship between social media marketing and organisational profitability.  The quantitative analysis of the data clearly showed that the relationship is strong and positive and is significant two tailed. This means that there is bidirectional relationship between the variables. On the contrary, one of the managers stated, “Social media marketing facilitates in using different techniques seamlessly without much increase in the budget. Social media marketing facilitate in effective management of capital and it is essential for organisational profitability” which has also identified the factors essential for the improvement of the organisations profit margin and revenue.  This means that managers also consider social media marketing as a method of enhancing the profitability of the organisations.
Conclusion
The results showed that social media marketing has more advantages than disadvantages. As per the findings, social media marketing is cheap, caters to a larger audience, and facilitates word of mouth and two communication. Moreover, it assists in higher market penetration and analysis of the implemented strategies. On the contrary, it can be said that negative feedback, criticism and scrutiny from the customers cause major adverse impact on the brand image and the campaign. The companies have changed their benchmark practices and engaged in digital marketing activities which has resulted in change in focus. The use of digital makes is easier and convenient for the companies to convey their message to different consumer segments at the same time. Social media marketing has changed the industry as majority of the users search for information on online platform and compare car feature, prices and other aspects. The quantitative analysis of the data clearly showed that the relationship is strong and positive and is significant two tailed. This means that there is bidirectional relationship between the variables

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