Research Techniques & Statistics for Business Decisions
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Statistics and Research Methods for Business Decision Making

University: N/A

  • Unit No: N/A
  • Level: High school
  • Pages: 20 / Words 4895
  • Paper Type: Assignment
  • Course Code: HI6007
  • Downloads: 229
 

INTRODUCTION

In the present time, it has been observed that statistical methods and research skills provide managers with detailed information about the current happenings and the latest trends and changes in different external and internal business scenarios (Berman and Wang, 2016). This supports making meaningful and sound decisions when uncertainties arise in the business situation.

In this report, different statistical techniques are used to resolve the business problem if any, and make proper decisions. Different kinds of graphs are used to make proper comparisons, and various kinds of useful tables and figures are prepared in this report. In addition, Estimation and testing significance level are used to determine valuable results.

QUESTION 1

A) Appropriate Graphical Technique to Compare the Amount

Australian food and fiber exports by state, 2010 and 2015

 

Exports ($million)

State

2010

2015

Victoria

7,344

11,656

Queensland

4,872

8,179

NSW

4,959

6,979

WA

4,219

6,350

SA

3,391

5,255

Tasmania

907

736

Others

973

4,278

 
B) Appropriate Graphical Technique to Compare the Percentage Value of the Amount

State

% change

Victoria

36.99

Queensland

40.43

NSW

28.94

WA

33.56

SA

35.47

Tasmania

-23.23

Others

77.26

 

C) Comments on Part (a) and (b)

The bar chart presented in Part A helps describe the actual amount of food and fiber exports in different states of Australia (Bickel and Lehmann, 2012). It shows that data on the export of food products in the years 2010 and 2015 help in the easy comparison of states' contributions to export figures in the respective countries. It is determined from the graph that in the year 2010, Victoria was the state that contributed the highest in exports, as total exports were 7344 $ million. Similarly, Tasmania was considered to be the weakest state in terms of exports, as the total amount was only 907 $ million in the respective year. In the year 2015, Tasmania again had the highest figures for exports, which were 11656 $million, and the figures for exports decreased in the respective year. Tasmania was the only state which showed a negative change in the figures of exports through these years. It is also figuring out that in 2010, exports from other states were lower, which is 973 million. However, the Australian government took several steps, such as the easy availability of financial resources, that increased production levels. This helped to increase the overall export level, as in the year 2015 the total amount reached 4278 $ million, which helped increase the overall profitability of the nation.

The line chart displayed above supports the actual percentage change in the figure for exports between 2010 and 2015. It is clear from the graph that Tasmania was the only state that showed a negative percentage change from this year, which is -23. 23%. The chart also shows the highest percentage of alteration in another state of Australia, which shares the highest amount of export in the year 2015. The main reason for the increase in exports is the fall in the inflation rate in 2010 (which was 2.9%), and in 2015 it remained at 1.5. This helps to raise the Australian international competitiveness and will likely improve and raise the export level in the respective year.

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QUESTION 2

The numbers of weekly sales calls by a sample of 40 telemarketers are recorded and presented below table:

14

8

6

12

21

4

9

0

25

17

9

5

8

18

16

3

17

19

10

15

5

20

17

14

19

7

10

15

10

8

28

31

27

30

2

17

26

11

29

19

 

A)Frequency Distribution and a Relative Frequency Distribution.

Classes

Tally Frequency

Frequency (f)

Relative Frequency

(f/40)

0 - 4

I

4

0.1

5 - 9

IIIII IIII

9

0.23

10 - 14

IIIII II

7

0.18

15-19

IIIII IIIII I

11

0.28

20- 24

II

2

0.05

24 - 29

I

5

0.13

30 - 34

II

2

0.05

Total

40

40

 

Frequency Distribution: This is a description that allows to display either in tabular or tabular format the amount of findings inside a given period (Dittmar and Duchin, 2015). It is used primarily in a mathematical sense related to normal distribution forecasting.

Relative Frequency: This is a kind of allocation that focuses on counting the overall population and frequency for a sub-group of people. Throughout this technique, there is a systematic experimental approach where experiments must be done over and over again to obtain the best result from such a trial as it is a test and it is feasible to get a specific frequency (Hutton, Jiang, and Kumar, 2014).

From the above table, it is determined that tally frequency is primarily used to calculate the frequency distribution. Class intervals 20 - 24 and 30 - 34 have the lowest frequency and class intervals 15 - 19 have the highest frequency of 11.

B) Cumulative Frequency Distribution:

Classes

Frequency (f)

Relative Frequency

(f/40)

Cumulative Frequency

Cumulative Relative Frequency

0 - 4

4

0.1

4

0.1

5 - 9

9

0.23

13

0.33

10 - 14

7

0.18

20

0.5

15-19

11

0.28

31

0.78

20- 24

2

0.05

33

0.83

24 - 29

5

0.13

38

0.95

30 - 34

2

0.05

40

1

Total

40

 

 

 

As seen in the table above, the cumulative frequency could be described as a total of all corresponding frequencies up to the usual stage. It measures the accumulated frequency by summarizing the frequency at each point, whereas the relative accumulated frequency is determined by applying the relative frequency to the present time.

C) Relative Frequency Histogram

Classes

Frequency

Relative Frequency

Cumulative Relative Frequency

0 - 4

4

0.1

0.01

5 - 9

9

0.23

0.33

10 - 14

7

0.18

0.5

15-19

11

0.28

0.78

20- 24

2

0.05

0.83

24 - 29

5

0.13

0.95

30 - 34

2

0.05

1

Total

40

 

 

 

D) An O-Give Curve for the Data

To create an O-give curve, transforming information to less than the form or more than the form is necessary initially (Meyr, Myers, and Pontious, 2014). To draw an O-give graph of relative frequency in the context of relevant information Less-than-form of the O-give graph was produced as shown below:

Classes

Frequency

Relative Frequency

Less than type

Cumulative Relative Frequency

0 - 4

4

0.1

Less than 4

0.01

5 - 9

9

0.23

Less than 9

0.33

10 - 14

7

0.18

Less than 14

0.5

15-19

11

0.28

Less than 19

0.78

20- 24

2

0.05

Less than 24

0.83

24 - 29

5

0.13

Less than 29

0.95

30 - 34

2

0.05

Less than 34

1

Total

40

 

 

 

 

The o-Give curve for Cumulative Frequency is developed below:

Classes

Frequency (f)

Less than type

Cumulative Frequency

0 - 4

4

Less than 4

4

5 - 9

9

Less than 9

13

10 - 14

7

Less than 14

20

15-19

11

Less than 19

31

20- 24

2

Less than 24

33

24 - 29

5

Less than 29

38

30 - 34

2

Less than 34

40

Total

40

 

 

 

E) Proportion of Data Lower Than 20

From the table above, the data that is lower than 20 can be determined by using the following equation, such as:

Data less than 20 is 31

 Thus, 31/40= 0.775

F) Proportion of Data Higher Than 24

In the above tabular discussion, it is easy to figure out the information related to the frequencies that are higher than 24. thus

Data more than 24 is 7

So, 7/40 = 0.292 (approx.)

Question 3

A) Appropriate Graphic Descriptive Measure

According to the case, it was evaluated that inflation has reduced Australia's currency buying power at the moment. Consequently, due to increased inflation and reduced spending power intensity, shareholders' aspirations also increased, as they guessed how much returns they could get on their investment prices.

Year

Rate of inflation (%)

All-Ordinaries index

1995

1.9

2000.8

1996

4.6

2231.7

1997

2.6

2662.7

1998

0.3

2608.2

1999

1.3

2963

2000

2.4

3115.9

2001

5.9

3352.4

2002

2.9

3241.5

2003

3

3032

2004

2.4

3499.8

2005

2.4

4197.5

2006

2.9

4933.5

2007

2.5

6337.6

2008

4.3

5513.5

2009

2.4

4127.6

2010

2.9

4632.8

2011

3.3

4553.9

2012

1.6

4385.2

2013

2.5

5110.5

2014

2.9

5423.9

2015

1.5

5713.4

Graphical with Rate of inflation

Time graph of Ordinary index

B) Scatter Plot

C) Numerical Summary Plot

Year

Rate of inflation (%)

(x)

All-Ordinaries index

(y)

(x)2

1995

1.9

2000.8

3.61

1996

4.6

2231.7

21.16

1997

2.6

2662.7

6.76

1998

0.3

2608.2

0.09

1999

1.3

2963.0

1.69

2000

2.4

3115.9

5.76

2001

5.9

3352.4

34.81

2002

2.9

3241.5

8.41

2003

3.0

3032.0

9

2004

2.4

3499.8

5.76

2005

2.4

4197.5

5.76

2006

2.9

4933.5

8.41

2007

2.5

6337.6

6.25

2008

4.3

5513.5

18.49

2009

2.4

4127.6

5.76

2010

2.9

4632.8

8.41

2011

3.3

4553.9

10.89

2012

1.6

4385.2

2.56

2013

2.5

5110.5

6.25

2014

2.9

5423.9

8.41

2015

1.5

5713.4

2.25

Total

56.5

83637.4

 

Mean

2.7

3982.7

 

Median

2.5

4127.6

 

Mode

2.4

#VALUE!

 

variance

1.42

1530783.22

 

Std. derivation

1.19

1237.24

 

Central tendency calculation

Mean= = total of observations/actual number of observations

            = 56.5 / 20 = 2.85

From Excel (Mean is 2.7)

Median: Middle value in case of even series

            = (N / 2)

            = 20 / 2

            = 10th Observation So the median is 2.4 and the Excel value is 2.5

Mode: The value which is repeated the most number of times in the given series so the model value is 2.4.

Variance = ∑x2 / n -  Mean2

               = 180.49 / 20 - (2.7) 2

               = 9.02 - 7.29

               = 1.73 and by using Excel the value is 1.42.

Standard Deviation:

√variance = 1.73

            = 1.31 and by using Excel the std dev is 1.19.

Quartiles,

Q1 = ¼th of total observation

     = ¼th  * 20 is the 5th term so the value from the list is 2963.0

Q3 = ¾th * 20 which is 15th term and the value is 4632.8

Calculation for secondary variables (Ordinary index)

Mean

3982.7

Median

4127.6

Mode

0

Variance

1530783.22

Standard deviation

1237.24

Quartile

1.9 is 5th term and 2.3

 

D) Correlation Coefficient

 

Rate of inflation (%)

All-Ordinaries index

Rate of inflation (%)

1

 

All-Ordinaries index

0.038875116

1

 

Correlations

 

 

 

Rate of inflation (%)

All-Ordinaries index

Rate of inflation (%)

Pearson Correlation

1

-.011

Sig. (2-tailed)

 

.963

The sum of Squares and cross-products

32.952

-342.143

Covariance

1.648

-17.107

N

21

21

All-Ordinaries index

Pearson Correlation

-.011

1

Sig. (2-tailed)

.963

 

The sum of Squares and cross-products

-342.143

30615662.571

Covariance

-17.107

1530783.129

N

21

21

           

E) Estimating Regression Line 

F) Estimating Coefficient of Determination

            The coefficient of association indicates the interdependence of two factors, whereas negative correlation implies both being inversely proportional (Priebe and Spink, 2012). Though constructive, one illustrates a direct relation. Throughout this context, the significance of the correlation is determined as

R2 = 0.9892

            This states that there has been appropriate variation among the purchasing power of customers with dollar currency and the rate of inflation for that specific year.

G) Testing the Significance of the Relationship

            The significance could be checked on two faces, i.e. at 5 percent and 95 percent, respectively. In this scenario, the coefficient association was measured as 0.0388 at a significance point of 5 percent. This indicates that somewhere in the ordinary measure and inflation rate there is still no distinction.

Hypothesis= H0 : β1 = 0

                 = Ha : β1 ≠ 0

Level of significance α = .05

            Test statistic: t = b1 / Sb1

Rejection rule = Reject Hin case if p-value ≤ .05 or |t| > 3.182 (With 3 degrees of freedom)

Value of test statistic t = b1 / Sb1 = 5 / 1.08 = 4.63

Rejecting Hin case t = 4.63. 3.182, which can reject H0

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H) Value of the Standard Error

The standard error could be described as a statistical term that is being used to measure a sample's accuracy and is also expressed by an assignment of surveys (Sekaran and Bougie, 2016). In the context of the above data, specific formulas can be implemented such as:

Thus, from the inflation figures,  Ꝺest.. = √0.9892 = 0.9946

CONCLUSION

In conclusion, it has been outlined according to the above analysis that analysis and statistical techniques are important aspects of decision-making within a company. Some techniques also help to evaluate the situations of the company and the sector, which benefit in reducing the chances of problems, and if any arise, better solutions are provided. In or out of certain adverse business and sector conditions, various internal and external factors are also evaluated. The entire study analyses multiple possible scenarios, utilizing different methods for improved understanding and review.

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