1) In first question, there is a negative concern within the question that is not appropriate in question design. Researcher is giving negative option to the respondent in advance by asking “you would not vote for Tony Abbott”. Alternative may be as below:
Would you vote for Tony Abbott?
2) In second question, it is not right way to ask the frequency for a habit for different large gaps like once a week, once a month and once a year. There should be a proper gap to determine a habit pattern. This question can be framed as below:
How often do you exercise per week?
· Not at all
· Once a week
· 2-3 times a week
· 4+ times a week
3) It will be better to not give any option to the customers regarding the aspects of the product. The below question will suggest the firm that the new product is effective to meet the requirements and specifications of the customers.
Will our new product or service meet customers’ specifications?
There may be issues with the results of this poll because there is no significant difference between the voting preferences for liberal and labour parties. At the same time, responses in form of undecided and declined to answer may affect the results by changing the polls results for winning party.
(a) Correlation Coefficient
|$’000 Sales (Y)||XY||X^2||Y^2|
For calculating correlation coefficient, the below formula can be used:
(Source: Newbold, et al. 2012)
= 187620 /
(b) Relationship between sales and advertising
On the basis of the above calculated correlation coefficient, it can be interpreted that there is a positive and strong relationship between sales and advertising. Correlation coefficient near to 1 (0.9993) shows that if the firm increases advertising budget, it will also increase the sales respectively.
(c) Equation of the regression line:
(Source: Newbold, et al. 2012)
The y-intercept, b, of the best-fitting line can be determined as below:
y = mx + b
y = 18.28X+14.76
(i) Z=(X-µ)/ s
Probability = 0.1587
(ii) Z=(X-µ)/ s
At least $1.4 million = (1-p)
Probability = (0.02275 or 0.97725)
(b) Range for 80% confident that sales will fall
At 80% significance, z value is 1.28
=Lower limit $744000
=Upper limit $1256000
Range = $744000 to $1256000
- a) Simple quantity index for electricity
- b) Simple aggregate quantity index for energy consumption
Q aggregate= (∑qn/∑q0)*100%
= (50+60+45)/ (100+85+90)*100%
- c) Simple average quantity index for energy consumption
|Energy consumption||Units in millions||2012||Qn/q0|
Simple average quantity index = 1/n∑(qn/q0) *100
= 1/3 (1.706)*100%
|Day of the week||Average revenue|
H0: µ< $2550
= -62 /28.21
p-value (0.5) = 0.0284
The p-value is less than .05. Therefore, the null hypothesis can be rejected. The claim is validate for the revenue on a week day (Monday-Thursday).
H0: µ< $5350
t= (5320.33-5350)/( 457.92/
= -29.67 /23.97
p-value (0.5) =.2169
The p-value is greater than .05. Therefore, the null hypothesis cannot be rejected. The claim is not validate for the revenue on a week day (Friday-Sunday) (Weiers, 2010).
(i) From the below table showing relative frequency, it can be determined that 14% of calls in the sample are answered in more than 2 minutes.
|Answering time||Frequency||Relative frequency|
|Within 1 minute||14||28.00%|
|Within 2 minutes||23||46.00%|
|Within 3 minutes||4||8.00%|
|Within 4 minutes||2||4.00%|
|Within 5 minutes||1||2.00%|
(ii) A bank claims that 85% of phone calls are answered within 2 minutes. On the basis of the above table, it can be stated that there is sufficient evidence to support the bank’s claim as 86% of calls are answered within 2 minutes.
- What would you do in order to apply statistical methods to work?
There is a need full study and deeply knowledge of statistic in order to apply the research methods to work in financial service industry. Statistical methods plays a significant role to interpreted the data correctly and make clear relationships by the use of financial data tools. In other words, statistical methods help to ensure the interpretation of the correct data and provide the relationship with the standardized way. The researcher may use several statistical analyses by the statistical methods to perfectly understand the data requirement (Pande et al., 2010). The scientists also use the statistical methods for working properly their exploratory research to make success in their aim.
- What steps would you take to source and interpret statistical data?
There are five steps that can be taken for sourcing and interpreting the statistical data. In the first step, the questions are defined for the analysis of data by the researcher. After this, the clear measurement priorities should be set by deciding the measures. The next step includes the collection of the correct data by the use of primary and secondary data collection methods. After the data collection, the research needs the analysis the data in the different format to reduce the complexity of collected data (Bock, 2012). Finally, the overall data are used for interpreting the result to conduct the research systematically.
- What process should you use to data analysis?
The data analysis process can be used to analyze the financial and statistical data. The quantitative research will be used in this case because it focuses on the numbers of the data. This data analysis is efficient for the research because it use the static and rigid approach for inflexibility in the process (Zhao et al., 2010). There is a less need of analysis the behaviour and attitude of the consumer do to focus on the measurement of the quantity. The quantitative research provides the high accuracy and objectivity to the data analysis that provide the summary of the useful data for the good research.
- What is economical modelling? Give an example.
The economical modelling is a hypothetical construct that provides both theoretical and practical information in order to economics at the international level. The economic model is helpful to predict the economic activities that are based on the assumptions (Al-Zahrani, 2010). It is designed for solving the complex problem related with the economics. For example- if an organization wants to predict the low of demand of the company then it can use the economical modelling that when the price should go up and down.
- Describe what liquidity ratios are.
The liquidity ratio is the analysis of the ratio for the liquidity and financial position of the company. It computes the ability of the payment to its short term debts of the company. In other words, the liquidity ratio defines the assets that are available in the cash and easily convertible in the cash against the outstanding debts (Kirkham, 2012). This ratio analyst is helpful to find the financial position of the company according to the corrective measurement of the liquidity. A high ratio of the liquidity indicates that the company is in the low risk and low ratio state that company is in risk to pay their short term debts.
- Where can you source information for financial and economic statistical and analytical data?
The are many sources of the information related with the financial and economic statistical and analytical data such as publications, Economist intelligence units, companies monthly economic news, primary research, by the use of global financial data, research of financial markets, etc. Additionally, the information can also collected from internet, communication with email, conducting interviews, web sites, use the social media through social networking, reading financial and economic books and blogs, etc. (Brown, et al., 2011). Analytical data and statistical data can be found from the articles related with the financial data, economic generals, case studies of the related companies with the finance, financial literatures etc.
- What is market research?
Market research is the process of conducting the research for understanding the perception of the consumer for new product in the target market. It is the process of discovering the value of the product in the target market by recording the opinion of the consumer. It is seen that market research may be conducted by the company or may be conducted by the specialist of the field of market research for the better result (Podsakoff et al., 2012). It tests the viability of the product and service of the company that can be helpful for making the effective plan and strategy.
- Briefly describe mean, median, mode, range and standard deviation.
The mean, mode and median are the central tendency in the statistics. In the statistics, the mean is the average of provided number and it also known as arithmetic mean. The mode is the highly repeated number within set of number or a number that has highest value of the frequency. The median id the value that has placed at the median or it is the middle number value within the set on number. In the concern of the range, it is the difference among the highest value of number and lowest value of number in a set. The standard deviation measures the dispersion by the use of mean in a set of data. Additionally, it is the square root of the variance and useful to calculate the rate of return.
- What are the general purpose, goal and aim of APRA’s industry risk management framework?
The aim of APRA’s industry risk management framework is to provide the standard to the industry for managing the risk related with the finance of the industry. APRA is very important to ensure the efficiency and effectiveness of the industry for risk management framework. APRA provides the regulations and reviews to manage the financial risk at broader level in the industry. APRA delivers the high quality supervision to regulate the financial sector by providing the security from risk (Royal et al., 2014). It also provides the commitment of providing the sound prudential policies which is supported by expert research and analysis.
- What is the aim and purpose of data analysis?
The main aim and purpose of the data analysis is to organize and represent the data in a systematic way. It provides the simple comparison between the two or more big data and help to solve the major problems in breaking in micro parts (Bazeley and Jackson, 2013). The data analysis provides help to structuring the data that are found from the different sources. The purpose of the data analysis is to convert the raw data into the useful information and provide the statistical output through publishing the analytical article.
- What is cross-validation and what is its goal?
The cross validation is the evolution method or technique that estimates the performance of the predictive model. It is also called the rotation estimation and it is a technique that assesses the statistical results. The main goal of the cross validation is to predict and estimate the performance of the practice (Pedregosa et al., 2011). Additionally, it is used for propose of ensuring the validation of the complete data. The cross validation also helps in order to improve the model performance.
- What do records tell us?
Records tell us about meaningful information and data that can be used to make informed decisions. At the same time, records also provide guidance to the individual about the procedures and processes that have been taken previously. It helps to manage the works in current context and avoid the mistakes in future concerns.
Al-Zahrani, K.S., (2010) Operational simulation and an economical modelling study on utilizing waste heat energy in a desalination plant and an absorption chiller (Doctoral dissertation, Newcastle University).
Bazeley, P. and Jackson, K. eds., (2013) Qualitative data analysis with NVivo. USA: Sage Publications Limited.
Bock, C., (2012) Analysing and interpreting DNA methylation data. Nature reviews. Genetics, 13(10), p.705.
Brown, B., Chui, M. and Manyika, J., (2011) Are you ready for the era of ‘big data’. McKinsey Quarterly, 4(1), pp.24-35.
Kirkham, R., (2012) Liquidity analysis using cash flow ratios and traditional ratios: The telecommunications sector in Australia. The Journal of New Business Ideas & Trends, 10(1), p.1.
Newbold, P., Carlson, W. and Thorne, B., (2012) Statistics for business and economics. UK: Pearson.
Pande, V.S., Beauchamp, K. and Bowman, G.R., (2010) Everything you wanted to know about Markov State Models but were afraid to ask. Methods, 52(1), pp.99-105.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. and Vanderplas, J., (2011) Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(Oct), pp.2825-2830.
Podsakoff, P.M., MacKenzie, S.B. and Podsakoff, N.P., (2012) Sources of method bias in social science research and recommendations on how to control it. Annual review of psychology, 63, pp.539-569.
Royal, C., Evans, J. and Windsor, S.S., (2014) The missing strategic link–human capital knowledge, and risk in the finance industry–two mini case studies. Venture capital, 16(3), pp.189-206.
Weiers, R.M., (2010) Introduction to business statistics. USA: Cengage Learning.
Zhao, X., Lynch Jr, J.G. and Chen, Q., (2010) Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of consumer research, 37(2), pp.197-206.