Get Instant Help From 5000+ Experts For
question

Writing: Get your essay and assignment written from scratch by PhD expert

Rewriting: Paraphrase or rewrite your friend's essay with similar meaning at reduced cost

Editing:Proofread your work by experts and improve grade at Lowest cost

And Improve Your Grades
myassignmenthelp.com
loader
Phone no. Missing!

Enter phone no. to receive critical updates and urgent messages !

Attach file

Error goes here

Files Missing!

Please upload all relevant files for quick & complete assistance.

Guaranteed Higher Grade!
Free Quote
wave

Summary of Main Findings

Discuss about the Analysis of Mobile Phone Data Analyst.

Date: 12th May, 2018

To: Michelle Yeoh, Chief Data Analyst

From: Grace Park, Research and Analysis Department

Subject: Re: Analysis of Mobile Phone Data

Dear Michelle

I have finished analysing the Mobile Phone market data that was provided to me via the memorandum dated 14th April 2018. Based on my analysis of the data provided,  I have framed by responses for the specific questions that were raised by you through the memorandum sent in April. The summary of the main findings is presented below.

It has been found that the mean monthly bill for the sample users of whom data was provided is about $ 67.65. Also, it is noteworthy that the amount of variation witnessed in the monthly bill is significant owing to various factors such as differences in usage and the plans chosen. Further, no significant difference has been found in the average monthly bill for the different lifestyle groups. In addition, the average monthly bill for an Australian smartphone user has been estimated to lie in the range between $62.62 and $72.68. Also, smartphone use in making payments represents a move towards cashless economy considering that the proportion of smartphone users in Australia who are estimated to use smartphones for making payments would lie between 69.17% and 82.83%.  Further, the gender differences in this regards is not found to be significant. The given data tends to support claim of your colleague that more than 75% of the smartphone users in Australia tend to use smartphone for work. However, the given data does not support your suspicion in regards to the rival claim that daily smartphone users make atleast 27 calls. In relation to the variation of the monthly bill, it is estimated that the most significant factor is data allowance. Also, considering the requirements of the next year survey, a minimum sample of 266 users would be required.

The detailed responses are highlighted below.

  • I have computed the various statistics related to summarising the “Monthly Bill” variable. Based on this, it may be estimated for the sample 150 users whose usage details were included in the data provided, mean monthly bill is $ 67.65. Also, considering the median monthly amount of $64, it would be fair to conclude that 50% of the mobile users included in the dataset i.e. 75 users had a mobile bill which did not exceed $ 64. The monthly bill that occurs most frequently is $ 50. Also, there seems to be a high variation in the monthly bill for the users with the minimum bill amount of $ 11 and the maximum bill amount $ 216. Hence, there are certain monthly bills which are abnormally high considering the average bill of users. Thus, it is apparent that based on the individual preference, usage and other factors, the bill amount for the smartphone across users is not homogeneous. Additionally, other indicators of dispersion in the data also hint to variation in the monthly bill amongst users being significant.
  • The objective is to outline if selected lifestyle factors tend to have an impact on the monthly bill or not considering the given data of 150 mobile users. Using the relevant statistical techniques, an estimate of the average monthly bill according to the different lifestyle factors has been worked out for the population of Australian mobile users. The three selected lifestyle factors that are considered important are Achievers, Independents along with Suburban Splendour.

Based on the analysis in the attached excel, it can be estimated with 95% confidence that the achievers population of mobile users tend to have an average monthly bill in the range of $ 54.90 and $87.80. Also, there is a 5% chance that the monthly average bill for the achievers lifestyle group would not lie in the range specified above.

Based on the analysis in the attached excel, it can be estimated with 95% confidence that the independents population of mobile users tend to have an average monthly bill in the range of $ 59.19 and $80.57. Also, there is a 5% chance that the monthly average bill for the independents lifestyle group would not lie in the range specified above.

Based on the analysis in the attached excel, it can be estimated with 95% confidence that the suburban splendour population of mobile users tend to have an average monthly bill in the range of $ 61.58 and $82.18. Also, there is a 5% chance that the monthly average bill for the suburban splendour lifestyle group would not lie in the range specified above.

The above consumer categories would be said to have different average monthly bills if there is atleast one category for which the estimation of population mean does not overlap with any of the other two categories. Clearly, this is not the case here, since there is significant overlapping in the average monthly bill estimates for the three categories of mobile users according to lifestyle factors. Thus, it would be appropriate to conclude that the common geoTribe categories do not differ in their monthly spend on smartphones as highlighted through above estimates.

  • A) The objective of this task was to estimate the average monthly bill of users across Australia. Based on the relevant statistical analysis in the attached excel, it can be estimated with a confidence of 95% that the average monthly bill for all Australian mobile users would lie between $62.62 and $72.68. Also, there is a 5% chance that the monthly average bill for the Australian mobile users would not lie in the range specified above.
  1. B) The provided data with regards to proportion of users those who use smartphone as a payment device has been used for computing the estimated proportion for the Australian population. Based on the computations highlighted in the attached excel, it may be concluded with 95% confidence that the proportion of Australian smartphone users who use smartphone as a payment means would lie between 69.17% and 82.83%. Clearly, this is quite substantial and represents an active step towards the cashless economy.
  2. c) The objective of this task is to ascertain if there are any gender differences with regards to usage of smartphone as a payment device. Based on the given data, it can be estimated with 95% confidence that the proportion of females that would use smartphones as a payment device would lie between 62.27% and 82.04%. On the other hand, it can be estimated with 95% confidence that the proportion of males that would use smartphones as a payment device would lie between 71.03% and 89.54%. The two estimates for male and female clearly overlap and hence it would be appropriate to conclude that no difference is observed between the male and female usage of smartphone as a payment device.

The objective of this statistical analysis is to ascertain whether the claim made by your colleague is true. The data provided does tend to lend support thereby highlighting that there is a 95% probability that the claim made by your friend is correct. This clearly highlights the growing tendency of the people to use smartphones in their work and expand usage from the personal to the professional domain. Also, going forwards this may have significant implications for the workplace considering that exciting opportunities and challenges would be presented owing to increased usage of smartphone at workplace or for professional purposes.

  1. b) The objective of this statistical analysis is to ascertain if the claim made by the business rival in relation to the average phone calls made by smartphone owners in Australia is indeed overstated or not. The data provided does not lend support to your suspicion. Based on this, it is evident that indeed the estimate of average calls by Australians using smartphones seems to have been correctly estimated by your rival firm. This can potentially have significant implications for the mobile companies and the expected traffic that their respective networks have to bear so as to ensure that the requisite infrastructure is in place to ensure superior service quality to customers.


The objective of this task is to highlight the impact of certain identified factors in relation to the monthly mobile bill variation seen between customers.

  1. a) Number of calls – There is a positive association between the number of calls and the monthly mobile bill. This is not surprising since higher number of calls would typically lead to higher mobile bill. However, the relationship between the two variables is weak which may be explained on account of this variable not including the duration of calls which can be a significant determinant of the monthly bill. Also, it might be possible that the monthly bill may be driven by the data usage and other value added services provided by the operator. The R2 value clearly highlights that only 17.36% of the variation in the monthly bill is explained on the basis of number of calls.
  2. b) SMS’s – There is a positive association between the number of SMS’s and the monthly mobile bill. This is not surprising since higher number of SMS’s would typically lead to higher mobile bill. However, the relationship between the two variables is week and shows lower strength as compared to the association between number of calls and monthly mobile bill. The R2 value clearly highlights that only 10.37% of the variation in the monthly bill is explained on the basis of the SMS.
  3. c) Data Allowance – There is a positive association between data allowance and the monthly mobile bill since higher data allowance comes at a higher bill. Also, the strength of the relationship is medium to strong. Further, on the basis of the R2 value, it can be inferred that 51.58% of the variation in the monthly bill is explained on the basis of the data allowance.

On the basis of the comparison of the above factors, it is apparent that the R2 value is highest for data allowance and lowest for the SMS. Hence, the most important factor from the above list is data allowance while the least significant factor is SMS’s.

  1. The objective of this statistical analysis is to highlight if the current sample data provided is too small considering the parameters in relation to the study to be conducted the next year. This discussion becomes imperative since sample size is a critical aspect in ensuring that the selected sample is representative of the population of interest.
  2. a) The margin of error allowed is 6%. Considering the proportion in the data presented, the minimum sample size is recommended as 266. Therefore considering the requirements provided and the underlying proportion, it may be concluded that the current sample size of 150 would be insufficient for conducting the next year study considering the minimum sample size exceeds the sample size taken for this year’s study. Continuing with the current sample size could result in the results being biased and hence lacking in reliability.
  3. b) The monthly bill needs to be estimated within a tolerance level of $4 error. Based on the relevant statistical computations presented in attached excel, the minimum size requirement would be 234. Hence, the current sample size of 150 would be insufficient for conducting the next year study considering the minimum sample size exceeds the sample size taken for this year’s study.

Thus, on the basis of the above discussion considering both (a) & (b) as requirements of the next year survey, the minimum sample size should be 266.

Regards

Grace

Cite This Work

To export a reference to this article please select a referencing stye below:

My Assignment Help. (2019). Analysis Of Mobile Phone Data Analyst. Retrieved from https://myassignmenthelp.com/free-samples/analysis-of-mobile-phone-data-analyst.

"Analysis Of Mobile Phone Data Analyst." My Assignment Help, 2019, https://myassignmenthelp.com/free-samples/analysis-of-mobile-phone-data-analyst.

My Assignment Help (2019) Analysis Of Mobile Phone Data Analyst [Online]. Available from: https://myassignmenthelp.com/free-samples/analysis-of-mobile-phone-data-analyst
[Accessed 03 March 2024].

My Assignment Help. 'Analysis Of Mobile Phone Data Analyst' (My Assignment Help, 2019) <https://myassignmenthelp.com/free-samples/analysis-of-mobile-phone-data-analyst> accessed 03 March 2024.

My Assignment Help. Analysis Of Mobile Phone Data Analyst [Internet]. My Assignment Help. 2019 [cited 03 March 2024]. Available from: https://myassignmenthelp.com/free-samples/analysis-of-mobile-phone-data-analyst.

Get instant help from 5000+ experts for
question

Writing: Get your essay and assignment written from scratch by PhD expert

Rewriting: Paraphrase or rewrite your friend's essay with similar meaning at reduced cost

Editing: Proofread your work by experts and improve grade at Lowest cost

loader
250 words
Phone no. Missing!

Enter phone no. to receive critical updates and urgent messages !

Attach file

Error goes here

Files Missing!

Please upload all relevant files for quick & complete assistance.

Other Similar Samples

support
Whatsapp
callback
sales
sales chat
Whatsapp
callback
sales chat
close