Statistics on Share Prices, Market Capital, Motor Vehicle Sales and Internet Access in Australian Households
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This document presents statistics on share prices, market capital, motor vehicle sales and internet access in Australian households. It includes data on prices of WBC and NAB, market capital of CBA, ANZ, HGH, AFG, BEN, and CYB, new motor vehicle sales by state, and households with internet access by state or territory.
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Statistics5 15171921232527293133353739 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 8 9 NAB & WBC Share price Frequency Share prices c) Companie s Market Capital (Million $Aus) CBA129,320.00 ANZ75,150.00 HGH742,200.00 AFG263,150.00 BEN5,500.00 CYB4,990.00
Statistics6 CBAANZHGHAFGBENCYB - 100,000.00 200,000.00 300,000.00 400,000.00 500,000.00 600,000.00 700,000.00 800,000.00 129,320.00 75,150.00 742,200.00 263,150.00 5,500.004,990.00 Companies Market capital in Mill $Aus d) NAB has a P/E ratio of 12.370 whereas WBC has a P/E ratio of 11.010. An investor is recommended to invest in WBC with low P/E ratio. This is because a low P/E value is an indication that the growth potential of that company is still not known to the market hence, a good opportunity to venture into it (Hirsa, & Neftci, 2013;Satchell, & Knight, 2011). On the other hand, a relatively high P/E value is a warning of an over-stocked price, hence not advisable for investment (Baker & Powell, 2009; Hjalmarsson, 2010). QUESTION 2 a), b) MonthState NSWVICQLDSAWATASNTACT Jan-162839222426171935208786112986261509 Feb-163263326782187115988872513198511528 Mar- 16 35963277162147060879054151510231550 Apr-2939123963177535174760113139691563
Statistics8 NSWVICQLDSAWATASNTACT Min2839222426168705174758012986261509 Q1 3003.7 53547 1114.7 521623073.5163.518 Media n 1354.2 51107.5737.25578.5275257.57619 Q31080898877.25341.75346.625277.7511714.75 Max96196283.5 7670.7 5 1302.7 5 2317.37 5167.25257.512.25 12345678 0 1000 2000 3000 4000 5000 6000 New MotorVehicle sales By state State Motor Vehicle sales d. NSW had the highest sales of new motor vehicles (43,449), followed by VIC (24, 262) and NT recorded the least sales (1,240). There was a substantial decline in the trend of the number of cars sold between NSW, VIC, and QLD states. The trend of decline between SA and WA was relatively minimal, and the trend among the remaining (TAS, NT, and ACT) was very minimal and relatively insignificant. Question 3 a. Households with internet access 2008- 09 2010- 11 2012- 13 2014- 15 2016- 17 000000000000000 State or territoryTotal New South Wales1890.22163.62274.52407.92439.911176.
Statistics9 1 Victoria1468.61683.51830.61934.22008.28925.1 Queensland1182.11347.315171552.41591.97190.7 South Australia431.8496.5554565.1575.52622.9 Western Australia626.1714814.8843.6859.73858.2 Tasmania125.7146.2164.6172177.7786.2 Northern Territory45.550.758.858.157.6270.7 Australian Capital Territory 108.2121.8128.9137.2140.1636.2 Total5878.26723.67343.27670.57850.635466. 1 (ABS, 2018) Probability that a randomly selected household lives in Victoria = 8925.1/ 35466.1 =0.251652 =0.252 b.probabilitythata randomly selected household lives in Tasmania and has an internet access in the year 2010-11 P (household lives in Tasmania) x (has internet access in 2010-11) =(786.2/35466.1) x (146.2/6723.6) =0.0221676 x 0.0217443 = 0.043912 = 0.044 c.household has an internet access in 2012-13, what is the probability that a randomly selected household lives in New South Wales? P (household has internet access in 2012-13) + P (household selected lives in NSW) = (7343.2/35466.1) + (11176.1/35466.1) =0.2070484 + 0.3151206 = 0.5221691 = 0.522
Statistics 10 d.probability that a randomly selected household has an internet access in 2010-11 or 2012-13? P (household has internet in 2010-11) + P (household has internet in 2012-13) = (6723.6/35466.1) + (7343.2/35466.1) =0.1895782 +0.2070484 =0.3966266 =0.397 Question 4 a. Mean = 73 mins sd = 8 mins upper 5% = ? X∼N (73, 82) Upper 5% = 3.65 Z =(76.65-73)/8 = 0.456 b. let X = number of degrees Celsius, a = 12, b = 25, X∼U (12, 25). Probability density function, f (X) = 1 /25-12= 1/ 13 for 12 ≤ X ≤ 25. Find P (X < 21) = (21-12) x 1/13 = 0.692 c. i.probability that the sample percentage of correct identifications is greater than 65 P ( X>0.65) = (base) (height) = 0.35 x 1 = 0.35 ii. probability that the sample will have between 50% and 60% of the identifications correct Let X represent the correct sample tested, X∼U (0.5, 0.6)
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Statistics 11 Probability that the selected sample falls between 50% and 60% f (X) = 1 /0.6 - 0.5 = 1/ 0.1 for 0.5 ≤ X ≤ 0.6 = 0.1 x 1/0.1 = 1 (Spiegel, Schiller, Srinivasan, & LeVan, 2009) Question 5 a. let X = successful trials, a =0.16, b= 0.71 X∼U (0.16, 0.71) Probability density function, f (X) = 1 /0.71-0.16= 1/ 0.55 for 0.16 ≤ X ≤ 0.71. Find P (X >= 0.7) = (0.7 +0.16) x 1/0.55 0.86/0.55 = 1.564 b. cancelling flight after booking 210/500 = 0.42 let X cancelled bookings, a =0.42, b= 0.5 X∼U (0.42, 0.5) Find P (0.42 <=X >= 0.5) = 0.08 c. X x- 101.22 2 (x- 101.22 2) 110 8.7777 78 77.049 38 123 21.777 78 474.27 16 80- 21.222 450.38 27
Statistics 13 Dividing sd by the square root of sample size 21.88/√(9) = 7.205 Multipliying by t-value 7.295 x 2.306 = 16.822 Lower end = 101.22 -16.822 = 84.398 Upper end = 101.22 +16.822 =118.042 CI (84.398, 118.042)
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Statistics 14 References Australian Bureau of Statistics (ABS), (2017).New motor vehicles sales by State between Jan 2016 and Dec2017. Retrieved from http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/9314.0December %202017?OpenDocument Australian Bureau of Statistics (ABS), (2018).Household Use of Information Technology, Australia, 2016-17. Retrieved from http://www.abs.gov.au/ausstats/abs@.nsf/mf/8146.0 Australian Security Exchange (ASX). (n.d).Prices and Research. Retrieved from https://www.asx.com.au/asx/share-price-research/company/NAB Baker, H. K., & Powell, G. (2009).Understanding financial management: A practical guide. John Wiley & Sons. Hirsa, A., & Neftci, S. N. (2013).An introduction to the mathematics of financial derivatives. Academic Press. Hjalmarsson, E. (2010). Predicting global stock returns.Journal of Financial and Quantitative Analysis,45(1), 49-80. Satchell, S., & Knight, J. (2011).Forecasting volatility in the financial markets. Elsevier. Spiegel, M. R., Schiller, J. J., Srinivasan, R. A., & LeVan, M. (2009).Probability and statistics(Vol. 2). New York: Mcgraw-hill.