Data Analysis And Statistics Exercises

Stem-and-leaf displays and frequency plots

1. Quarterly opening prices

Year Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper	Quarter	MQG	PPT		Year Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper	Quarter	MQG	PPT
2007	1	79.65	78.45		2013	1	37.86	38.84
2	85.56	80.91			2	38.54	41.20
3	81.13	77.50			3	43.16	39.90
4	82.66	74.26			4	50.11	46.00
2008	1	63.92	58.30		2014	1	54.09	47.04
2	62.37	54.84			2	57.70	46.90
3	50.58	40.76			3	58.52	48.82
4	28.91	34.93			4	61.17	46.45
2009	1	25.56	30.72		2015	1	62.15	49.18
2	32.93	32.18			2	77.97	54.16
3	43.29	33.51			3	82.15	44.85
4	49.17	37.62			4	85.70	44.91
2010	1	49.33	35.05		2016	1	71.58	41.31
2	49.46	34.55			2	63.50	42.75
3	36.58	29.03			3	74.39	45.50
4	35.60	37.85			4	79.80	45.24
2011	1	39.95	31.29		2017	1	84.60	46.80
2	34.58	28.89			2	93.00	52.78
3	27.11	23.58			3	85.83	50.48
4	24.50	22.75			4	98.35	48.48
2012	1	25.08	20.25
2	28.73	25.50
3	24.57	23.91
4	31.37	27.94

MQG and PPT Stem-and-Leaf Plot                      Stem &  Leaf                98753320 . 2 .  4455788             98775443210 . 3 .  12456789        9887666655442110 . 4 .  33999                    4420 . 5 .  00478                         . 6 .  12233                     874 . 7 .  14799                       1 . 8 .  1224555                         . 9 .  38  Each leaf: 1 case(s)

(Fletcher, 2009)

1. Histogram and Frequency polygon for MQG and PPT

Figure 1: Histogram and Frequency polygon of MQG and PPT

1. Market capitals for 6 companies listed in ASX with over AUD 500 million

Figure 2: Market capitals

1. Invest in MQG or PPT

Based on the series of previous share prices from 2007 to March 2018, I would advise on investing with PPT because the prices are down and there is a possibility of a rise in a few moments. This is in comparison to MQG whose prices have been rising since 2010 and just as any other stock price, there are very high chance of the prices losing value. Therefore, it would be more advisable to invest in a stock whose price has a higher chance of increasing than decreasing. In addition, PPT has a higher dividend yield of 6.65% compared to MQG which is 4.66%. Further, PPT has a lower Price-earning Ration compared to MQG, which make PPT stock better to invest in because their future growth has higher chances of growth(Aspara, 2009; Lynott, 2005).

1. Mean, median, 1^stand 3^rd Quartiles

	TLS	SPK	TPM	CNU	VOC
Mean	13.37	13.33	18.65556	11.76667	21.34
Median	13.75	12.85	15.8	10.95	22.25
First Quartile	10.85	11.5	14.2	6.7	12.625
Third Quartile	15.475	15.55	25.3	17.475	28.9

1. Standard Deviation, Mean Absolute Distance and Range statistics

	TLS	SPK	TPM	CNU	VOC
Standard Deviation	2.322951	2.148046	6.225951	4.764335	10.46501
Mean Absolute Distance	2.056	1.936	5.617284	4.533333	8.56
Range	6.8	6.2	19.2	11.3	36.6

(Cohen, Manion, & Morrison, 2011)

1. Box and Whisker Plots

Figure 3: Box and Whisker Plots

1. A discussion on the price-earnings ratio values

Higher values of price-earnings ratio show that the stock is overpriced while stocks with small values are seen to be better because they have higher growth potentials. Therefore, CNU has the highest growth potential compared to the others because the median measure small compared to the others. However, SPK has the least margin for the 3^rd and 1^st quartile, hence lower deviation from the median. It is very risky to invest in VOC stock because it is hard to estimate its ideal price-earnings ratio because of the higher variation. Therefore, it will be safer to invest in CNU, SPK and TLS stock as opposed to TPM and VOC(Titman, Wei, & Xie, 2004).

1. The probability that an Australian will die from neoplasms.

1. The probability of a Female Australian to die from disease of the circulatory system

1. Proportions of deaths

The disease with the highest proportion of male deaths compared to female deaths is Neoplasms with a difference of 6.3%. Diseases of circulatory system such as heart disease have the highest difference in proportion between female deaths and male deaths with a difference of 3.4%.

1. The probability of dying from diabetes mellitu

(McCluskey & Lalkhen, 2007)

1. Probability of rainfall
2. The probability that there will be no rainfall in any given day

1. The probability of 2 or more days of rainfall in a week

1. Assuming that the weekly rainfall follows a normal distribution

The mean weekly rainfall is 9.9132mm and a standard deviation of 12.297mm

1. The probability of having rainfall between 5mm to 15mm

1. Amount of rainfall if only 10% of the weeks have that amount of rainfall or higher

 (Tsokos, Wooten, Tsokos, & Wooten, 2016)

The amount of rainfall will be  

1. Normality Test

Refractive Index

Figure 4: Refractive index for refractive index

Sodium

Figure 5: Probability plot for Sodium

Magnesium

Figure 6: Probability plot of Magnesium

Aluminium

Figure 7: Probability plot of Aluminium

Silicon

Figure 8: Probability plot of Silicon

Potassium

Figure 9: Probability plot of Potassium

Calcium

Figure 10: Probability plot of Calcium

Barium

Figure 11: Probability plot of Barium

Iron

Figure 12: Probability plot of iron

Refractive index, Silicon, Aluminium, and Sodium are the only variables which are approximately normally distributed(Abdal-sahib et al., 2013; Tsokos et al., 2016).

1. Confidence intervals of Float and non-float glass

Table 1: Float Glass Confidence intervals for normally distributed variables

	Statistic	Std. Error
Refractive index	Mean	1.5185708	.00023735
95% Confidence Interval for Mean	Lower Bound	1.5180990
Upper Bound	1.5190426
Sodium	Mean	13.2803	.05402
95% Confidence Interval for Mean	Lower Bound	13.1730
Upper Bound	13.3877
Aluminum	Mean	1.1711	.03080
95% Confidence Interval for Mean	Lower Bound	1.1099
Upper Bound	1.2324
Silicon	Mean	72.5772	.06030
95% Confidence Interval for Mean	Lower Bound	72.4574
Upper Bound	72.6971

Table 2: Non-Float Glass Confidence intervals for normally distributed variables

	Statistic	Std. Error
Refractive index	Mean	1.5186186	.00043613
95% Confidence Interval for Mean	Lower Bound	1.5177497
Upper Bound	1.5194874
Sodium	Mean	13.1117	.07618
95% Confidence Interval for Mean	Lower Bound	12.9599
Upper Bound	13.2635
Aluminum	Mean	1.4082	.03652
95% Confidence Interval for Mean	Lower Bound	1.3354
Upper Bound	1.4809
Silicon	Mean	72.5980	.08311
95% Confidence Interval for Mean	Lower Bound	72.4325
Upper Bound	72.7636

References

Abdal-sahib, R., Altammar, S. M., Azlan, H. A., Aytemur, A., Balters, S., Steinert, M., … Canny, J. (2013). Testing for Normality. Frontiers in Psychology, 98(1), 1–8. https://doi.org/10.3389/fpsyg.2014.01470

Aspara, J. (2009). Aesthetics of stock investments. Consumption Markets & Culture, 12(2), 99–131. https://doi.org/10.1080/10253860902840917

Cohen, L., Manion, L., & Morrison, K. (2011). Descriptive Statistics. In Research methods in education (pp. 622–640). https://doi.org/10.1213/ANE.0000000000002471

Fletcher, J. (2009). Normal distribution. BMJ, 338(feb18 2), b646–b646. https://doi.org/10.1136/bmj.b646

Lynott, W. J. (2005). Stock investments. Water Well Journal, 59(12), 39. Retrieved from https://www.scopus.com/inward/record.url?eid=2-s2.0-30344484585&partnerID=40&md5=ba73235c348c82cf518a1f10cd84cbe4

McCluskey, A., & Lalkhen, A. G. (2007). Statistics III: Probability and statistical tests.s, 7(5), 167–170. https://doi.org/10.1093/bjaceaccp/mkm028

Titman, S., Wei, K. C. J., & Xie, F. (2004). Capital Investments and Stock Returns. Journal of Financial and Quantitative Analysis, 39(04), 677. https://doi.org/10.1017/S0022109000003173

Tsokos, C., Wooten, R., Tsokos, C., & Wooten, R. (2016). Normal Probability. In The Joy of Finite Mathematics (pp. 231–263). https://doi.org/10.1016/B978-0-12-802967-1.00007-3