Construct a relative frequency histogram for these data

Reference no: EM131049197

Question 1-

Visit the Australian Stock Exchange website, www.asx.com.au and from "Prices and research" drop-down menu, select "Company information". Type in the ASX code "CCL" (Coca-Cola Amatil Limited), and find out details about the company. Your task will be to get the opening prices of a CCL share for every quarter from January 2001 to December 2015. If you are working with the monthly prices, read the values in the beginning of every Quarter (January, April, July, October) for every year from 2001 to 2015. It is part of the assignment task to test your ability to find the information from an appropriate website. If you are unable to do so, you may read the values from the chart provided below obtained from Etrade Australia. Obviously, reading from the chart will not be accurate and you may expect around 60 percent marks with such inaccuracy. After you have recorded the share prices, answer the following questions:

(a) List all the values in a table and then construct a stem-and-leaf display for the data.

(b) Construct a relative frequency histogram for these data with equal class widths, the first class being "$4 to less than $6".

(c) Briefly describe what the histogram and the stem-and-leaf display tell you about the data. What effects would there be if the class width is doubled, which means the first class will be "$4 to less than $8"?

(d) What proportion of stock prices were above $10?

(Note: Use only the original values of share prices and not adjusted values.)

Question 2-

The following table provides the median weekly rents of a 3-bedroom house of a few randomly selected suburbs in four capital cities of Australia - Sydney, Melbourne, Brisbane and Perth - for March 2016. The data is obtained from the website https://www.realestate.com.au/neighbourhoods/. From the data answer the questions below for the capital cities.

(a) Compute the mean, median, first quartile, and third quartile for each capital city (with only the data provided for that city, do not add/delete values for any new/given suburb of question 2) using the exact position, (n+1)f, where n is the number of observations and f the relevant fraction for the quartile.

(b) Compute the standard deviation, range and coefficient of variation from the sample data for each city.

(c) Draw a box and whisker plot for the median weekly rents of each city and put them side by side on the same scale so that the prices can be compared.

(d) Compare the box plots and comment on the distribution of the data.

Question 3-

The Table below is taken from the Australian Bureau of Statistics website. It provides data on energy use of households - almost all houses use mains electricity but some use additional energy sources such as gas and solar. (You can get the data from Table 1 from the URL: https://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/4602.0.55.001Mar%202014?OpenDocument.) Missing cells indicate data not published, and totals may be higher since all possibilities may not be listed. The totals are not incorrect.).

Based on the information available in the table above -

(a) What is the probability that an Australian household, randomly selected, uses solar as a source of energy?

(b) What is the probability that an Australian household, randomly selected, uses mains gas and is located in Victoria?

(c) Given that a household uses LPG/bottled gas, what is the probability that the household is located in South Australia?

(d) Is the percentage of Australian households using mains gas independent of the state?

Question 4-

(a) The following data collected from the Australian Bureau of Meteorology Website (https://www.bom.gov.au/climate/data/?ref=ftr) gives the daily rainfall data for the year 2015 in Brisbane. The zero values indicate no rainfall and the left-most column gives the date. Assuming that the weekly rainfall event (number of days in a week with rainfall) follows a Poisson distribution (There are 52 weeks in a year and a week is assumed to start from Monday. The first week starts from 29 December 2014 - you are expected to visit the website and get the daily values which are not given in the table below. Make sure you put the correct station number. Ignore the last few days of 2015 if it exceeds 52 weeks.):

(i) What is the probability that on any given week in a year there would be no rainfall?

(ii) What is the probability that there will be 2 or more days of rainfall in a week?

(b) Assuming that the weekly total amount of rainfall (in mm) from the data provided in part (a) has a normal distribution, compute the mean and standard deviation of weekly totals.

(i) What is the probability that in a given week there will be between 5 mm and 10 mm of rainfall?

(ii) What is the amount of rainfall if only 13% of the weeks have that amount of rainfall or higher?

Question 5-

The following data is taken from the UCI machine learning data repository (https://archive.ics.uci.edu/ml/datasets/Wine+Quality). It lists a few attributes of red wine, randomly sampled from thousands of bottles, which can be classified as of good, medium and poor quality.

(a) Test for normality of all the variables separately for good wine using normal probability plot.

(b) Construct a 95% confidence interval for each of the variables for good wine.

(d) Check if the means calculated for the medium and poor quality red wines fall within the corresponding confidence intervals of the good quality wine. For those attributes whose means lie outside the confidence interval, the attributes are significant in determining the quality. This assumption is, however, partially compromised if the attribute fails the normality test. Identify the significant and non-significant variables, and comment.

Good quality red wine
*Alcohol*	*Residual sugar*	*Chlorides*	*Total sulfur dioxide*	*Density*	pH	*Sulphates*	*Citric acid*
10	1.2	0.065	21	0.9946	3.39	0.47	0
9.5	2	0.073	18	0.9968	3.36	0.57	0.02
10.5	1.8	0.092	103	0.9969	3.3	0.75	0.56
9.7	2.1	0.066	30	0.9968	3.23	0.73	0.28
9.5	1.9	0.085	35	0.9968	3.38	0.62	0.16
10.5	1.8	0.065	16	0.9962	3.42	0.92	0.16
13	1.2	0.046	93	0.9924	3.57	0.85	0.08
10.3	1.4	0.056	24	0.99695	3.22	0.82	0.47
10.8	2.6	0.095	28	0.9994	3.2	0.77	0.74
10.8	2.6	0.095	28	0.9994	3.2	0.77	0.74
10.5	2.1	0.054	19	0.998	3.31	0.88	0.58
12.2	1.6	0.054	106	0.9927	3.54	0.62	0.04
9.2	2.2	0.075	24	1.00005	3.07	0.84	0.44
9.2	2.2	0.075	24	1.00005	3.07	0.84	0.44
10.5	2.6	0.085	33	0.99965	3.36	0.8	0.47
10.2	1.8	0.071	10	0.9968	3.2	0.72	0.52
12.8	3.6	0.078	37	0.9973	3.35	0.86	0.46
12.6	6.4	0.073	13	0.9976	3.23	0.82	0.45
10.5	5.6	0.087	47	0.9991	3.38	0.77	0.32
9.9	3.5	0.358	10	0.9972	3.25	1.08	0.68
10.5	5.6	0.087	47	0.9991	3.38	0.77	0.32
10.6	2.5	0.091	49	0.9976	3.34	0.86	0.09
10.6	2.5	0.091	49	0.9976	3.34	0.86	0.09
11.5	3.2	0.083	59	0.9989	3.37	0.71	0.39
11.5	3.2	0.083	59	0.9989	3.37	0.71	0.39
11.5	3.65	0.121	14	0.9978	3.05	0.74	0.66
11.7	2.5	0.078	38	0.9963	3.34	0.74	0.01
12.2	3.4	0.128	21	0.9992	3.17	0.84	0.53
9.8	2.3	0.082	29	0.9997	3.11	1.36	0.54
12.3	2.7	0.072	34	0.9955	3.58	0.89	0.02
11.7	2.95	0.116	29	0.997	3.24	0.75	0.66
10.4	3.1	0.109	23	1	3.15	0.85	0.66
10	5.8	0.083	42	1.0022	3.07	0.73	0.66
10	5.8	0.083	42	1.0022	3.07	0.73	0.66
12	2.4	0.074	18	0.9962	3.2	1.13	0.53
11.8	4.4	0.124	15	0.9984	3.01	0.83	0.71
12	2.4	0.074	18	0.9962	3.2	1.13	0.53
10	2.5	0.096	49	0.9982	3.19	0.7	0.31
12.9	1.4	0.045	88	0.9924	3.56	0.82	0.05
13	4.2	0.066	38	1.0004	3.22	0.6	0.76
10.8	3	0.093	30	0.9996	3.18	0.63	0.66
11.7	6.7	0.097	19	0.9986	3.27	0.82	0.53
11.8	2.4	0.089	67	0.9972	3.28	0.73	0.33
12.3	2.3	0.059	48	0.9952	3.52	0.56	0.03
11	2.1	0.066	24	0.9978	3.15	0.9	0.47
12.3	2.3	0.059	48	0.9952	3.52	0.56	0.03
11	2.1	0.066	24	0.9978	3.15	0.9	0.47
9.8	2.2	0.072	29	0.9987	2.88	0.82	0.72
11.2	3.7	0.1	43	1.0032	2.95	0.68	0.76
11.6	2.7	0.077	19	0.9963	3.23	0.63	0.49
12.5	1.7	0.054	27	0.9934	3.57	0.84	0.01
11.2	2.8	0.084	22	0.9998	3.26	0.74	0.63
13.4	5.2	0.086	19	0.9988	3.22	0.69	0.67
11.2	2.8	0.084	22	0.9998	3.26	0.74	0.63
11.7	2.8	0.08	17	0.9964	3.15	0.92	0.56
10.8	2.8	0.081	67	1.0002	3.32	0.92	0.55
13.3	2.5	0.055	25	0.9952	3.34	0.79	0.5
13.4	2.6	0.052	27	0.995	3.32	0.9	0.51
11	2.6	0.07	16	0.9972	3.15	0.65	0.53
11	2.6	0.07	16	0.9972	3.15	0.65	0.53
12	6.55	0.074	76	0.999	3.17	0.85	0.73
12	6.55	0.074	76	0.999	3.17	0.85	0.73
10.9	1.9	0.078	24	0.9976	3.18	1.04	0.47
10.8	1.8	0.077	22	0.9976	3.21	1.05	0.42
12.5	2.9	0.072	26	0.9968	3.16	0.78	0.63
10.8	1.8	0.075	21	0.9976	3.25	1.02	0.46
11.4	2.8	0.084	43	0.9986	3.04	0.68	0.75
11.8	2.4	0.107	15	0.9973	3.09	0.66	0.64
11.8	2.4	0.107	15	0.9973	3.09	0.66	0.64

Medium quality red wine
*alcohol*	*residual sugar*	*chlorides*	*total sulfur dioxide*	*density*	pH	*sulphates*	*citric acid*
9.4	1.9	0.076	34	0.9978	3.51	0.56	0
9.8	2.6	0.098	67	0.9968	3.2	0.68	0
9.8	2.3	0.092	54	0.997	3.26	0.65	0.04
9.4	1.9	0.076	34	0.9978	3.51	0.56	0
9.4	1.8	0.075	40	0.9978	3.51	0.56	0
9.4	1.6	0.069	59	0.9964	3.3	0.46	0.06
10.5	6.1	0.071	102	0.9978	3.35	0.8	0.36
9.2	1.8	0.097	65	0.9959	3.28	0.54	0.08
10.5	6.1	0.071	102	0.9978	3.35	0.8	0.36
9.9	1.6	0.089	59	0.9943	3.58	0.52	0
9.1	1.6	0.114	29	0.9974	3.26	1.56	0.29
9.2	3.8	0.176	145	0.9986	3.16	0.88	0.18
9.2	3.9	0.17	148	0.9986	3.17	0.93	0.19
9.3	1.7	0.368	56	0.9968	3.11	1.28	0.28
9.7	2.3	0.082	71	0.9982	3.52	0.65	0.31
9.5	1.6	0.106	37	0.9966	3.17	0.91	0.21
9.4	2.3	0.084	67	0.9968	3.17	0.53	0.11
9.3	1.4	0.08	23	0.9955	3.34	0.56	0.16
9.5	1.8	0.08	11	0.9962	3.28	0.59	0.24
9.5	1.6	0.106	37	0.9966	3.17	0.91	0.21
9.4	1.9	0.08	35	0.9972	3.47	0.55	0
10.1	2.4	0.089	82	0.9958	3.35	0.54	0.07
9.8	2.3	0.083	113	0.9966	3.17	0.66	0.12
9.2	1.8	0.103	50	0.9957	3.38	0.55	0.25
10.5	5.9	0.074	87	0.9978	3.33	0.83	0.36
10.5	5.9	0.074	87	0.9978	3.33	0.83	0.36
10.3	2.2	0.069	23	0.9968	3.3	1.2	0.22
9.5	1.8	0.05	11	0.9962	3.48	0.52	0.02
9.2	2.2	0.114	114	0.997	3.25	0.73	0.43
9.5	1.6	0.113	37	0.9969	3.25	0.58	0.52
9.2	1.6	0.066	12	0.9958	3.34	0.56	0.23
9.2	1.4	0.074	96	0.9954	3.32	0.58	0.37
9.2	1.7	0.074	23	0.9971	3.15	0.74	0.26
9.4	3	0.081	119	0.997	3.2	0.56	0.36
9.5	3.8	0.084	45	0.9978	3.34	0.53	0.04
9.6	3.4	0.07	10	0.9971	3.04	0.63	0.57
9.4	5.1	0.111	110	0.9983	3.26	0.77	0.12
10	2.3	0.076	54	0.9975	3.43	0.59	0.18
9.2	2.2	0.079	52	0.998	3.44	0.64	0.4
9.3	1.8	0.115	112	0.9968	3.21	0.71	0.49
9.8	2	0.081	54	0.9966	3.39	0.57	0.05
10.9	4.65	0.086	11	0.9962	3.41	0.39	0.05
10.9	4.65	0.086	11	0.9962	3.41	0.39	0.05
9.6	1.5	0.079	39	0.9968	3.42	0.58	0.11
10.7	1.6	0.076	15	0.9962	3.44	0.58	0.07
10.7	2	0.074	65	0.9969	3.28	0.79	0.57
9.5	2.1	0.088	96	0.9962	3.32	0.48	0.23
9.5	1.9	0.084	94	0.9961	3.31	0.48	0.22
9.6	2.5	0.094	83	0.9984	3.28	0.82	0.54
10.5	2.2	0.093	42	0.9986	3.54	0.66	0.64
10.5	2.2	0.093	42	0.9986	3.54	0.66	0.64
10.1	2	0.086	80	0.9958	3.38	0.52	0.12
9.2	1.6	0.069	15	0.9958	3.41	0.56	0.2
9.4	1.9	0.464	67	0.9974	3.13	1.28	0.7
9.1	2	0.086	73	0.997	3.36	0.57	0.47
9.4	1.8	0.401	51	0.9969	3.16	1.14	0.26

Poor quality red wine
*alcohol*	*residual sugar*	*chlorides*	*total sulfur dioxide*	*density*	pH	*sulphates*	*citric acid*
9	2.2	0.074	47	1.0008	3.25	0.57	0.66
8.4	2.1	0.2	16	0.9994	3.16	0.63	0.49
10.7	4.25	0.097	14	0.9966	3.63	0.54	0
9.9	1.5	0.145	48	0.99832	3.38	0.86	0.42
11	3.4	0.084	11	0.99892	3.48	0.49	0.02
10.9	2.1	0.137	9	0.99476	3.5	0.4	0
9.8	1.2	0.267	29	0.99471	3.32	0.51	0
10.2	5.7	0.082	14	0.99808	3.4	0.52	0.05
9.95	1.8	0.078	12	0.996	3.55	0.63	0.02
9	4.4	0.086	29	0.9974	3.38	0.5	0.08
9.8	1.5	0.172	19	0.994	3.5	0.48	0.09
9.3	2.8	0.088	46	0.9976	3.26	0.51	0.3
13.1	2.1	0.054	65	0.9934	3.9	0.56	0.15
9.2	2.1	0.084	43	0.9976	3.31	0.53	0.26
9.1	1.5	0.08	119	0.9972	3.16	1.12	0.2
10.5	1.4	0.045	85	0.9938	3.75	0.48	0.04
9.4	3.4	0.61	69	0.9996	2.74	2	1
9.2	1.3	0.072	20	0.9965	3.17	1.08	0.02
9	1.6	0.072	42	0.9956	3.37	0.48	0.03
9.1	1.8	0.058	8	0.9972	3.36	0.33	0.03
11.4	2.1	0.061	31	0.9948	3.51	0.43	0.06
10.4	2	0.089	55	0.99745	3.31	0.57	0.36
9.4	2	0.087	67	0.99565	3.35	0.6	0.04
9.8	3.3	0.096	61	1.00025	3.6	0.72	0
9.6	4.5	0.07	49	0.9981	3.05	0.57	0.49
9.6	2.1	0.07	47	0.9991	3.3	0.56	0.49
10	2.3	0.103	14	0.9978	3.34	0.52	0.24
10	2.1	0.088	23	0.9962	3.26	0.47	0.27
11.3	3.4	0.105	86	1.001	3.43	0.64	0.22
11	2.2	0.07	14	0.9967	3.32	0.58	0.01
11	4.4	0.096	13	0.997	3.41	0.57	0.02
9.6	2.6	0.073	84	0.9972	3.32	0.7	0.48
9.7	1.6	0.078	14	0.998	3.29	0.54	0.04
11.2	3.1	0.086	12	0.9958	3.54	0.6	0.1
11.4	2.1	0.102	7	0.99462	3.44	0.58	0.24
10.9	2.5	0.058	9	0.99632	3.38	0.55	0.07
9.9	1.6	0.147	51	0.99836	3.38	0.86	0.44

Reference no: EM131049197

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