FINANCE CALCULATION - An Australian Manufacturing Company Sanitary Product.

An Australian manufacturing company which is a market leader in the sanitary product sector

in the Australasian market with operating headquarters in Sydney is keen to develop new

products so that company can expand into Australian and Asian markets. Research and

Development (R&D) department of the company has prepared a proposal to work on new

product line of fragrance.

Assume you work as an Analyst for this company and you have been asked to study the

market for fragrances. You have been asked to study the characteristics of currently available

fragrances so the company will be in a better position to price its proposed new product. You

have collected data for 95 products on the market. The data is on an excel file named

“FRAGRANCE”.

First Column: PRICE (that is Price of the product in Australian dollars for 100 ml of a

fragrance).

Second Column: GENDER (1 for Female product and 2 for Male product).

Third Column: TYPE (Type of fragrance: 1 for perfume and 2 for cologne).

Forth Column: MADE (the country in which the fragrance is produced: 1 made in France, 2

made in USA, 3 made in other countries).

Fifth Column: INTEN (Intensity or strength of the product: 1 for strong, 2 for medium and 3

for mild).

See next pages for more information. Questions:

Part 1) Provide a description of the data (for all the 95 products together) and show what

Mean, Standard Deviation, Coefficient of Variation mean in this context.

Part 2) Calculate the Mean, Median, Mode, Standard Deviation, Coefficient of Variation,

95% confidence interval for means for:

o Prices for gender - male and female.

o Prices for fragrance - perfume and cologne.

o Prices for intensity - strong, medium and mild.

o Prices for different countries – France, USA and other.

What conclusions can you draw from these analyses? Also comment on the shape of

distributions.

Part 3) You are also required to answer the following questions (For one-sided tests, use the

question of interest as the alternative hypothesis):

1. Determine if average prices for female products exceed average prices for male

products.

2. Determine if there is evidence to suggest that the variance of prices for male products

is equal to those of female products.

3. Determine if average prices for perfume exceeds the average price of cologne.

4. Determine if there is evidence to suggest that the variance of prices for perfume and

cologne are equals.

5. Determine if there is a difference in the prices for intensity.

6. Determine if there is a difference for the prices for different countries.

Give conclusions for each of the analysis plus an overall conclusion which combine all of the

above (parts 1 and 2). Be clear in the conclusion that you draw from your analysis, and

provide some useful suggestions to the company’s manager.

Notes:

v You must follow the hypothesis testing steps to test the above hypotheses. v You must clearly specify all hypotheses and the chosen test procedures.

v Use 0.05 level of significance in your analyses and assume that we have normal

distributions and also “equal variances” of populations.

v Each part requires an output from any statistical computer package (Excel, SPSS, R etc.).

The statistical output must be provided.

v Use and interpret appropriate output from statistical computer packages such as

SPSS/Minitab/Excel/PHStat to conduct hypothesis testing.

v Prepare and produce a business report based on the information provided in the

“Presentation of the assignment”.

v You report can be maximum 3000 words, penalties will be applied if the report is longer

than this.

v Submit the business report as indicated in the “Presentation of the assignment” BEFORE

4pm, 17 April 2014 at the Business Central.

v No assignment will be accepted after 4pm, 17 April 2014.

v Take your assignment to the Business Central to be scanned and submitted

v Please ensure that you have signed the plagiarism declaration before submitting your

assignment for scanning at Business Central.

Executive summary

In this business report the analyst analyze the market about the fragrances. To analyze the market the analyst have taken different kind of variable such as the price of the product, type of the fragrance, the effective gender, the country where the product made and the intensity and the strength of the product.

The analyst use different kind of statistical tools to analyze the market of fragrances. The important statistical tools are mean, median, mode, standard deviation and correlation coefficient.

 As per the analysis, the analyzer observes that the value of mean of price is 29 and the value of median of price of the fragrance is 124. The value of the mode of the product is 69 and the value of the standard deviation is 79.64. 

  

IMPORTANT:

It is compulsory for all students to submit their assignments (only final version) into

the Turnitin system BEFORE submitting the hard copy to the Business Central. Then

an 'originality report' will automatically be sent to the Lecturer (Dr Amir Arjomandi).

Otherwise your assignment will not be marked and therefore you will be awarded a 0

for this assessment task.

• You must submit your assignment to Turnitin before the due date (4pm, 15 April

2014). No assignment will be accepted by Turnitin after 4pm, 15 April 2014.

• The first time a UOW student uses the Turnitin system, they must register using a

functioning UOW email address as their user name and adhere to the following

guidelines:

1. Use one sign in name only

2. Use one document name only for each assignment that includes your UOW student

number 3. Any resubmissions must use the same document name as the original submission

4. References must be included in your Turnitin submission

5. Do not include the assignment topic question at the beginning of your submission.

• Failure to comply with these requirements may result in penalties being applied.

-------------------------------------------------------------

Introduction

An Australian manufacturing company which is a market leader in the sanitary sector in the Australian market with operating headquarters in the Sydney which is keen to develop new products so that company can expand into the Australian and Asian markets. Research and development of this company is going to prepare a proposal to work a new product line in the fragrance. In this project the analyst study the market for fragrance. In this project, the analyst has collected data for 95 products on the market. The analyst has to study the characteristics of the currently available fragrance so the company will be in a better position to price its proposed new product.

The terms of mean median mode and the range that describes properties of the statistical distribution. In the method of statistics, a distribution is one set of possible values which represent defined the events (Atkinson, 2008). Random variable can be expressed as a variable. There are two major type of statistical distribution. The effective type has a discrete random variable. It express hat every term is precise and isolated numerical value (Dimarco, 2008). One of the distributions of discrete random variables is that a set of result for the test taken by the class. Another major type of distribution is a continuous random variable (Comaniciu and Meer, 2002). In that situation, a term has to acquire any within an unbroken interval. This is one of the functions which can be used by a computer in an attempt which forecast the path of a weather system (Lee et al. 2011).       

Mean

One of the most common expressions for the mean of statistical distribution with the discrete random variable is the mathematical average of every term. When the analyst calculates the mean then they have to add the values of all the terms and they have to divide the number of terms (Evanovich, 2007). This method can be called the arithmetic mean. On the other hand, there are other expressions for the mean that a finite set of terms but in mathematics these form are rarely used in the statistical calculation (Comaniciu et al. 2000). The proper mean of the statistical distribution with a following random variables and this can also called the expected value that can be obtained by the integrating the product of the variable with its probability as defined by the effective distribution. The value of mean can be representing by the lower case Greek letter mu (Hughes and Jagannathan, 2009).

Median

Median is another method of dispersion with the discrete random variables which depends on whether the number of the term as per the distribution. When the number of term is odd, then the median is the value of the term in the middle (American Mathematical Society, 2014). When the term of this variable is even then the median is the average of the two terms in the middle which that the numbers of terms have the value greater than or equal to as it is the same as the number of term has the value less than or equal to it (Lenn and Stefano, 2012). On the other hand, the median of the distribution as a continuous random variables is one of the value m such that the probability is at least 50 per cent that a random chosen point on the function that will be less than or equal to the m and on the other aspect the probability is at least ½ that of the random chosen in the point on the function that will be greater than or equal to the m (Arxiv.org, 2014).

Mode

Mode is another aspect of dispersion. As per the mode it is common for a distribution with the discrete random variable that has to be more than one mode, in this case there are not many terms (Fagam et al. 2008). This kind of incident happens at the time when two or more terms occur with the equal frequency. A distribution of the two modes can be known as bimodal. On the other hand, a distribution of three modes can be called as tri modal (Schwarz and Yenmez, 2009). The mode of the distribution as per the continuous random variables is the maximum value of the function (Math.berkeley.edu, 2014). There may more than one mode with discrete distribution.         

Standard deviation

Standard deviation is the measure of dispersion of the set of data for the mean. When the data is more spread the there is a chance of higher deviation. This method is calculated as the square root of the variance (Weissman, 2007). As per the part in finance, standard deviations have to be applied to the annual rate of return of an investment which measures the volatility of the investment (Pagel and Hudetz, 2011). The standard deviation can be known as the historical volatility and which is used by the investors as per the gauge for the amount of expected volatility. When the analyst have to look at the peoples typical daily consumption then number of people typical consumption have to turn out to be normally distributed. The consumption will be close to the mean. The x axis is the value of question which calories consumed, dollars earned or the crimes committed. On the other hand, y axis is the number of data points for every value of x axis.

 Standard deviation can be defined as the mean of the mean. This helps the analyst to find the story behind the data. This helps to understand the concept of normal distribution. A normal distribution of the certain data means that the most of the in the set of data which is close to the average, on the other hand, relatively few example tend to one extreme (Lee et al.  2008).

               

Coefficient of Variation

The correlation coefficient of the two variables in the data sample can be defined as the covariance divided by the product of the individual’s standard of the variation. This can be known as normalized measurement of how they are linearly related. A correlation coefficient is one of the statistical measures of the certain degree of which change to the value of one variable can predict the change to the other variable (Lazaridis, 2005). The negatively correlated value defines that when the value of one variable increase then the value of other variable decrease. The value of r can be defined as the liner correlation coefficient that helps to determine the strength and the direction of the liner relationship between the different variables. The linear correlation can be defined as the Pearson product moment correlation coefficient (Math.mit.edu, 2014). The effective value of r can be -1, +1 or 0 which is used for positive liner correlation and negative liner correlation respectively.

Positive correlation

Positive correlation can be representing by the value of +1. The value of the positive 1 can indicates the perfect positive fit. This value also indicates the relationship between x and y variable. In this case, when the value of x increases then the value of y also increases (Siddiqui and Siddiqui, 2006).

Negative correlation

Negative correlation indicates that the value of -1. The value of negative 1 represent the perfect negative fit. The negative value indicates the negative relationship such that when the value of x increase then the value of y decrease (Madura, 2011).

No correlation

No correlation indicates the weak linear correlation. In this case the value of r is close to 0. A value of zero means that there is no relation between two variables. That indicates that if the value of x increase then the value of y may not change (Math.umn.edu, 2014).

Calculation

Mean

Mean	Median	Mode	Standard deviation
Price
29	124	69	79.64251

Price and gender

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.138716
R Square	0.019242
Adjusted R Square	0.008582
Standard Error	78.53473
Observations	94
ANOVA
	Df	SS	MS	F	Significance F
Regression	1	11132.73	11132.73	1.805004	0.182413192
Residual	92	567428.7	6167.703
Total	93	578561.4
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	164.2359	24.37366	6.738255	1.37E-09	115.8277393	212.6441	115.8277	212.6441
1	-23.7965	17.71229	-1.3435	0.182413	-58.97466951	11.3816	-58.9747	11.3816

The residual output of above calculation will be given below         

RESIDUAL OUTPUT
Observation	Predicted 267	Residuals
1	140.4394	76.56061
2	140.4394	106.5606
3	140.4394	106.5606
4	140.4394	138.5606
5	140.4394	116.5606
6	140.4394	136.5606
7	140.4394	126.5606
8	140.4394	164.5606
9	140.4394	136.5606
10	140.4394	106.5606
11	140.4394	146.5606
12	140.4394	166.5606
13	140.4394	66.56061
14	140.4394	66.56061
15	140.4394	66.56061
16	140.4394	124.5606
17	116.6429	68.35714
18	116.6429	68.35714
19	116.6429	66.35714
20	140.4394	58.56061
21	140.4394	102.5606
22	140.4394	22.56061
23	140.4394	111.5606
24	140.4394	82.56061
25	140.4394	72.56061
26	140.4394	42.56061
27	140.4394	32.56061
28	140.4394	2.560606
29	116.6429	76.35714
30	116.6429	66.35714
31	140.4394	-1.43939
32	140.4394	2.560606
33	140.4394	22.56061
34	116.6429	16.35714
35	116.6429	6.357143
36	140.4394	-7.43939
37	140.4394	-67.4394
38	140.4394	-57.4394
39	140.4394	18.56061
40	140.4394	47.56061
41	116.6429	0.357143
42	116.6429	-5.64286
43	116.6429	60.35714
44	140.4394	-1.43939
45	140.4394	-51.4394
46	116.6429	-19.6429
47	116.6429	-47.6429
48	116.6429	-59.6429
49	116.6429	18.35714
50	116.6429	40.35714
51	116.6429	42.35714
52	116.6429	2.357143
53	140.4394	-101.439
54	140.4394	-21.4394
55	116.6429	-47.6429
56	140.4394	-71.4394
57	116.6429	-87.6429
58	116.6429	-47.6429
59	140.4394	-75.4394
60	140.4394	-36.4394
61	140.4394	-89.4394
62	140.4394	-89.4394
63	140.4394	-97.4394
64	116.6429	-87.6429
65	140.4394	-65.4394
66	140.4394	-71.4394
67	140.4394	-85.4394
68	140.4394	-85.4394
69	140.4394	-85.4394
70	140.4394	-99.4394
71	140.4394	-91.4394
72	140.4394	-89.4394
73	140.4394	-95.4394
74	140.4394	-75.4394
75	140.4394	-75.4394
76	140.4394	-97.4394
77	140.4394	-97.4394
78	116.6429	-51.6429
79	116.6429	-80.6429
80	116.6429	-0.64286
81	116.6429	-42.6429
82	140.4394	-94.4394
83	140.4394	-14.4394
84	140.4394	-74.4394
85	140.4394	-92.4394
86	140.4394	-102.439
87	140.4394	-106.439
88	140.4394	-104.439
89	116.6429	-50.6429
90	116.6429	58.35714
91	116.6429	38.35714
92	140.4394	-28.4394
93	140.4394	45.56061
94	140.4394	-16.4394

Price and type

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.63254
R Square	0.400107
Adjusted R Square	0.393586
Standard Error	61.42111
Observations	94
ANOVA
	Df	SS	MS	F	Significance F
Regression	1	231486.5	231486.5	61.3607	7.98903E-12
Residual	92	347074.9	3772.553
Total	93	578561.4
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	287.0064	20.61327	13.92338	2.25E-24	246.0666549	327.9461	246.0667	327.9461
1	-99.611	12.71635	-7.83331	7.99E-12	-124.8667935	-74.3553	-124.867	-74.3553

The residual output of above calculation will be given below         

RESIDUAL OUTPUT
Observation	Predicted 267	Residuals
1	187.3953	29.60465
2	187.3953	59.60465
3	187.3953	59.60465
4	187.3953	91.60465
5	187.3953	69.60465
6	187.3953	89.60465
7	187.3953	79.60465
8	187.3953	117.6047
9	187.3953	89.60465
10	187.3953	59.60465
11	187.3953	99.60465
12	187.3953	119.6047
13	187.3953	19.60465
14	187.3953	19.60465
15	187.3953	19.60465
16	187.3953	77.60465
17	187.3953	-2.39535
18	87.78431	97.21569
19	87.78431	95.21569
20	187.3953	11.60465
21	187.3953	55.60465
22	187.3953	-24.3953
23	187.3953	64.60465
24	187.3953	35.60465
25	187.3953	25.60465
26	187.3953	-4.39535
27	187.3953	-14.3953
28	187.3953	-44.3953
29	87.78431	105.2157
30	87.78431	95.21569
31	187.3953	-48.3953
32	187.3953	-44.3953
33	87.78431	75.21569
34	87.78431	45.21569
35	87.78431	35.21569
36	187.3953	-54.3953
37	187.3953	-114.395
38	187.3953	-104.395
39	187.3953	-28.3953
40	187.3953	0.604651
41	187.3953	-70.3953
42	187.3953	-76.3953
43	187.3953	-10.3953
44	87.78431	51.21569
45	87.78431	1.215686
46	87.78431	9.215686
47	87.78431	-18.7843
48	87.78431	-30.7843
49	87.78431	47.21569
50	87.78431	69.21569
51	87.78431	71.21569
52	187.3953	-68.3953
53	87.78431	-48.7843
54	87.78431	31.21569
55	87.78431	-18.7843
56	187.3953	-118.395
57	187.3953	-158.395
58	187.3953	-118.395
59	87.78431	-22.7843
60	87.78431	16.21569
61	87.78431	-36.7843
62	87.78431	-36.7843
63	87.78431	-44.7843
64	87.78431	-58.7843
65	87.78431	-12.7843
66	87.78431	-18.7843
67	87.78431	-32.7843
68	87.78431	-32.7843
69	87.78431	-32.7843
70	87.78431	-46.7843
71	87.78431	-38.7843
72	87.78431	-36.7843
73	87.78431	-42.7843
74	87.78431	-22.7843
75	87.78431	-22.7843
76	87.78431	-44.7843
77	87.78431	-44.7843
78	87.78431	-22.7843
79	87.78431	-51.7843
80	87.78431	28.21569
81	187.3953	-113.395
82	87.78431	-41.7843
83	87.78431	38.21569
84	87.78431	-21.7843
85	87.78431	-39.7843
86	87.78431	-49.7843
87	87.78431	-53.7843
88	87.78431	-51.7843
89	87.78431	-21.7843
90	87.78431	87.21569
91	87.78431	67.21569
92	187.3953	-75.3953
93	187.3953	-1.39535
94	87.78431	36.21569

Price and country

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.058164
R Square	0.003383
Adjusted R Square	-0.00745
Standard Error	79.16714
Observations	94
ANOVA
	Df	SS	MS	F	Significance F
Regression	1	1957.318	1957.318	0.3123	0.57763
Residual	92	576604.1	6267.436
Total	93	578561.4
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	146.003	24.06721	6.066469	2.87E-08	98.20342	193.8025	98.20342	193.8025
1	-6.25937	11.20069	-0.55884	0.57763	-28.5049	15.98617	-28.5049	15.98617

The residual output of above calculation will be given below         

RESIDUAL OUTPUT
Observation	Predicted 267	Residuals
1	139.7436	77.25639
2	139.7436	107.2564
3	139.7436	107.2564
4	139.7436	139.2564
5	139.7436	117.2564
6	139.7436	137.2564
7	139.7436	127.2564
8	133.4842	171.5158
9	133.4842	143.5158
10	133.4842	113.5158
11	133.4842	153.5158
12	133.4842	173.5158
13	133.4842	73.51576
14	133.4842	73.51576
15	133.4842	73.51576
16	133.4842	131.5158
17	133.4842	51.51576
18	133.4842	51.51576
19	133.4842	49.51576
20	127.2249	71.77513
21	127.2249	115.7751
22	127.2249	35.77513
23	127.2249	124.7751
24	127.2249	95.77513
25	127.2249	85.77513
26	127.2249	55.77513
27	127.2249	45.77513
28	127.2249	15.77513
29	127.2249	65.77513
30	127.2249	55.77513
31	139.7436	-0.74361
32	139.7436	3.256388
33	139.7436	23.25639
34	139.7436	-6.74361
35	139.7436	-16.7436
36	133.4842	-0.48424
37	133.4842	-60.4842
38	133.4842	-50.4842
39	133.4842	25.51576
40	133.4842	54.51576
41	133.4842	-16.4842
42	133.4842	-22.4842
43	133.4842	43.51576
44	133.4842	5.515758
45	133.4842	-44.4842
46	133.4842	-36.4842
47	133.4842	-64.4842
48	133.4842	-76.4842
49	133.4842	1.515758
50	133.4842	23.51576
51	133.4842	25.51576
52	127.2249	-8.22487
53	127.2249	-88.2249
54	127.2249	-8.22487
55	127.2249	-58.2249
56	139.7436	-70.7436
57	139.7436	-110.744
58	139.7436	-70.7436
59	139.7436	-74.7436
60	139.7436	-35.7436
61	139.7436	-88.7436
62	139.7436	-88.7436
63	139.7436	-96.7436
64	139.7436	-110.744
65	133.4842	-58.4842
66	133.4842	-64.4842
67	133.4842	-78.4842
68	133.4842	-78.4842
69	133.4842	-78.4842
70	133.4842	-92.4842
71	133.4842	-84.4842
72	133.4842	-82.4842
73	133.4842	-88.4842
74	133.4842	-68.4842
75	133.4842	-68.4842
76	133.4842	-90.4842
77	133.4842	-90.4842
78	133.4842	-68.4842
79	133.4842	-97.4842
80	133.4842	-17.4842
81	127.2249	-53.2249
82	127.2249	-81.2249
83	127.2249	-1.22487
84	127.2249	-61.2249
85	127.2249	-79.2249
86	127.2249	-89.2249
87	127.2249	-93.2249
88	127.2249	-91.2249
89	127.2249	-61.2249
90	127.2249	47.77513
91	127.2249	27.77513
92	139.7436	-27.7436
93	139.7436	46.25639
94	139.7436	-15.7436

Price and intensity

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.881551
R Square	0.777131
Adjusted R Square	0.774709
Standard Error	37.43736
Observations	94
ANOVA
	Df	SS	MS	F	Significance F
Regression	1	449618.2	449618.2	320.7993	9.61E-32
Residual	92	128943.2	1401.556
Total	93	578561.4
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	300.2355	10.08592	29.76777	5.2E-49	280.204	320.267	280.204	320.267
1	-82.5639	4.609707	-17.9109	9.61E-32	-91.7191	-73.4086	-91.7191	-73.4086

The residual output of above calculation will be given below         

RESIDUAL OUTPUT
Observation	Predicted 267	Residuals
1	217.6716	-0.67161
2	217.6716	29.32839
3	217.6716	29.32839
4	217.6716	61.32839
5	217.6716	39.32839
6	217.6716	59.32839
7	217.6716	49.32839
8	217.6716	87.32839
9	217.6716	59.32839
10	217.6716	29.32839
11	217.6716	69.32839
12	217.6716	89.32839
13	217.6716	-10.6716
14	217.6716	-10.6716
15	217.6716	-10.6716
16	217.6716	47.32839
17	217.6716	-32.6716
18	217.6716	-32.6716
19	217.6716	-34.6716
20	217.6716	-18.6716
21	217.6716	25.32839
22	217.6716	-54.6716
23	217.6716	34.32839
24	217.6716	5.328387
25	217.6716	-4.67161
26	217.6716	-34.6716
27	217.6716	-44.6716
28	217.6716	-74.6716
29	217.6716	-24.6716
30	217.6716	-34.6716
31	135.1077	3.892258
32	135.1077	7.892258
33	135.1077	27.89226
34	135.1077	-2.10774
35	135.1077	-12.1077
36	135.1077	-2.10774
37	135.1077	-62.1077
38	135.1077	-52.1077
39	135.1077	23.89226
40	135.1077	52.89226
41	135.1077	-18.1077
42	135.1077	-24.1077
43	135.1077	41.89226
44	135.1077	3.892258
45	135.1077	-46.1077
46	135.1077	-38.1077
47	135.1077	-66.1077
48	135.1077	-78.1077
49	135.1077	-0.10774
50	135.1077	21.89226
51	135.1077	23.89226
52	135.1077	-16.1077
53	135.1077	-96.1077
54	135.1077	-16.1077
55	135.1077	-66.1077
56	52.54387	16.45613
57	52.54387	-23.5439
58	52.54387	16.45613
59	52.54387	12.45613
60	52.54387	51.45613
61	52.54387	-1.54387
62	52.54387	-1.54387
63	52.54387	-9.54387
64	52.54387	-23.5439
65	52.54387	22.45613
66	52.54387	16.45613
67	52.54387	2.456129
68	52.54387	2.456129
69	52.54387	2.456129
70	52.54387	-11.5439
71	52.54387	-3.54387
72	52.54387	-1.54387
73	52.54387	-7.54387
74	52.54387	12.45613
75	52.54387	12.45613
76	52.54387	-9.54387
77	52.54387	-9.54387
78	52.54387	12.45613
79	52.54387	-16.5439
80	52.54387	63.45613
81	52.54387	21.45613
82	52.54387	-6.54387
83	52.54387	73.45613
84	52.54387	13.45613
85	52.54387	-4.54387
86	52.54387	-14.5439
87	52.54387	-18.5439
88	52.54387	-16.5439
89	52.54387	13.45613
90	217.6716	-42.6716
91	217.6716	-62.6716
92	135.1077	-23.1077
93	135.1077	50.89226
94	135.1077	-11.1077

Conclusion

From the calculation it is observed that the value of mean will be 29 and the value of the median will be 124. On the other hand, it is observed that the value of the mode will be 69 and the value of the standard deviation will be 79.64251.

Price and gender

From the above calculation it is observe that the value of R square is .0192 which is not good fit. 1 per cent of the variation in price is explained by the gender. Here the significance F is .1824. Here, the regression line is in negative slope.

Price and type

From the calculation it is observed that, the value of R square will be .400. It indicates that there is good fit between price of the products and products type. Here, the value of significance F is 7.989.

Price and country

From the calculation it is observed that the value of R square will be .0033 which indicates there is no good fit. Here, the value of significance F will be .57763. The price of the product may not dependent by the area where it is produced.  

Price and intensity

From the above calculation, it is observed that the value of the R square will be .777 which is the good fit. It indicates 77 per cent of variation in price is explained by the variables of intensity. Here, the significance F is 9.61.