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Visual presentation of the prices based on product from different brands
Price of Calvin Klein 
Price of Zara 
Price of Tommy Hilfiger 
Price of Ralph Lauren 
94.27 
47.53 
60.73 
63.77 
70.71 
48.87 
87.92 
97.55 
60.48 
58.19 
47.07 
64.47 
61.83 
32.64 
96.78 
149.02 
73.78 
54.35 
83.47 
29.53 
82.79 
51.75 
92.30 
95.28 
65.26 
75.09 
35.89 
131.19 
48.48 
61.67 
118.92 
54.59 
78.00 
31.28 
98.56 
100.66 
81.44 
42.92 
72.89 
111.94 
128.17 
72.02 
49.06 
32.78 
31.00 
28.04 
14.38 
80.25 
37.42 
60.34 
79.39 
89.56 
64.86 
59.32 
130.20 
115.84 
63.02 
34.98 
41.18 
22.08 
84.65 
56.49 
47.57 
38.35 
84.74 
25.07 
29.02 
41.41 
86.82 
36.22 
44.24 
62.18 
50.59 
70.48 
87.10 
77.11 
41.17 
39.91 
60.49 
45.88 
57.74 
46.55 
72.93 
60.77 
29.09 
48.50 
98.93 
41.05 
63.03 
52.36 
52.82 
94.84 
60.27 
66.86 
123.02 
89.81 
56.75 
38.36 
15.42 
74.94 
44.24 
51.88 
56.38 
73.80 
109.63 
47.16 
103.36 
60.55 
101.15 
54.65 
105.25 
44.30 
100.12 
66.34 
86.78 
42.48 
75.55 
73.45 
124.03 
59.07 
Charts
One of the fundamental aspects in representing the data output on prices is through distribution. The price distribution arises because the rates will not yield the same at every time due to various macro and micro economic factors. Therefore, there exist a scattering of values around central tendency. The location, shape and variability of the distributions can be analysed using the following tools. (Freedman, 2010)
Location – Mean & median; Spread – Variance, Standard deviation and range and Shape – Skewness and Kurtosis
The descriptive statistics of prices

Calvin Klein 




Location 
Mean 
69.56889 

Median 
65.05888 
Spread 
Standard Deviation 
23.37837 

Sample Variance 
546.5483 

Range 
99.08802 
Shape 
Kurtosis 
0.111549 

Skewness 
0.401199 

Minimum 
29.08674 

Maximum 
128.1748 

Count 
30 

Zara 




Location 
Mean 
51.10847 

Median 
51.8144 
Spread 
Standard Deviation 
13.86985 

Sample Variance 
192.3726 

Range 
50.02136 
Shape 
Kurtosis 
0.77666 

Skewness 
0.05338 

Minimum 
25.06978 

Maximum 
75.09114 

Count 
30 

Tommy Hilfiger 




Location 
Mean 
73.86924 

Median 
76.16283 
Spread 
Standard Deviation 
32.29041 

Sample Variance 
1042.671 

Range 
115.8186 
Shape 
Kurtosis 
0.86718 

Skewness 
0.06482 

Minimum 
14.38371 

Maximum 
130.2023 

Count 
30 

Ralph Lauren 




Location 
Mean 
71.50168 

Median 
64.12269 
Spread 
Standard Deviation 
31.26372 

Sample Variance 
977.4204 

Range 
126.9439 
Shape 
Kurtosis 
0.07787 

Skewness 
0.600018 

Minimum 
22.07529 

Maximum 
149.0192 

Count 
30 
From the above descriptive analysis it is noted that the mean and median value of Calvin Klein is different, the mean states the average of the distribution whereas the median states the 50 percentile of the data or the mid value. However, it is noted that mean and median of Zara is almost same at 51, however Tommy Hilfiger and Ralph Lauren is also have a difference between the mean and median
While measuring the spread of the data, the standard deviation of Calvin Klein, Tommy Hilfiger and Ralph Lauren has highest value, this shows that the deviation of the values from the mean is huge. Therefore a large standard deviation means the observed value has a huge difference when compared with mean. Therefore the goal should be less standard deviation which is noted in Zara. Therefore, it can be stated that the mean value of Zara has more meaningful information . (Triola, 2014)
In order to analyse the shape of the data, skewness and kurtosis is applied, skewness will state the symmetry of the data. A data to be normally distributed should possess the value of skewness as 0. Similarly Kurtosis is used to measure the combined size of two tails of information, for a data to be normally distributed should have value equal to 3. In the descriptive analysis it is noted that Zara and Tommy Hilfiger has a skewness value almost 0, whereas Calvin Klein and Ralph Lauren have values more than 0, therefore these two products are asymmetrical and the prices may not have normal distribution.
Visual representation

Business Style 




Location 
Mean 
89.14395 

Median 
87.36749 
Spread 
Standard Deviation 
22.28638 

Sample Variance 
496.6829 

Range 
73.71693 
Shape 
Kurtosis 
0.94622 

Skewness 
0.274778 

Minimum 
56.48535 

Maximum 
130.2023 

Count 
30 

Sports style 




Location 
Mean 
64.06367 

Median 
61.19693 
Spread 
Standard Deviation 
15.32209 

Sample Variance 
234.7665 

Range 
62.71523 
Shape 
Kurtosis 
0.6026 

Skewness 
0.34497 

Minimum 
36.21763 

Maximum 
98.93286 

Count 
30 

Casual style 




Location 
Mean 
41.33898 

Median 
41.17424 
Spread 
Standard Deviation 
13.02517 

Sample Variance 
169.655 

Range 
50.87537 
Shape 
Kurtosis 
0.36824 

Skewness 
0.11966 

Minimum 
14.38371 

Maximum 
65.25908 

Count 
30 
From the above descriptive analysis it is noted that the mean and median value of Business style and Sports style wears are different, the mean states the average of the distribution whereas the median states the 50 percentile of the data or the mid value. However, it is noted that mean and median of Casual style wear is almost same at 41. Therefore the mean and median is closely related for Casual style wear. (Boslaugh, 2012)
While measuring the spread of the data, the standard deviation of Business style has highest value with 22.28, this shows that the deviation of the values from the mean is huge. Therefore a large standard deviation means the observed value has a huge difference when compared with mean. Therefore the goal should be less standard deviation which is noted in Sports style and Casual style with SD of 15.32 and 13.02 respectively. Therefore, it can be stated that the mean value of Business style and Casual style has more meaningful information .
In order to analyse the shape of the data, skewness and kurtosis is applied, skewness will state the symmetry of the data. A data to be normally distributed should possess the value of skewness as 0. Similarly Kurtosis is used to measure the combined size of two tails of information, for a data to be normally distributed should have value equal to 3. In the descriptive analysis it is noted that Casual style has a skewness value almost 0, whereas Business style and Sports style have values more than 0, therefore these two style are asymmetrical and the prices may not have normal distribution.
Zara business 
Zara sports 
Zara casual 
58.19 
54.35 
47.53 
75.09 
51.75 
48.87 
72.02 
61.67 
32.64 
60.34 
42.92 
31.28 
59.32 
36.22 
28.04 
56.49 
48.50 
34.98 
70.48 
52.36 
25.07 
66.86 
51.88 
39.91 
66.34 
47.16 
46.55 
73.45 
54.65 
38.36 

Zara business style 




Location 
Mean 
65.85872 

Median 
66.60369 
Spread 
Standard Deviation 
6.859775 

Sample Variance 
47.05652 

Range 
18.60579 
Shape 
Kurtosis 
1.73364 

Skewness 
0.07044 

Minimum 
56.48535 

Maximum 
75.09114 

Count 
10 

Zara sports style 




Location 
Mean 
50.14484 

Median 
51.8144 
Spread 
Standard Deviation 
6.984534 

Sample Variance 
48.78372 

Range 
25.45067 
Shape 
Kurtosis 
1.056389 

Skewness 
0.56811 

Minimum 
36.21763 

Maximum 
61.6683 

Count 
10 

Zara casual style 




Location 
Mean 
37.32185 

Median 
36.6685 
Spread 
Standard Deviation 
8.370247 

Sample Variance 
70.06104 

Range 
23.8008 
Shape 
Kurtosis 
1.32792 

Skewness 
0.102239 

Minimum 
25.06978 

Maximum 
48.87058 

Count 
10 
From the above descriptive analysis it is noted that the mean and median value of all style of products of Zara looks same, the mean states the average of the distribution whereas the median states the 50 percentile of the data or the mid value. However, it is noted that mean and median of all products of Zara is almost same.
The goal should be less standard deviation which is noted in Business style, Sports style and Casual style of Zara products are less. Therefore, it can be stated that the mean value of all products are closely related to the mean value.
In order to analyse the shape of the data, skewness and kurtosis is applied, skewness will state the symmetry of the data. A data to be normally distributed should possess the value of skewness as 0. Similarly Kurtosis is used to measure the combined size of two tails of information, for a data to be normally distributed should have value equal to 3. In the descriptive analysis it is noted that business style has a skewness value almost 0, whereas sports style and Sports style have values more than 0, therefore these two style are asymmetrical and the prices may not have normal distribution. (Witte, 2010)
A ttest is one of the statistical test which helps the researcher to determine whether the two set of data is significantly different from each other. A t test is one of the commonly implemented test analysis, which will follow the steps of the normal distribution. It should be noted that when the scaling term is unknown and it is replaced by an estimate which is available on the data then we can follow students ttest. (Sincich, 2012)
The first step is to formulate the hypothesis, the null hypothesis will state that there mean difference is the same or to put it in other words, there is no significant difference between the variables. The alternate hypothesis will be stated as there is a significant difference among the variables. The interpretation of the data can be stated if the test statistic value is greater than the significance value then null hypothesis is rejected and alternate hypothesis is accepted. (Freedman, 2010)
In the given problem, the objective is to test if there is a significant difference in average prices across these two gender targets. Therefore the following hypothesis is framed.
Null hypothesis: There is no significant difference between average prices and gender
Alternate hypothesis: There is a significant difference between average prices and gender
tTest: TwoSample Assuming Unequal Variances 








Price 
Gender 

Mean 
66.51207 
1.5 

Variance 
754.4582 
0.252101 

Observations 
120 
120 

Hypothesized Mean Difference 
0 


df 
119 


t Stat 
25.92355 


P(T<=t) onetail 
0.004 


t Critical onetail 
1.657759 


P(T<=t) twotail 
0.008 


t Critical twotail 
1.9801 


From the above ttest analysis it can be noted that mean value of price is 66.51 and the gender is 1.5, the variance of prices are high when compared with the gender.
It is further noted that t statistic is 25.92 whereas the p value is 0.004, this shows that the mean of values between price changes and gender are same. Therefore, null hypothesis is accepted. Hence it is stated that there is no significant difference between average prices and gender.
A ttest is one of the statistical test which helps the researcher to determine whether the two set of data is significantly different from each other. A t test is one of the commonly implemented test analysis, which will follow the steps of the normal distribution. It should be noted that when the scaling term is unknown and it is replaced by an estimate which is available on the data then we can follow students ttest.
The first step is to formulate the hypothesis, the null hypothesis will state that there mean difference is the same or to put it in other words, there is no significant difference between the variables. The alternate hypothesis will be stated as there is a significant difference among the variables. The interpretation of the data can be stated if the test statistic value is greater than the significance value then null hypothesis is rejected and alternate hypothesis is accepted.
In the given problem, the objective is to test if there is a significant difference in average prices across brands. Therefore the following hypothesis is framed.
Null hypothesis: There is no significant difference between average prices and brands
Alternate hypothesis: There is a significant difference between average prices and brands
tTest: TwoSample Assuming Unequal Variances 






Price 
Brands 
Mean 
66.27881803 
2.512605042 
Variance 
754.2678226 
1.25195841 
Observations 
119 
119 
Hypothesized Mean Difference 
0 

df 
118 

t Stat 
25.30703038 

P(T<=t) onetail 
0.008 

t Critical onetail 
1.657869523 

P(T<=t) twotail 
0.001 

t Critical twotail 
1.980272226 

From the above ttest analysis it can be noted that mean value of price is 66.27 and the brands is 1.5, the variance of prices are high when compared with the brands.
It is further noted that t statistic is 25.30 whereas the p value is 0.008, this shows that the mean of values between price changes and brands are same. Therefore, null hypothesis is accepted. Hence it is stated that there is no significant difference between average prices and brands.
A ttest is one of the statistical test which helps the researcher to determine whether the two set of data is significantly different from each other. A t test is one of the commonly implemented test analysis, which will follow the steps of the normal distribution. It should be noted that when the scaling term is unknown and it is replaced by an estimate which is available on the data then we can follow students ttest.
The first step is to formulate the hypothesis, the null hypothesis will state that there mean difference is the same or to put it in other words, there is no significant difference between the variables. The alternate hypothesis will be stated as there is a significant difference among the variables. The interpretation of the data can be stated if the test statistic value is greater than the significance value then null hypothesis is rejected and alternate hypothesis is accepted. (Freedman, 2010)
In the given problem, the objective is to test if there is a significant difference in average prices across styles. Therefore the following hypothesis is framed.
Null hypothesis: There is no significant difference between average prices and styles
Alternate hypothesis: There is a significant difference between average prices and styles
tTest: TwoSample Assuming Unequal Variances 






Prices 
Styles 
Mean 
66.27881803 
2.008403361 
Variance 
754.2678226 
0.66942031 
Observations 
119 
119 
Hypothesized Mean Difference 
0 

df 
118 

t Stat 
25.51697319 

P(T<=t) onetail 
0.003 

t Critical onetail 
1.657869523 

P(T<=t) twotail 
0.007 

t Critical twotail 
1.980272226 

From the above ttest analysis it can be noted that mean value of price is 66.27 and the styles is 2.0, the variance of prices are high when compared with the styles.
It is further noted that t statistic is 25.5 whereas the p value is 0.003, this shows that the mean of values between price changes and styles are same. Therefore, null hypothesis is accepted. Hence it is stated that there is no significant difference between average prices and styles.
A ttest is one of the statistical test which helps the researcher to determine whether the two set of data is significantly different from each other. A t test is one of the commonly implemented test analysis, which will follow the steps of the normal distribution. It should be noted that when the scaling term is unknown and it is replaced by an estimate which is available on the data then we can follow students ttest.
The first step is to formulate the hypothesis, the null hypothesis will state that there mean difference is the same or to put it in other words, there is no significant difference between the variables. The alternate hypothesis will be stated as there is a significant difference among the variables. The interpretation of the data can be stated if the test statistic value is greater than the significance value then null hypothesis is rejected and alternate hypothesis is accepted.
In the given problem, the objective is to test if there is a significant difference in Zara average prices across Zara styles. Therefore the following hypothesis is framed.
Null hypothesis: There is no significant difference between Zara average prices and Zara styles
Alternate hypothesis: There is a significant difference between Zara average prices and Zara styles
Zara prices and styles can be grouped as follows
PRICE 
STYLE 
47.53 
3 
48.87 
3 
58.19 
1 
32.64 
3 
54.35 
2 
51.75 
2 
75.09 
1 
61.67 
2 
31.28 
3 
42.92 
2 
72.02 
1 
28.04 
3 
60.34 
1 
59.32 
1 
34.98 
3 
56.49 
1 
25.07 
3 
36.22 
2 
70.48 
1 
39.91 
3 
46.55 
3 
48.50 
2 
52.36 
2 
66.86 
1 
38.36 
3 
51.88 
2 
47.16 
2 
54.65 
2 
66.34 
1 
73.45 
1 
tTest: TwoSample Assuming Unequal Variances 






Zara prices 
Zara styles 
Mean 
51.23195651 
1.965517241 
Variance 
198.7692613 
0.677339901 
Observations 
29 
29 
Hypothesized Mean Difference 
0 

df 
28 

t Stat 
18.78610969 

P(T<=t) onetail 
0.0014 

t Critical onetail 
1.701130908 

P(T<=t) twotail 
0.002 

t Critical twotail 
2.048407115 

From the above ttest analysis it can be noted that mean value of Zara average price is 51.23 and the styles of Zara is 1.96, the variance of prices are high when compared with the styles.
It is further noted that t statistic is 18.8 whereas the p value is 0.001, this shows that the mean of values between price changes and styles are same. Therefore, null hypothesis is accepted. Hence it is stated that there is no significant difference between Zara average prices and Zara styles.
Boslaugh, Sarah (2012). Statistics in a Nutshell. Cengage Publishing
Freedman, David (2010). Statistics. 4th Edition. Cengage Publishing
Sincich, T. Terry (2012). Statistics. 12th Edition.
Triola (2014). Essentials of Statistics. 5th Edition. McGraw Hill
Witte, S. Robert. (2010). Statistics. 5th Edition. McGraw Hill