Distribution of Sales in UK - Speech or Presentation Example

TASK DISTRIBUTION OF SALES IN UK Figure Histogram of Total Sales) The above histogram provides a holistic view of the annual sales made in UK. On average each outlet generates sales of ₤756200. The sample comprises of 105 outlets and the data has been divided as per the standards shown below: Table 1: Frequency Counts The distribution of the graph is ambiguous as it is difficult to find whether it is normally distributed or not, however the probability plot which is shown in figure 2 affirms that it has a normal distribution with few outliers. Figure 2: (Probability plot of Total Sales) PROPORTION OF OUTLETS IN THE THREE REGIONS Region Counts Percentage North 36 34% South 41 39% East 28 27% Total 105 100% Table 2: Proportion of outlets in different regions Figure 3: (Proportion of outlets in the three regions) The graph shows that the South Region has the highest number of outlets in its area whereas North region follows the way with 34.3%. MOST POPULAR SCREEN IN THE THREE REGIONS Figure 4: (Popularity of screens in different region) Region TYPE A (£000s) TYPE B (£000s) TYPE C (£000s) North 6517 9125 11816 South 7084 10367 13538 East 4888 6887 9181 Table 3: Data on screens and region From the above table and diagram we conclude that TYPE C screen is the most popular across all the regions with a share of approximately 43%. COMPARISON OF MALE & FEMALES SALES WITH TYPE OF SCREEN Gender TYPE A (£000s) TYPE B (£000s) TYPE C (£000s) Male 7866 10778 14604 Female 10623 15601 19931 Table 4: Data on sales for different type of screens across both gender without sample size Simply making judgments based on the above table can lead to false conclusion since it does not show the number of male and female managers. However, after incorporating the data for size of both genders we can find the average sales made by each manager. Male = 44 Female = 61 Gender Average Sales TYPE A (£000s) Average Sales TYPE B (£000s) Average Sales TYPE C (£000s) Male 179 245 332 Female 174 256 327 Table 4: (Data on sales for different type of screens across both gender with sample size) Figure 5: (Average sales for different type of screens by male and female managers) From the above figures we can conclude that TYPE A & TYPE C screen are sold the most by male managers as compared to female managers. ANNUAL SALES AND TYPE OF GENDER Null Hypothesis: (There is no difference in the annual sales made by both the genders) Alternative Hypothesis: (There is a difference in the annual sales made by both the genders) Two-Sample T-Test and CI: Total Annual Sales, Gender Gender N Mean StDev SE Mean Male 44 755.6 98.2 15 Female 61 756.6 90.4 12 Difference = mu (1) - mu (2) Estimate for difference: -1.0 95% CI for difference: (-38.3, 36.3) T-Test of difference = 0 (vs not =): T-Value = -0.05 P-Value = 0.958 DF = 88 If the value of the test statistic is less than -2.262 or greater than 2.262 than reject; otherwise do not reject. Figure 6: (Distribution Plot) Since the value of t-test statistic is -0.05 as found in the Minitab output given above therefore we reject do not reject the null hypothesis and we can conclude that there is no difference in the annual sales made by male compared to female managers. The two similar diagrams below confirm that there is no variation in the annual sales made by male compared to female managers. Figure 7: (Individual Plots) RELATIONSHIP OF TOTAL SALES WITH ADVERTISING Figure 8: (Regression line for annual sales and advertising budget) Pearson correlation of Advertising (£) and Total Sales(£000s) = 0.784 The correlation test from the Minitab output shows that there is a strong relationship between total sales and advertising budget recommended for outlet because the correlation value 0.784 is close to 1. Regression Analysis: Total Sales (£000s) versus Advertising (£) The regression equation is Total Sales (£000s) = 510.4 + 0.2400 Advertising (£) S = 58.2296 R-Sq = 61.4% R-Sq (adj) = 61.0% Analysis of Variance Source DF SS MS F P Regression 1 555267 555267 163.76 0.000 Error 103 349241 3391 Total 104 904508 RELATIONSHIP OF TOTAL SALES AND DISTANCE OF OUTLETS Figure 9: (Regression line for annual sales and distance of outlets) Pearson correlation of Total Sales (£000s) and Distance (miles) = -0.777 There is a strong negative correlation between distance of outlets from the high street and annual sales which implies that if the distance of outlet from the high street is increased than the sales will fall heavily due to the influencing variable. Regression Analysis: Total Sales (£000s) versus Distance (miles) The regression equation is Total Sales (£000s) = 933.7 - 80.17 Distance (miles) S = 59.0155 R-Sq = 60.3% R-Sq (adj) = 60.0% Analysis of Variance Source DF SS MS F P Regression 1 545776 545776 156.70 0.000 Error 103 358732 3483 Total 104 904508 TASK 2 SOCIAL TRENDS Household expenditure: by purpose United Kingdom   1971 1981 1991 2001 2007 Food and non-alcoholic drinks 21 17 12 9 9 Alcoholic drinks and tobacco 7 6 5 4 4 Clothing and footwear 9 7 6 6 6 Housing, water and fuel 15 17 18 18 20 Household goods and services 7 7 6 6 6 Health 1 1 2 2 2 Transport 12 15 15 15 14 Communication 1 2 2 2 2 Recreation and culture 9 9 10 12 12 Education 1 1 1 2 2 Restaurants and hotels 10 11 12 11 12 Miscellaneous goods and services 7 7 11 11 11 Total domestic household expenditure 100 100 100 98 98 of which goods 65 61 53 49 47 of which services 35 40 47 49 51 Source: Office for National Statistics Table 5: (Data for household expenditure) Figure 10: Changes in household expenditure over the period of time From 1971 to 2007, we have seen drastic changes in the proportion of different expenditure categories. The proportion of total household expenditure on services in the UK increased from 35% to 51%. Food and non-alcoholic drink was the largest category in 1971, which accounted for almost for 21% of expenditure, but this category has tumbled down to 9 %. However this does not imply that the real expenditure on food and alcoholic drink is falling rather the percentage on goods and services is increasing more rapidly than other categories. We will now examine one of the variables to predict what will happen in the future. FORECAST OF FOOD AND NON-ALCOHOLIC DRINKS Figure 11: (Fitted line plot for number of years and Food and non-alcoholic drinks) Pearson correlation of Year and Food and non-alcoholic drinks = -0.979 Regression Analysis: Food and non-alcoholic drinks versus Year The regression equation is = 20.74 - 0.3537 Year We will now forecast what will be the percentage of Food and non-alcoholic drinks in 2020. Since we assumed that 1971 has a value of 1 therefore the value of 2020 would be 50. Inputting the value to the equation provides: Food and non-alcoholic drinks (2020) = 20.74 - 0.3537(50) = 3.06 % Read More