Inferential Statistic is the process of drawing conclusions from data that are subject to random variation, for example, observational errors or sampling variation. Our team uses inferential statistic to compare two groups, which are Melks and DHL. This paper outlines the sampling and data collection procedure used to test the null hypothesis. The null and alternate hypotheses are: (There is no significant difference in brand awareness based on the marketing channel used). (There is a significant difference in brand awareness on the basis of the marketing channel used). Here is an example of the statistics of our results describing the total number of people from minimum age to maximum from ages 35 to 55. Mock data for the independent variables for Melks.
The Pie chart below states the percentages of income of each region surveyed, starting with Los Angeles leading with 44%, New York with 35%, and the southern region with 21%. Los Angeles and New York combined holds nearly 80% of the income documented in the surveys.
The next pie graphs explain how MELKS services where utilized from consumers by ordering merchandise online from Online stores like Amazon, eBay, Craig’s list, and a cluster of department stores while and receiving by Melks carriers. Also one of the graphs show the percentage DHL controls with their deliveries from these same online stores. Although the graphs look very similar, however they are different in the number of customers of whom use the services.
The above column chart shows how we picked the targeted group for our team study with the people in the 35-55 years old age group. These particular groups of people have families, and they use delivery services of some type when ordering, delivering or purchasing products through the Internet. In conclusion Inferential Statistics is drawing conclusions from data that are subject to random variation in this case is Melks and DHL and this shows us the difference in percentage of usage between the two carrier services Melks and DHL. This has give us more information to determine if the null hypothesis will be correct or if we will need to accept the alternative hypothesis.
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