The Regression Analysis Of Dunkin Donuts

I could use hypothesis testing in my current job as a landscaper. My boss could claim it talks no more then 40 minutes to mow 1/3-acre lawn. The null and alternative hypothesis would be; Ho: u40. I can then test a sample of 30 1/3-acre lawns and I would record how long it takes in minutes to mow the lawns. I can use the test statistic and the alpha value to find out if I have enough information to reject the null or fail to reject the null. I can use the data, and the conclusion of the data to see if I should adjust how much time I spend on lawns.

I can use confidence intervals in my job as a landscaper. My boss is 99% confidence that him and me can clear 30 driveways after a 6-inch snowstorm in eight hours. Data for this can be collected by testing if we can complete 15 driveways in 8 hours after a six-inch storm. It is a small sample size so I would use t. We are using a 99% confidence interval, so it means my boss is more confident that we can clear the driveways. The more confident the wider it is because the higher the interval the more times it is tested.

I could use regression analysis in my past place of employment at Dunkin Donuts. I can use it for finding out how many donuts are sold at which time a day. The dependent variable is Donuts sold because that is what I want to find. The time of day would be the independent variable would be time because there can be multiple variable that could impact how many donuts are sold including day of the week, holidays, or special say like national coffee day where someone gets a free donut with a medium sized hot or iced coffee.

The time of day variable would be on the scale of 18 and a half hours because the Dunkin Donuts where I worked is open 4:30am to 10pm. Data can be collected by recording how many donuts are sold during the day by hour. How many are sold by 5:30am, 6:30am, 7:30am, 8:30am, 9:30am 10:30am, etc. Then I would take that data to make a graph with a trendline and use an equation and put in times to get the resulting estimating y. I can give the data to Dunkin Donuts where we can use it to predict times and make sure we have the right number of donuts for the day and for the time. We can also find out what Dunkin Donuts and the employees can do to help sell more donuts in the hours that have many donut sales.