Question

Recall your hypotheses about the impact of the independent variables on the number of daily check-ins. Are there any coefficients that surprise you or contradict your original hypotheses? Develop a possible explanation for why the estimated coefficients might make sense in the context of the problem. Do the coefficients for payday or any of the holidays surprise you? What was your original hypothesis for those variables? Interpret the coefficients and explain why they support or contradict your intuition. Do any of the coefficients for day of the week surprise you? What was your original hypothesis for those variables? Interpret the coefficients and explain why they support or contradict your intuition. Do any of the coefficients for the number of rooms reserved on a given day to a particular type of guest (TotalRewards, SpecialEvent, VIP, FreeIndependent, Wholesale, Group) surprise you? What was your original hypothesis for those variables? Interpret the coefficients and explain why they support or contradict your intuition. Does the coefficient Rate surprise you? What was your original hypothesis for that variable? Interpret the coefficient and explain why it supports or contradicts your intuition.

Answer 1

Answer:

Detailed answer is given in the attached diagram.

Answer 2

In this exercise we have to use the knowledge given in the text and analyze the alternatives that best explain the predictions, thus we have to:

1) The checkins falls below the normal count as expressed by the negative coefficients of all these holidays.

2) Friday has the highest coefficient.

3) small positive coefficients of 0.12 and 0.26 respectively indicating small positive correlation they have with number of checkins.

1)It is expected that for the paydays and the holidays the checkins must be more or may have positive coefficients. But it has been observed that there are few holidays for which the coefficients are negative. Easter is a holiday which has a negative coefficient, this implies that people prefer to stay at home during this holiday and then there are lesser than normal number of checkins on Easter

• The coefficient is $-234.93$

• Father's Day has a coefficient of $-65.76$

• July 4th has a coefficient of $338.44$

• Columbus Day has a coefficient of $-47.59$

• Halloween has a coefficient of $-301.57$

Thus, as in general we consider that on paydays and the holidays there is going to be a surge in the checkins, but against this belief there are certain holidays during which the checkins falls below the normal count as expressed by the negative coefficients of all these holidays.

2) The coefficients of weekdays are as follows:

• Sunday: $561.72$

• Monday: $288.05$

• Tuesday : $96.89$

• Wednesday: $145.22$

• Thursday: $266.61$

• Friday: $604.89$

It was expected that the check-ins are the highest for Friday's since the weekend begins then, and this is true in the given data. Friday has the highest coefficient of 604.8.

Surprisingly Sunday has the second highest coefficient which indicates that there are many checkins on Sunday as well.

As expected, Tuesday and Wednesday have the least coefficients  indicating minimum check in's in midst of the week. Monday has third highest coefficient, indicated official major official checkins on Monday's.

3) Special event checkins with coefficient 0.19, this implies there is small positive relation of special events and checkins. This coefficient is as predicted, or slightly smaller than as predicted. It is predicted that with special events there would be more checkins.

Total rewards have a coefficient of 0.29. This implies there is a positive correlation of checkins and total rewards. Higher the rewards more the checkins. This is as per the hypothesis predicted for total rewards, as more rewards pull more customers.

VIP has a coefficient of -0.17. This shows that there is negative correlation on VIP and number of checkins. This is not as predicted since a positive relation of VIP and number of checkins is expected. May be this is because when a VIP checkins, not many visitors can checkin during that time.

Free independent customers have the highest coefficient of 0.40. This implies ore the free independent customers come; it results in more checkins. This is as per predicted by the hypothesis.

Whole sale and groups have small positive coefficients of 0.12 and 0.26 respectively indicating small positive correlation they have with number of checkins, and this again is as per the predicted hypothesis.

See more about hypothesis at brainly.com/question/2695653