Part I

We’ll use the “Debt and Taxes” tab in the Lab 5 Excel Workbook

The Economic Data Runs from 1946 (1^st year post WW2) to 2016

Note: This issue is tremendously more complicated than the two variables presented here. This is only a partial look at the issue and there is ample room for debate as causes of the issues at hand.

1) Examining the Relationships

              Create and copy in the following Charts

                             1) Line Chart with “Year”, “Top Bracket %”, and “Debt (Relative to 1946)”

                             2) Scatterplot with “Year” and “Top Bracket %,” choose “Show Trendline”

                             3) Scatterplot with “Year” and “National Debt (Trillions),” choose “Show Trendline”

              a) What trends do you see over time?

              b) Do “Top Bracket %” and “National Debt(Trillions)” appear associated?

              c) What might be a possible confounding factor?

2) Running Regressions

              a) Use “Data->Data Analysis->Regression” with “Top Bracket” as the y variable and

“Year” as the x- variable.

What is your model? Slope t-value? F-Value? R squared?

              b) Run a second regression with “National Debt(Trillions)” as the y variable and

                             “Year” as the x-variable.

What is your model? Slope t-value? F-Value? R squared?

c) Run a final regression with “National Debt(Trillions)” as the y variable and

                             “Top Bracket %” as the x-variable

What is your model? Slope t-value? F-Value? R squared?

              d) Based on the R squared from part c) how much of the debts change is due to taxes?

Part II

We will use the “Twins Data” tab in the workbook.

1) Single Variable

              a) Create a Scatterplot of “Wins” and “Runs” (You might need to rescale the axis for each)

              b) Run a Regression with “Wins” as y and “Runs” as x

c) What is your model? Slope t-value? F-Value? R squared?

2) Multivariable

              a) Traditional Stats

                             Run a regression with “Wins” as the y variable and both “Batting Average” and “ERA”

as the two x variables

What is your model? Slope t-values? F-Value? R squared?

              b) Moneyball Stats

                             Run a regression with “Wins” as the y variable and “OPS” and “WHIP” as the x variables

What is your model? Slope t-value? F-Value? R squared?

3) Of the 3 options which model do you feel works the best? Explain.

At many amusement parks, customers who enter the park after 4 p.m. receive a steep discount on the price they pay. This is a type of price discrimination because the amusement park charges a lower price to

students who can’t visit until after 4 p.m. anyway.

people who have a more inelastic demand for amusement parks.

people who have a more elastic demand for amusement parks.

people with 9-to-5 jobs.

A random sample of 378 hotel guests was taken at La Mirage and it was found that 194 requested non-smoking room. Another random sample of 516 hotel guests at Neptune Grand showed that 320 requested non-smoking room. We wan to test the claim that the percentage of guests requesting non-smoking room is different
between the two hotels, using significance level 0.05. Round you answer to 3 decimal places.

In a survey of 3929 ​travelers, 1431 said that location was very important for choosing a hotel and 1222 said that reputation was very important in choosing an airline. Complete parts ​(a) through​ (c) below.

a. Construct a 95% confidence interval estimate for the population proportion of travelers who said that location was very important for choosing a hotel.

b. Construct a 95​% confidence interval estimate for the population proportion of travelers who said that reputation was very important in choosing an airline.

In a survey of 3,654 ​travelers, 1,456 said that location was very important for choosing a hotel and1,210 said that reputation was very important in choosing an airline. Complete parts ​(a) and (b) below.

a. Construct a 90% confidence interval estimate for the population proportion of travelers who said that location was very important for choosing a hotel.

b. Construct a 90% confidence interval estimate for the population proportion of travelers who said that reputation was very important in choosing an airline.

8. A hotel manager is looking to enhance the initial impression that hotel guests have when they check in. Believed to contribute to initial impressions is the time it takes to deliver a guest’s luggage to his or her room after check-in. A random sample of 20 deliveries on a particular day were selected from Wing A of the hotel, and a random sample of 20 deliveries were selected in Wing B (i.e., the Excel tab LUGGAGE). (a) Identify which type of test is most appropriate for you to use, justify your answer. (b) Determine whether or not the mean delivery time differs for the two wings of the hotel (use α = .05). (c) If faster luggage delivery time is positively related to guests’ initial impression, which wing(s) should receive the highest impression ratings? (3 points)

Peter was born after an uneventful pregnancy and weighed 3.1kg. At 3 months, he developed otitis media and an upper respiratory tract infection. At the ages of 5 months and 11 months, he was admitted to hospital with Haemophilus influenzae pneumonia. The infections responded promptly to the appropriate antibiotics on each occasion. When 16 months old, he developed balanitis. He is the fourth child of unrelated parents: his three sisters show no predisposition to infection.

Examination at the age of 18 months showed a pale, thin child whose height and weight were below the third centile. There were no other abnormal features. He had been fully immunized as an infant (at 2, 3 and 4 months) with tetanus and diphtheria toxoids, whole-cell pertussis, Haemophilus conjugate vaccine and oral polio. In addition he had received measles, mumps and rubella vaccine at 12 months. All immunizations were uneventful.

Immunological investigations (Table C3.1) into the cause of his recurrent infections showed severe panhypogammaglobulinemia with absent antibody production. Although there was no family history of hypogammaglobulinemia, the absence of mature B lymphocytes in his peripheral blood strongly supported a diagnosis of ________________________________?????. His antibody deficiency was treated by 2-weekly intravenous infusions of human normal IgG in a dose of 400mg/kg body weight/month. Over the following 2 years, his health steadily improved: his weight and height are now on the 10th centile, and he has had only one episode of otitis media in the last 18 months.

Table C3.1 Immunological investigations

*Normal range for age 18 months shown in brackets.