Wells Fargo claims that the waiting time for its customers is the lower than at Bank of the West. You draw random samples of 30 customers at each bank. At Wells Fargo, the mean waiting time is found to be 5.3 minutes, with a standard deviation of 1.1 minutes. At Bank of the West, the mean waiting time is found to be 5.6 minutes with a standard deviation of 1.2 minutes. Test Wells Fargo’s claim at a 0.05 significance level. Let population 1 be Wells Fargo customers and population 2 be Bank of the West customers.

a) State the null and alternative hypotheses.

b) Find the value of the test statistic. You may use a graphing calculator. Round to three decimal places.

c) Find the P-value. You may use a graphing calculator. Round to three decimal places.

d) State whether you should reject or fail to reject the null hypothesis. Justify your answer.

e) State your conclusion in non-technical terms.

f) To construct a confidence interval for ?1 − ?2 that corresponds to this hypothesis test, what confidence level should you use? (90%, 95%, 99%, for example – a sketch may help)

g) Construct this confidence interval. You may use a graphing calculator. Round to three decimal places.

h) Explain why you reach the same conclusion with the confidence interval and the hypothesis test.

Healthy Hots, a fast-food restaurant that offers heart-healthy food, is experiencing several difficulties with operations. Although customers like the idea of heart-healthy foods, and surveys indicate that customers find the food to be tasty and appealing, business has fallen off in recent weeks. At this point, the restaurant is making a profit. Customers have complained about slow service, and employee turnover is high.

            How can you apply the three principles of total quality management discussed in this module to improve the quality operations of the restaurant? Provide specific and possible solutions.

Since the SUTA rates changes are made at the end of each year and there is much discussion about changes to the FUTA rate, the available 2017 rates were used for FUTA and SUTA. Note: For this textbook edition the rate 0.6% was used for the FUTA tax rate for employers. Example 5-10 Applebaum Security Company is located in State H, which enables employers to reduce their contribution rates under the experience-rating system. From 2004 to 2013, inclusive, the company's total contributions to state unemployment compensation amounted to $18,135. For the calendar years 2014 to 2017, inclusive, the contribution rate for Applebaum was 3.7%. The contributions of each employer are credited to an account maintained by the State Unemployment Compensation Commission. This account is credited with contributions paid into the account by the employer and is charged with unemployment benefits that are paid from the account. Starting January 1, 2018, the contributions rate for all employers in State H will be based on the following tax-rate schedule:

Ryan White Title- Latino Commission on AIDS (TOPIC)

Contribute a description of their respective stakeholder/organization/agency and will Include statements about their:

Mission

Goals

What this stakeholder/agency/organization can contribute to the coalition effort and why

HOW the coalition work will align with the collective missions of each organization

An economist with a major bank wants to learn, quantitatively, how much spending on luxury goods and services can be explained based on consumers’ perception about the current state of the economy and what do they expect in the near future (6 months ahead).  Consumers, of all income and wealth classes, were surveyed.  Every year, 1500 consumers were interviewed.  The bank having all of the data from the 1500 consumers interviewed every year, computed the average level of consumer confidence (an index ranging from 0 to 100, 100 being absolutely optimistic) and computed the average dollar amount spent on luxuries annually.  Below is the data shown for the last 24 years.

Date                 X                     Y (in thousands of dollars)

1994                79.1                 55.6

1995                79                    54.8

1996                80.2                 55.4

1997                80.5                 55.9

1998                81.2                 56.4

1999                80.8                 57.3

2000                81.2                 57

2001                80.7                 57.5

2002                80.3                 56.9

2003                79.4                 55.8

2004                78.6                 56.1

2005                78.3                 55.7

2006                78.3                 55.7

2007                77.8                 55

2008                77.7                 54.4

2009                77.6                 54

2010                77.6                 56

2011                78.5                 56.7

2012                78.3                 56.3

2013                78.5                 57.2

2014                78.9                 57.8

2015                79.8                 58.7

2016                80.4                 59.3

2017                80.7                 59.9

Questions:

1. Measure the strength of the linear association between consumers’ moods and the dollar amounts spent on luxury items.
2. Construct the linear regression model for the dollar amount spent on luxury goods and services.
3. Explain how you would interpret the slope and the intercept of the regression model.
4. How well does our model fit the data? Explain what it means.
5. Do you think that measuring the level of optimism is a good predictor for trying to forecast future spending on luxury items?  Explain why or why not.
6. How would you be able to improve on the model?  You must provide a minimum of two specific ways to go about improving the model.
7. If the economist expects that, by year’s end, the average level of consumer confidence will hit 81.5 points, how much will be expected by consumers to spend on luxury items?

Is the number of tornadoes increasing? In the last homework, data on the number of tornadoes in the United States between 1953 and 2014 were analyzed to see if there was a linear trend over time. Some argue that it’s not the number of tornadoes increasing over time, but rather the probability of sighting them because there are more people living in the United States. Let’s investigate this by including the U.S. census count (in thousands) as an additional explanatory variable (data in EX11-24TWISTER.csv).

Perform a multiple regression using both year and census count as explanatory variables. Write down the fitted model. Are year and census count respectively significant in the MLR model?

Is the number of tornadoes increasing? In the last homework, data on the number of tornadoes in the United States between 1953 and 2014 were analyzed to see if there was a linear trend over time. Some argue that it’s not the number of tornadoes increasing over time, but rather the probability of sighting them because there are more people living in the United States. Let’s investigate this by including the U.S. census count (in thousands) as an additional explanatory variable (data in EX11-24TWISTER.csv).

Fit one SLR model with year as the predictor, another SLR model with census count as the predictor. Write down the two models. Are year and census count significant, respectively?

The population of Egypt in 2002 was 73,312,600 and was 78,887,000 in 2006. Assume the population of Egypt grew exponentially over this period.

1.   Determine the 4-year growth factor and percent change.

2. Determine the 1-year growth factor and percent change.

3. Define a function that gives the population of Egypt in terms of the number of years that have elapsed since 2002.

4. Re-write your function from part(c) so that it gives the population of Egypt in terms of the number of decades that have elapsed since 20002.

5. Assuming Egypt’s population continued to grow according to this model, how long will it take for the population of Egypt to double?

What is the regression model for the data? Is this a good model?

Year 2006 = 8,860 Students

2007 = 9,056

2008 = 9,050

2009 = 9,429

2010 = 9,407

2011 = 9,352

2012 = 9,608

2013 = 10,107

2014 = 10,382

2015 = 10,340

2016 = 10,805

2017 = 11,034

2018 = 11,639