Verizon Wireless records as a measure of productivity the number of weekly cell phone activations each of its retail employees achieves. The data below show a sample of 25 employees, for each employee giving the number of activations in the sampled week, the number of years of experience on the job, the gender (0-Male; 1-Female), the employee performance rating on a scale of 1-100, and the employee's age.

(Please help me solve this with MS EXCEL)

a. Estimate the mean number of activations in a week for a 30 year old male employee who has 5 years of experience and a performance rating of 90.

b. interpret the coefficient for the Gender variable:

c. What are the null and alternate hypotheses that would be used to test if the model is significant overall. (Express symbolically if possible)

d. What is the p-value that would be used for the hypothesis test corresponding to the hypotheses in part c?

e. What are the null and alternate hypotheses that would be used to determine if age is significantly related to the number of activations? (Express symbolically if possible)

f. Determine if age is significantly related to number of activations. Use α = 0.05. Give complete conclusions.

The at rest pulse rate of 32 students were recorded. The dataset contains the following variables: (1) Gender, (2) Age, (3) Pulse Rate (beats per minute), (4) Ordinal scale for how good they think they are in shape (1 = poor, 5 = good), (5) Weight, (6) Height Is there evidence to suggest the typical pulse rate of males is less than females? Download/Display Data Gender Age Are you in shape? Pulse Rate (min) Weight Height (inches) Female 18 2 84 165 61 Male 18 5 53 170 69 Female 27 3 100 150 64 Male 21 3 80 170 72 Female 21 4 80 130 63 Male 20 3 52 190 73 Male 19 3 72 165 72 Male 20 3 92 270 71 Female 18 3 80 138 64 Female 18 4 80 168 65 Male 19 4 80 147 71 Female 27 3 144 194 65 Male 35 2 96 260 72 Male 32 2 68 220 69 Female 18 2 80 145 64 Male 18 4 52 140 68 Female 23 3 80 140 63 Male 18 3 68 250 74 Male 19 4 80 230 75 Male 21 4 64 135 69 Male 18 2 58 190 70 Female 19 4 68 112 63 Male 19 2 68 170 67 Female 35 2 76 219 66 Female 25 3 84 127 61 Male 21 3 72 175 70 Male 20 2 72 174 70 Female 19 1 72 150 70 Female 22 3 68 170 66 Female 23 2 84 192 71 Female 27 4 92 137 67 Female 27 4 92 137 67.

You have restaurants in two major cities. You want to test out a new menu item. You offer a small sample to a random sample of customers in each city. You are interested in estimating the overall difference in proportion of customers between the two cities who would purchase this new item. In the first city, 64 out of 148 said that they would purchase the item if it was available. In the second city, 80 out of 302 said yes.

Estimate the overall difference in proportions of customers who would buy this product between the two cities. Use a 90% confidence level.

a) State the parameter of interest. Verify that the necessary conditions are present in order to carry out the procedure.

b) Find the margin of error.

EBP=  

c) Write out the confidence interval: (,)

d) Interpret the 90% confidence interval in context.

-Determine the Finished Goods for January and provide the number of the Jobs in Finished Goods at the end of January and determine the total cost of ending finished goods for January.

-Determine the Cost of Goods Sold for January and provide the number of the Jobs in Cost of Goods Sold for January and determine the total cost of goods sold for January.

-Determine the ending Work-In-Process Inventory at the end of January and provide the number of the Jobs still in Work-in-Process at the end of January and the determined total cost of ending work in process for January.

1. Delta Corporation has financial pretax income of $850,000 for 2019. There are three differences between financial pretax income and taxable income.

1. Unearned revenue causes taxable income to be greater than financial income by $80,000. It reverses equally over the next two years.
2. Depreciation on the tax return is $120,000 higher than the amount reported on the financial statements. The difference reverses equally over the next three years
3. The financial statements include $20,000 interest earned on municipal securities.

The companies tax rate for 2019 is 20% and will be the same for all future years.

The company expects to be profitable in future years

Required:

1. Compute taxable income, income taxes payable for 2019.
2. Prepare the journal entry to record income tax expense, income taxes payable and deferred tax for the year.
3. Prepare the income tax expense section of the income statement beginning with income before tax.

Describe a research effort where you could use a Multiple Regression analysis. It could be something related to work productivity, or perhaps a student’s performance in school.

List three variables (X1, X2, X3) you’d include in a Multiple Regression Model in order to better predict an outcome (Y) variable. For example, you might list three variables that could be related to how long a person will live (Y). Or you might list three variables that contribute to a successful restaurant. Your Regression Model should have three variables that will act as “predictors” (X1, X2, X3) of a “criterion” (Y’). Note that the outcome or criterion variable (e.g. how long a person would live, or the success/profit made by a restaurant measured) in must be a “Measurement” variable, that is something that is measured on a scale like inches, pounds, IQ, lifespan, stock value, etc. But that the predictors (X variables) can be either a measurement variable OR a categorical variable such as gender, political party, location, etc.

A golf club manufacturer claims that golfers can lower their scores by using the manufacturer's newly designed golf clubs. Eight golfers are randomly selected and each is asked to give his or her most recent score. After using the new clubs for one month, the golfers are asked again to give their most recent score. The scores for each golfer are given in the table below. Is there enough evidence to support the manufacturer's claim? Let d=(golf score after using the newly designed golf clubs)−(golf score before using the newly designed golf clubs). Use a significance level of α=0.05 for the test. Assume that the scores are normally distributed for the population of golfers both before and after using the newly designed clubs. Golfer 1 2 3 4 5 6 7 8 Score (old design) 96 86 79 95 78 92 75 79 Score (new design) 94 89 74 90 82 90 71 74 Step 3 of 5 : Compute the value of the test statistic. Round your answer to three decimal places.

I Have an assignment which is requiring to me to draw scientific correlations between 3-6 parameters of data collected.The data collected was in regards to BMI, height, weight, resting heart rates before and after exercise,Gender, wrist and waist circumference, postcode, air pollution and bushfires. For my scientific report and analysis I have decided to choose BMI and air pollution, but I don't know how to scatterplot or correlate the information valid for my investigation as there are so many variable to contribute to my correlation, for example BMI correlates with height, weight and gender and air pollution correlates with postcode, how do I collate all of this together to make clear correlations between BMI and Air pollution?

A market researcher believes that brand perception of one of the company's products may vary between different groups. After interviewing 373 persons, the following data was compiled. Can we conclude that brand perception is dependent on age? Age Favorable Unfavorable Neutral Total 18-30 80 27 25 132 30-45 73 27 23 123 Over 45 69 25 24 118 Total 222 79 72 373

Step 1 of 8: State the null and alternative hypothesis.
H0:brand perception and age are dependant, Ha: brand perception and age are independent OR H0:brand perception and age are independent, Ha:brand perception and age are dependant

Step 2 of 8: Find the expected value for the number of particpants who are 1818-3030 years old and have a favorable perception of the brand. Round your answer to one decimal place.

Step 3 of 8: Find the expected value for the number of particpants who are 3030-4545 years old and have a favorable perception of the brand. Round your answer to one decimal place.

Step 4 of 8: Find the value of the test statistic. Round your answer to three decimal places.

Step 5 of 8: Find the degrees of freedom associated with the test statistic for this problem.

Step 6 of 8: Find the critical value of the test at the 0.0250.025 level of significance. Round your answer to three decimal places.

Step 7 of 8: Make the decision to reject or fail to reject the null hypothesis at the 0.0250.025 level of significance.
Fail to reject Null hypothesis OR Reject Null hypothesis

Step 8 of 8: State the conclusion of the hypothesis test at the 0.0250.025 level of significance.

Data for Sale  

Want a list of 3,877 charity donors in Detroit? You can buy it from USAData for $465.24 through USAData’s Web site, which is linked to large databases maintained by Acxiom and Dun & Bradstreet, anyone with a credit card can buy marketing lists of consumers broken down by location, demographics, and interests. The College Board sells data on graduating high school seniors to 1,700 colleges and universities for 28 cents per student. These businesses are entirely legal. Also selling data are businesses that obtain credit card and cell phone records illegally and sell to private investigators and law enforcement. The buying and selling of personal data has become a multibillion dollar business that’s growing by leaps and bounds. Unlike banks or companies selling credit reports, these private data brokers are largely unregulated.

There has been little or no federal or state oversight of how they collect, maintain, and sell their data. But they have been allowed to flourish because there is such a huge market for personal information and they provide useful services for insurance companies, banks, employers, and federal, state, and local government agencies. For example, the Internal Revenue Service and departments of Homeland Security, Justice, and State paid data brokers $30 million in 2005 for data used in law enforcement and counterterrorism.

The Internal Revenue Service signed a five-year $200 milllion deal to access ChoicePoint’s databases to locate assets of delinquent taxpayers. After the September 11, 2001 terrorist attacks, ChoicePoint helped the U.S. government screen candidates for the new federally controlled airport security workforce.

ChoicePoint is one of the largest data brokers, with more than 5,000 employees serving businesses of all sizes as well as federal, state, and local governments. In 2004, ChoicePoint performed more than seven million background checks. It pocesses thousands of credit card transactions every second. ChoicePoint builds its vast repository of personal data through an extensive network of contractors who gather bits of information from public filings, financial-services firms, phone directories, and loan application forms. The contractors use police departments, school districts, the department of motor vehicles, and local courts to fill their caches. All of the information is public and legal.

ChoicePoint possesses 19 billion records containing personal information on the vast majority of American adult consumers. According to Daniel J. Solove, associate professor of law at George Washington University, the company has collected
information on nearly every adult American and “these are dossiers that J. Edgar Hoover would be envious of.”

The downside to the massive databases maintained by ChoicePoint and other data brokers is the threat they pose to personal privacy and social well being. The quality of the data they maintain can be unreliable, causing people to lose their jobs and
their savings. In one case, Boston Market fired an employee after receiving a background check from ChoicePoint that showed felony convictions. However, the report had been wrong. In another, a retired GE assembly-line worker was charged a higher insurance premium because another person’s driving record, with multiple accidents, had been added to his ChoicePoint file.

ChoicePoint came under fire in early 2005 for selling information on 145,000 customers to criminals posing as legitimate businesses. The criminals then used the identities of some of individuals on whom ChoicePoint maintained data to open fraudulent credit card accounts.

Since then ChoicePoint curtailed the sale of products that contain sensitive data, such as social security and driver’s license ID numbers, and limited access by small businesses, including private investigators, collection agencies, and non-bank financial institutions. ChoicePoint also implemented more stringent processes to verify customer authenticity.

Marc Rotenberg of the Electronic Privacy Information Center in Washington, D.C., believes that the ChoicePoint case is a clear demonstration that self-regulation does not work in the information business and that more comprehensive laws are needed. California, 22 other states, and New York City have passed laws requiring companies to inform customers when their personal data files have been compromised. More than a dozen data security bills were introduced in Congress in 2006 and some type of federal data security and privacy legislation will likely result. Privacy advocates are hoping for a broad federal law with a uniform set of standards for privacy protection practices.

1) Discuss the issues raised by data brokers in the context of management, organization, and technology factors.

2)    Use a professional code of ethics to recommend solutions to the issues in 2.