Complete Course BUS308 Complete Course

Saturday, 12 December 2015

Complete Course BUS308 Complete Course

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Week 1
Data Scales.
A marketing agency is interested in the buying habits of those who shop online versus those who shop in person. In your discussion post, address each of the following and explain your reasoning as to why you picked the specific scale:
a. A database is created and shoppers are classified as online or in-person shoppers. Such classifications represent data of which scale? Explain your reasoning.

b. Online and in-person shoppers are to be compared on their relative incomes. Income data represent data of which scale? Explain your reasoning.
c. If shoppers are also ranked from the most to the least frequent shoppers, those rankings represent data of which scale? Explain your reasoning.
d. Shoppers are asked to complete a customer satisfaction survey. In response to each question on the survey, the shoppers circle one of five answers: strongly agree, agree, neutral, disagree, or strongly disagree. Their responses are data of which scale? Explain your reasoning.
e. What measure(s) of central tendency will be most informative for each of the above data? Justify why each measure is most informative.
f. What would be more critical for the marketing agency to utilize; descriptive or inferential statistics? Why?


The manager of a sandwich shop gathers data on what people spend on lunch on a particular day of the week.
The results are $4.20, $4.22, $2.35, $4.32, $5.25, $6.48, $6.78, $8.59, $6.95, $5.52, $6.83, $7.35, $4.36, $9.39, $6.42.
To the degree that this represents the population of all those who eat lunch at sandwich shops, what percentage of customers spend:
a. less than $4.34?
b. between $3.50 and $4.80?
c. less than $2.35?
Imagine that you are the manager of a sandwich shop. Based on your calculations, what do you think is the best pricing point for your shop? What would you do to increase sales at the sandwich shop?
For additional information on calculating probability refer to the Week One Recommended Resources.


1.         Question :       Data on the city from which members of a board of directors come represent interval data.
 2.        Question :       Inferential statistics infer the characteristics of samples.
 3.        Question :       The mode is which of the following?
 4.        Question :       The standard error of the mean can be calculated by dividing μ by the square root of the number of values in the distribution.
 5.        Question :       If a certifying agency raises the requirements for real estate agents, what sort of decision error is the agency protecting against?
 6.        Question :       Which of the following defines statistical significance?
 7.        Question :       In a frequency distribution such as a bell-shaped curve, what does the vertical height of the curve indicate?
 8.        Question :       Which of the following is a provision of the central limit theorem?
 9.        Question :       In statistical notation, M is to μ as s is to σ.
 10.         Question :           Technically, “statistic” refers to which?


Problem Set Week One.
Problem 1
The performances of a group of interns are evaluated by their supervisors at the end of their internships.
Their scores are: 55, 47, 62, 27, 50, 49, 66, 53, 50, 44, 63, 59.
Complete the calculations below using this data. Show all of your work and clearly label each of your calculations.
a. the mean
b. the median
c. the range
d. the standard deviation
e. the variance
Problem 2
The Anxiety General Stress Test (ANGST) has been designed to gauge the level of psychological stress management trainees experience when they are under pressure.
For a random sample of trainees the scores are as follows: 47, 49, 53, 53, 54, 58, 61, 64, 75, 81.
Complete the calculations below using this data. Show all of your work and clearly label each of your calculations.
a. What is the z score equivalent of ?
b. What is the probability that someone selected at random will score 81 or lower?
c. What percentage of all trainees will score between 60 and 75?


BUS 308 Week 2 Journal


Imagine a shift manager at a manufacturing plant is gathering data on the number of units workers assemble during two different shifts over 10 different days. If the number of units assembled by each shift varies greatly from day to day, what impact will that have on the likelihood of a significant difference between the two shifts? Explain and support your response.


ANOVA Testing.
The manager of an agency providing temporary employees to city offices is analyzing the number of days temporary hires typically work in various types of industries.
The data are as follows:
Legal clerical: 2, 1, 4, 4, 2, 5, 6
Accounting firms: 3, 6, 4, 5, 5, 7, 8
Insurance: 5, 4, 7, 9, 9, 8, 11
Using the data above, answer the following questions:
a. Are there significant differences in the length of time temporary employee’s work in the different industries?
b. How much of the differences can be explained by the industry?
c. Which groups are significantly different from others?
d. Why would a manager be focused on measuring the number of days that a temporary works each week?


1.         Question :       How is the sum of squares unlike either the standard deviation or the variance?
 2.        Question :       If sums of squares statistics are calculated for shoppers at three different retail outlets, what statistic will indicate the variability among those at each outlet?
 3.        Question :       Which is the symbol used for the test statistic in ANOVA?
 4.        Question :       If ANOVA reveals that four different departments have significantly different levels of productivity, what will a post-hoc test indicate?
 5.        Question :       The independent t-test is based on which distribution?
 6.        Question :       What does omega-squared indicate?
 7.        Question :       Each different t-distribution is defined by which of the following?
 8.        Question :       When a significant interaction is graphed, what is indicated on the vertical axis?
 9.        Question :       Four different groups of employees are randomly selected from a common population for a study of differences in the impact of a wage increase.  Why will there be differences even before the incentive is applied?
 10.         Question :           What is the probability of type II error when the null hypothesis is rejected?


Problem Set Week Two.
Problem One
Suppose that an automotive parts and accessories chain is experimenting with a new sales promotion. Two similar stores are selected for the experiment. For Store 1, nothing changes. This store constitutes the control group. For Store 2, the treatment group, the promotion is implemented.
Sales in hundreds of dollars over a five-day period are as follows:
Control: 6, 6, 7, 10, 12, 9, 6, 5, 5, 7
Treatment: 2, 5, 2, 4, 7, 1, 2, 3, 4, 5
The expectation that sales will be higher in the treatment group makes this a one-tailed test; the alternate hypothesis is m1 < m2. Use Excel to determine whether differences between the two groups are statistically significant.
Show all of your work and clearly label each of your calculations. Share your calculations and your interpretations of your findings in your Word document.
Problem Two
Suppose that a home builder is approached by a customer who wants to move in as soon as possible. The customer chooses three home designs that she likes and asks the home builder which one could be completed the fastest. To compare the three designs on speed of completion, the builder randomly selects 10 homes that he built in the past based on each of the three designs.
Use the Excel Analysis ToolPak to run an ANOVA test in order to determine which design would be best for the customer. Show all of your work and clearly label each of your calculations. Make sure to also clearly describe the respective data and your conclusions.
The data for the number of days to build each home are as follows:
Design A: 15, 17, 19, 21, 23, 25, 27, 29, 31, 33
Design B: 29, 34, 39, 44, 49, 54, 59, 64, 69, 74
Design C: 22, 24, 25, 27, 28, 28, 29, 31, 33, 34
For more information on conducting an ANOVA test in Excel reference the Week Two Recommended Resources.
Problem Three
An insurance company is reviewing its current policy rates. When originally setting the rates they believed that the average claim amount was $1,800. Now there are concerns that if the true mean is actually higher than this they could potentially lose a lot of money. They randomly select 40 claims, and calculate a sample mean of $1,950. Assuming that the standard deviation of claims is $500, and set significance , test to see if the insurance company should be concerned


Interval Data.
A courier service in a large city tracks the number of deliveries they are asked to make by 10 clients both before and after offering a progressive discount for repeat business. Their goal is to assess the effects of the discount.
a. What is the most appropriate statistical test in this situation? Why?
b. Are there significant differences in the number of deliveries?
c. If the goal is to promote repeat business, should the discount be continued?


An employment agency gathers the following data on its clients:
a) Age
b) Gender
c) Educational level (no high school, high school, associate’s, bachelor’s, master’s)
d) Years of past experience
e) Whether or not they have been successfully placed in employment by the agency
Additionally, the following data is gathered for those who have been successfully placed:
a) Starting salary
b) Current salary
c) Tenure in months
Based on the information above answer the following questions:
a. Which type of correlation procedure would be most appropriate to gauge the relationship between each pair of variables?
b. Do you expect each pair of variables to be significantly correlated or not? Why?
c. For the variables you expect to be significantly correlated, what do you expect the direction of the relationship to be? Why?


Evaluation of Correlations.
Data are gathered regarding the length of tenure top executives have at a major corporation and whether those executives have been divorced. The Human Resources department is evaluating this data to drive decision-making in regard to their hiring process. The data for eight executives is as follows:
In a three to five page paper, excluding title page and reference page(s), answer the following questions to analyze the data. Include clearly labeled calculations, if applicable. Calculations conducted in Excel must be copied and pasted into the Word document. This paper should be formatted according to APA guidelines outlined in the Ashford Writing Center.
a. What’s the most appropriate procedure for evaluating the relationship between tenure and divorce?
b. What is the correlation and how can it be interpreted in terms of magnitude, direction and practical importance?
c. How much of whether executives have been divorced can be accounted for by their length of tenure with the organization? How much of tenure can be explained by whether there has been a divorce?
d. Make a logical argument for why lengthy tenure may be causing divorce.
e. Make another logical argument for why divorce may be causing lengthy tenure.


Simple Regression Analysis.
Use the data in the chart to answer the questions below. The data indicates the number of “sick days” appliance installers take during a three month period, and the number of complaints filed by customers during the same interval. Use the Analysis Toolpak in Excel to perform this simple regression and answer the questions.
a. Is the correlation between number of sick days and number of customer complaints statistically significant?
b. What is the best prediction for the number of complaints that will be registered for an installer who takes five sick days during the period?


Multiple Regression Analysis.
Develop a multiple linear regression equation that describes the relationship between tenure and the other variables in the chart above. Use the Analysis Toolpak located in Excel to perform this multiple regression.
Do these two variables explain a reasonable amount of the variation in the dependent variable?
Estimate the tenure of someone that could have $5.8($k) and 15 years of job satisfaction. Make sure to state your multiple regression equation in your example. What are some of things that you can estimate from the model? How effective is evaluating the R-squared of the model? What is the relationship between the independent and dependent variables?
Use Excel to help you answer the questions in the forum but do not attach your Excel document to the discussion post.
Guided Response: Review several of your classmates’ postings. Respond to at least two classmates by commenting on how this information might be used to make business decisions.


Problem Set Week Four.
Problem One
The manager of a catering company is using the number of people in the party to predict the cost of the drinks that are required for the event. The following are the data for 12 recently catered events:
Complete the calculations below using this data. Show all of your work and clearly label each of your calculations.
a. Provide a scatterplot
b. Calculate a linear regression
c. Calculate the residuals
d. Calculate the correlation between the two variables
e. Calculate the mean, median, and standard deviation of the number of people and cost of drinks
For additional assistance with these calculations reference the Recommended Materials for Week Four.
Problem Two
You are a real estate agent and you are trying to predict home prices for your clients that want to list their house for sale. You have a very small city without much data. You will need to use the data that you have available for the past year on homes that have been sold.
Complete the calculations below using this data. Show all of your work and clearly label each of your calculations.
Conduct a multiple regression analysis to predict home prices. In your analysis complete the following:
a. Calculate the multiple regression analysis and report your data.
b. Determine the list price for your client’s home if it has three bedrooms, three bathrooms, and 1900 square footage. Provide your analysis and show all of your calculations.
For additional assistance with these calculations reference the Recommended Materials for Week Four.


1.         Question :       With reference to problem 1, what statistic determines the correlation of experience with productivity, controlling for age in experience?
 2.        Question :       In a problem where interest rates and growth of the economy are used to predict consumer spending, which of the following will increase prediction error?
 3.        Question :       With reference to problem 3, how is the regression constant or the a value interpreted?
 4.        Question :       Which of the following is a problem in simple regression?
 5.        Question :       In a problem where average temperature and number of daylight hours are used to predict energy consumption in homes, what does the standard error of multiple estimate gauge?
 6.        Question :       What does “shrinkage” mean in reference to regression solutions?
 7.        Question :       The degree to which years of education and years of experience together correlate with annual salary is indicated in multiple correlation.
 8.        Question :       The criterion variable in regression is the variable used to predict the value of y.
 9.        Question :       Which of the following are consistent with the requirements of simple regression?
 10.      Question :       Larger sample diminish the standard error of the estimate.   


Confidence Intervals.
A hardware retailer has average sales of $64,235 with a standard deviation of $5,918 for a 12-month period. The mean monthly sales for all retailers in the chain are $59,844. Is this hardware retailer’s sales significantly different from all retailers in the chain at ? Are they significantly different at ? Calculate a .95 confidence interval for the data in problem.
Explain your findings and determine what question the confidence interval answers.
Guided Response: Review several of your classmates’ postings. Respond to at least two classmates by commenting on whether or not you think changing the confidence intervals will result in a different outcome. Explain if you agree or not with the role of a confidence interval in the interpretation of the answer


Correlation and Confidence Intervals.
A car dealer is using the number of years customers have owned their vehicles to predict how long it will be before they elect to replace them. The correlation between the two is (the longer they have owned their present vehicles, the more quickly they are expected to replace them). The other relevant data are as follows for 32 customers:
Based on the information above, answer the following questions:
a. How long is the time to expected replacement for a customer who has owned a vehicle 6.5 years?
b. Calculate .95 and .99 confidence intervals and explain your results.
c. How will a larger standard deviation in the criterion variable affect the width of the confidence intervals? Why?


Writing the Final Paper
Assignment Instructions:
A retail store has recently hired you as a consultant to advise on economic conditions. One important indicator that the retail store is concerned about is the unemployment rate. The retail store has found that an increase in the unemployment rate will cause a lack of consumer spending in their stores. Retail stores use the unemployment rate to estimate how much inventory to keep at their stores, which is important in maintaining cost effectiveness. In this consultant role you will apply calculations and research to create a predictive sales report.
You will complete this project in two parts, but will submit your work as one Word document. Copy and paste your calculations from your Excel workbook into the Word document.
The Final Project must be eight to ten pages in length, excluding title page and reference page(s) and must include at least three scholarly sources, in addition to the Job and Labor Statistics site. Be sure to format your work in accordance with APA guidelines outlined in the Ashford Writing Center.
Part I
Reference the data in this Excel workbook to complete the following quantitative components of the predictive sales report. You will complete the calculations below in your own Excel workbook and then copy and paste from your Excel workbook into the Word document.
1. Calculate the mean yearly value using the average unemployment rate by month found in the “Final Project Data Set.”
2. Using the years as your x-axis and the annual mean as your y-axis, create a scatter plot and a linear regression line.
3. Answer the following questions using your scatter plot and linear regression line:
a. Compute the slope of the linear regression line.
b. Identify the Y-intercept of the linear regression line.
c. Identify the equation of the linear regression line in slope-intercept form.
d. Calculate the unemployment rate in 2016, based on the linear regression line.
e. Calculate the residuals of each year.
f. Find the latest unemployment rate in your state. You will need to go to the Bureau of Labor Statistics website ( and hover over “Subject Areas” in the top menu panel then select “State and Local Unemployment Rates” from the drop down menu under “Unemployment Rate”. Determine whether the rate in your state is within the range of the linear regression line or if it is an outlier.
g. Interpret your results of the model and explain how a company could use the results to drive decision making.
Next interpret the analysis from Part I to complete the following qualitative components of the predictive sales report:
1. Introduce the project and its significance to the retail store.
2. Reference the statistical analysis that you completed in Part I and explain where the data came from, what type of analysis was done, what the findings were, and whether or not you believe the data to be accurate.
3. Explain your data-driven conclusions regarding the effects of the changing unemployment rate on the retail store.
4. Predict what could occur in the future that would change your linear regression line and therefore your prediction of sales.


BUS 308 Week 1 DQ 1 Data Scales
BUS 308 Week 1 DQ 2 Probability
BUS 308 Week 1 Quiz
BUS 308 Week 1 Problem Set Week One
BUS 308 Week 2 Journal
BUS 308 Week 2 DQ 1 t-Tests
BUS 308 Week 2 DQ 2 ANOVA Testing
BUS 308 Week 2 Quiz
BUS 308 Week 2 Problem Set
BUS 308 Week 3 DQ 1 Interval Data
BUS 308 Week 3 DQ 2 Correlation
BUS 308 Week 3 Assignment Evaluation of Correlations
BUS 308 Week 4 DQ 1 Simple Regression Analysis
BUS 308 Week 4 DQ 2 Multiple Regressions Analysis
BUS 308 Week 4 Problem Set
BUS 308 Week 4 Quiz
BUS 308 Week 5 DQ 1 Confidence Intervals
BUS 308 Week 5 DQ 2 Correlation and Confidence Intervals
BUS 308 Week 5 Final Paper
BUS 308 Complete Course BUS308 Complete Course
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