BUS 308 (New) Reading feeds the Imagination/Uophelpdotcom

1. BUS 308 (New) Reading feeds the Imagination/Uophelpdotcom For more course tutorials visit www.uophelp.com

2. BUS 308 Entire Course (New) For more course tutorials visit www.uophelp.com BUS 308 Week 1 DQ 1 Language BUS 308 Week 1 DQ 2 Probability BUS 308 Week 1 Quiz BUS 308 Week 1 Problem Set BUS 308 Week 1 Quiz (New) BUS 308 Week 2 DQ 1 Hypotheses BUS 308 Week 2 DQ 2 Variation BUS 308 Week 2 Quiz

3. BUS 308 Week 1 DQ 1 Language For more course tutorials visit www.uophelp.com Discussion 1-1/Language Numbers and measurements are the language of business. Organizations look at results in many ways: expenses, quality levels, efficiencies, time, costs, etc. What measures does your department keep track of? Are they descriptive or inferential data, and what is the difference between these? (Note: If you do not have a job where measures are available to you, ask someone you know for some examples, or conduct outside research on an interest of yours, or use personal measures.)

4. BUS 308 Week 1 DQ 2 Probability For more course tutorials visit www.uophelp.com Things vary in life – virtually nothing (except physical standards such as the speed of light) we interact with is constant over time. Much of this variation follows somewhat predictable patterns that can be examined using probability. An example of a subjective probability is: “Cops usually do not patrol this road, so I can get away with speeding.” An empirical probability example is: “Each production run has a 5% reject rate.” A classical (or theoretical) probability example is: “This die has six sides, so I should see the number 2 come up 1/6th of the time.” What are some examples of probability outcomes in your work or life? How would looking at them in terms of probabilities help us understand what is going on? How does the normal curve relate to activities/things you are associated with?

5. BUS 308 Week 1 Problem Set For more course tutorials visit www.uophelp.com 1. For assistance with these calculations, see the Recommended Resources for Week One. Measurement issues. Data, even numerically code variables, can be one of 4 levels – nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, as this impacts the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data. Please list, under each label, the variables in our data set that belong in each group.. 2. The first step in analyzing data sets is to find some summary descriptive statistics for key variables. For salary, compa, age, Performance Rating, and Service; find the mean and standard deviation for 3 groups: overall sample, Females, and Males. You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. Note: Place data to the right, if you use Descriptive statistics, place that to the right as well: 3. What is the probability for a: a. Randomly selected person being a male in grade E?

6. BUS 308 Week 1 Quiz (New) For more course tutorials visit www.uophelp.com 1. Question : In statistical notation, M is to μ as s is to σ. Question 2. Question : A parameter refers to a sample characteristic. Question 3. Question : Data on the city from which members of a board of directors come represent interval data. Question 4. Question : In a frequency distribution such as a bell-shaped curve, what does the vertical height of the curve indicate?

7. BUS 308 Week 1 Quiz For more course tutorials visit www.uophelp.com 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?

8. BUS 307 Week 2 Quiz (Ash) For more course tutorials visit www.uophelp.com BUS 307 Week 2 Quiz (Ash)

9. BUS 307 Week 3 DQ 1 Forecasting Models (Ash) For more course tutorials visit www.uophelp.com BUS 307 Week 3 DQ 1 Forecasting Models Forecasting Models. From Chapter 9, answer Discussion Question 1: Which forecasting techniques do you think Ford should have used to forecast changes in the demand, supply, and price of palladium? Time series models? Causal models? Qualitative models? Justify your answer and respond to at least two of your classmates’ postings.

10. BUS 308 Week 2 DQ 1 Hypotheses For more course tutorials visit www.uophelp.com Discussion 2-1/Hypotheses What is a hypothesis test? Why do we need to use them to make decisions about relating sample results to the population; why can’t we just make our decisions by the sample value?

11. BUS 308 Week 2 DQ 2 Variation For more course tutorials visit www.uophelp.com Variation exists in virtually all parts of our lives. We often see variation in results in what we spend (utility costs each month, food costs, business supplies, etc.). Consider the measures and data you use (in either your personal or job activities). When are differences (between one time period and another, between different production lines, etc.) between average or actual results important? How can you or your department decide whether or not the observed differences over time are important? How could using a mean difference test help?

12. BUS 308 Week 2 Problem Set For more course tutorials visit www.uophelp.com Problem Set Week Two Complete the problems below and submit your work in an Excel document. Be sure to show all of your work and clearly label all calculations. All statistical calculations will use the Included in the Week Two tab of theEmployee Salary Data Set are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean. 1. Below are 2 one-sample t-test comparing male and female average salaries to the overall sample mean. Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female salaries? 2. Based on our sample data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other.

13. BUS 308 Week 2 Quiz (New) For more course tutorials visit www.uophelp.com 1. Question : What is the relationship between the power of a statistical test and decision errors? Question 2. Question : The desired sample depends on all of these factors except? Question 3. Question : What question does the z test answer? Question 4. Question : The desired sample size depends only the size of the population to be tested. Question 5. Question : Each different t-distribution is defined by which of the following?

14. BUS 308 Week 2 Quiz For more course tutorials visit www.uophelp.com 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?

15. BUS 308 Week 3 DQ 1 ANOVA For more course tutorials visit www.uophelp.com In many ways, comparing multiple sample means is simply an extension of what we covered last week. Just as we had 3 versions of the t-test (1 sample, 2 sample (with and without equal variance), and paired; we have several versions of ANOVA – single factor, factorial (called 2-factor with replication in Excel), and within- subjects (2-factor without replication in Excel). What examples (professional, personal, social) can you provide on when we might use each type? What would be the appropriate hypotheses statements for each example?

16. BUS 308 Week 3 DQ 2 Effect Size For more course tutorials visit www.uophelp.com Several statistical tests have a way to measure effect size. What is this, and when might you want to use it in looking at results from these tests on job related data?

17. BUS 308 Week 3 Problem Set For more course tutorials visit www.uophelp.com ASSIGNMENT WEEK 3 Complete the problems below and submit your work in an Excel document. Be sure to show all of your work and clearly label all calculations. All statistical calculations will use the (Note: Questions 1- 4 have additional elements to respond to below the analysis results.) 1. Last week, we found that the average performance ratings do not differ between males and females in the population. Now we need to see if they differ among the grades. Is the average performance rating the same for all grades? (Assume variances are equal across the grades for this ANOVA.) 2. While it appears that average salaries per grade differ, we need to test this assumption. Is the average salary the same for each of the grade levels? (Assume equal variances, and use the Analysis toolpak function ANOVA.) Use the input table to the right to list salaries under each grade level.

18. BUS 308 Week 4 DQ 1 Confidence Intervals For more course tutorials visit www.uophelp.com Discussion 4-1/Confidence Intervals Many people do not “like” or “trust” single point estimates for things they need measured. Looking back at the data examples you have provided in the previous discussion questions on this issue, how might adding confidence intervals help managers accept the results better? Why? Ask a manger in your organization if they would prefer a single point estimate or a range for important measures, and why? Please share what they say.

19. BUS 308 Week 4 DQ 2 Chi-Square Tests For more course tutorials visit www.uophelp.com Discussion 4-2/Chi-Square Tests Chi-square tests are great to show if distributions differ or if two variables interact in producing outcomes. What are some examples of variables that you might want to check using the chi-square tests? What would these results tell you?

20. BUS 308 Week 4 Problem Set For more course tutorials visit www.uophelp.com ASSIGNMENT WEEK 4 Let’s look at some other factors that might influence pay. Complete the problems below and submit your work in an Excel document. Be sure to show all of your work and clearly label all calculations. All statistical calculations will use the 1. Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender. Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1)? 2. Using our sample data, construct a 95% confidence interval for the mean salary difference between the genders in the population. How does this compare to the findings in week 2, question 2? 3. We found last week that the degrees compa values within the population. Do not impact compa rates. This does not mean that degrees are distributed evenly across the grades and genders. Do males and females have the same distribution of degrees by grade?

21. BUS 308 Week 4 Quiz (New) For more course tutorials visit www.uophelp.com 1. Question : The goodness of fit test null hypothesis states that the sample data does not match an expected distribution. Question 2. Question : Statistical significance in the Chi-square test means the population distribution (expected) is not the source of the sample (observed) data. Question 3. Question : While rejecting the null hypothesis for the goodness of fit test means distributions differ, rejecting the null for the test of independence means the variables interact. Question 4. Question : The null hypothesis for the test of independence states that no correlation exists between the variables.

22. BUS 308 Week 4 Quiz For more course tutorials visit www.uophelp.com 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?

23. BUS 308 Week 5 DQ 1 Correlation For more course tutorials visit www.uophelp.com Discussion 5-1/Correlation What results in your departments seem to be correlated or related to other activities? How could you verify this? Create a null and alternate hypothesis for one of these issues. What are the managerial implications of a correlation between these variables?

24. BUS 308 Week 5 DQ 2 Regression For more course tutorials visit www.uophelp.com Discussion 5-2/Regression At times we can generate a regression equation to explain outcomes. For example, an employee’s salary can often be explained by their pay grade, appraisal rating, education level, etc. What variables might explain or predict an outcome in your department or life? If you generated a regression equation, how would you interpret it and the residuals from it?

25. BUS 308 Week 5 Final Paper (2 Papers) For more course tutorials visit www.uophelp.com This tutorial contains 2 Different Papers The final paper provides you with an opportunity to integrate and reflect on what you have learned during the class. The question to address is: “What have you learned about statistics?” In developing your responses, consider – at a minimum – and discuss the application of each of the course elements in analyzing and making decisions about data (counts and/or measurements). The course elements include: • Descriptive statistics

26. BUS 308 Week 5 Problem Set For more course tutorials visit www.uophelp.com ASSIGNMENT WEEK 5 1. Create a correlation table for the variables in our (Use analysis ToolPak or StatPlus:mac LE function Correlation). a. Reviewing the data levels from week 1, what variables can be used in a Pearson’s Correlation Table (which is what Excel produces)? b. Place the table here. c. Using r= approximately .28 as the significant r value (at p = .05) for a correlation between 50 values, what variables are significantly related to salary? To compa? d. Looking at the above correlations – both significant or not – are there any surprises – by that I mean any relationships you expected to be meaningful and are not, and vice-versa? e. Does this information help us answer our equal pay for equal work question?

27. BUS 308 (New) Reading feeds the Imagination/Uophelpdotcom For more course tutorials visit www.uophelp.com