BUS 308 ASH entire course Flashcards
ASH BUS 308 Entire Course NEW
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For more classes visit http://www.uopassignments.com BUS 308 Week 2 Problem Set BUS 308 Week 3 Problem Set (Anova) BUS 308 Week 4 Problem Set (Regression and Correlation) BUS 308 Week 5 Final Paper Statistics Reflection (2 Papers) BUS 308 Week 1 DQ 1 BUS 308 Week 1 DQ 2 BUS 308 Week 2 DQ 1 BUS 308 Week 2 DQ 2 BUS 308 Week 3 DQ 1 BUS 308 Week 3 DQ 2 BUS 308 Week 4 DQ 1 BUS 308 Week 4 DQ 2 BUS 308 Week 5 DQ 1 BUS 308 Week 5 DQ 2 BUS 308 Week 1 Quiz (2 Set) BUS 308 Week 2 Quiz (3 Set) BUS 308 Week 3 Quiz (3 Set) BUS 308 Week 4 Quiz (3 Set) BUS 308 Week 5 Quiz (3 Set)
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ASH BUS 308 Week 2 Quiz (3 Set) NEW
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Question 1. To find the z-score of a value, which Excel function could be used?
Question 2. Which statement is not true?
Question 3. What is the alternate hypothesis in a problem where sales group two is predicted to be “…significantly less productive than sales group one?”
Question 4. Using the T-test: Two-sample Assuming Equal Variances test, the output can provide…
Question 5. To find the normal curve probability of exceeding a specific z-score, which Excel function could be used?
Question 6. When interpreting the effect size, a high effect is shown by a value equal to or greater than”.
Question 7. Using the Data Analysis Descriptive Statistics tool, the output can provide the …
Question 8. Using alpha = .05, what is your decision if the p-value is 0.01 for a one-tail test?
Question 9. Which of the following defines statistical significance
Question 10. If the p-value is greater than (>) our decision criteria, alpha, then we reject the null hypothesis claim of no difference.
BUS 308 Week 2 Quiz Set 2
Question 1. Which statement is not true?
Question 2. If the p-value is less than (
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ASH BUS 308 Week 1 DQ 2 NEW
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DQ #2: Webliography
Post a question that you had related to the material this week. Conduct research to provide the answer to the question and provide the source.
ASH BUS 308 Week 1 DQ 2 NEW
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ASH BUS 308 Week 1 Quiz (2 Set) NEW
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Question 1. Calculating the median requires data of at least what level ?
Question 2. If sales data are reported in dollar values, what is the scale of the data ?
Question 3. Empirical probability is
Question 4. A probability is found by dividing the number of possible outcome (0) by the number of successes (e)P = o/e.
Question 5. Which of the following measure central tendency and includes data from every score?
Question 6. A parameter refers to a sample characteristic.
Question 7. The mean is ?
Question 8. The probability of two independent events occurring together equals the product of each of the individual event probabilities.
Question 9. Days of the week are considered what level of data ?
Question 10. Data on the ages of customers are ratio scale data.
BUS 308 Week 1 Quiz Set 2
Question 1. The probability of finding 100 defective products in a sample of 500 is 25%.
Question 2. In statistical notation, M is to µ as s is to σ.
Question 3. Empirical probability is
Question 4. The standard deviation measures the central tendency of the data set.
Question 5. The mean is?
Question 6. Data on the city from which members of a board of directors come from represent interval level data?
Question 7. The mean is the most frequently used measure of central location.
Question 8. Distances are considered an example of which data scale?
Question 9. The standard deviation of normally distributed data is equal to about 1/6 of the data set’s
Question 10. Calculating the median requires data of at least what level?
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ASH BUS 308 Week 1 DQ 1 NEW
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Part Two – Data Characteristics
Read Lecture One on descriptive data and review the Employee Data . Be sure to familiarize yourself with the different variables shown on the Data tab. In this course, we will be using the Employee Data and statistical tools to answer a single research question: In our BUS308 company, are the males and females paid equally for equal work?
Lecture One discusses different ways data values can be classified. In our data set for the equal pay for equal work assignment, students in the past have correctly identify the variable gender (coded M and F for male and female respectively) as nominal level data, but they often see gender1 (coded 0 and 1 for male and female respectively) as interval or ratio level data. Why? What could cause this wrong classification? What data do you use in your personal or professional lives that might suffer from not being correctly labeled/understood?
Part Three –Descriptive Statistics
Read Lecture Two on describing data sets and view The Role of Data & Analytics Today video (https://www.youtube.com/watch?v=fxroi4beKhE). Lecture Two discusses several different ways of summarizing a data set–central location, variability, etc. Often, business reports provide a mean or average value for some measure (such as average number of defects per production run). Why is the average alone not enough information to make informed judgements about the result? What other descriptive statistic should be included? Why? Can you illustrate this with an example from your personal or professional lives? (This should be started on Day 3.)
Part Four – Probability
Read Lecture Three on probability. Lecture Three introduces the idea of probability—a measure of how likely it is to get a particular outcome. Looking at outcomes as resulting from probabilities (somewhat random outcomes/selections) rather than fixed constants often changes the way we see things. How does considering the salary outcomes in our sample the result of a probabilistic sample rather than a completely accurate and precise reflection of the population change how we interpret the sample statistic outcomes? What results in your personal or professional lives could be viewed this way? What differences would this cause? Why?
Your responses should be separated in the initial post, addressing each part individually,
ASH BUS 308 Week 1 DQ 1 NEW Check this A+ tutorial guideline at http://www.uopassignments.com/bus-308-ash/bus-308-week-1-dq-1-recent For more classes visit http://www.uopassignments.com
ASH BUS 308 Week 2 DQ 2 NEW
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Post a question that you had related to the material this week. Conduct research to provide the answer to the question and provide the source
ASH BUS 308 Week 2 DQ 2 NEW
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Post a question that you had related to the material this week. Conduct research to provide the answer to the question and provide the source
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DQ #1: Hypothesis Testing / T-tests / F-test
Although the initial post is due on Day 5, you are encouraged to start working on it early, as it is a three-part discussion that should be completed in sequential order.
Part One – Hypothesis Testing
Read Lecture Four. Lecture Four starts out with the five-step procedure for hypothesis testing. What is this? What does it do for us? Why do we need to follow these steps in making a judgement about the populations our samples came from? What are the “tricky” parts of developing appropriate hypotheses to test? What examples can you suggest where this process might be appropriate in your personal or professional lives? (This should be started on Day 1.)
Part Two – T-tests
Read Lecture Five. Lecture Five illustrates several t-tests on the data set. What conclusions can you draw from these tests about our research question on equal pay for equal work? What is missing from these results to give us a complete answer to the question? Why? (This should be started on Day 3.)
Part Three – F-test
Read Lecture Six. Lecture Six introduces you to the F-test for variance equality. Last week, we discussed how adding a variation measure to reports of means was a smart thing to do. Why does variation make our analysis of the equal pay for equal work question more complicated? What causes of variation impact salary that we have not discussed yet? How can you relate this issue to measures used in your personal or professional lives? (This should be completed by Day 5.)
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ASH BUS 308 Week 3 DQ 2 NEW
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DQ #2: Webliography
Post a question that you had related to the material this week. Conduct research to provide the answer to the question and provide the source.
ASH BUS 308 Week 3 DQ 2 NEW
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ASH BUS 308 Week 2 Problem Set NEW
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Before starting this assignment, make sure the the assignment data from the Employee Salary Data Set file is copied over to this Assignment file. You can do this either by a copy and paste of all the columns or by opening the data file, right clicking on the Data tab, selecting Move or Copy, and copying the entire sheet to this file (Weekly Assignment Sheet or whatever you are calling your master assignment file).
It is highly recommended that you copy the data columns (with labels) and paste them to the right so that whatever you do will not disrupt the original data values and relationships.
To Ensure full credit for each question, you need to show how you got your results. For example, Question 1 asks for several data values. If you obtain them using descriptive statistics, then the cells should have an “=XX” formula in them, where XX is the column and row number showing the value in the descriptive statistics table. If you choose to generate each value using fxfunctions, then each function should be located in the cell and the location of the data values should be shown. So, Cell D31 - as an example - shoud contain something like “=T6” or “=average(T2:T26)”. Having only a numerical value will not earn full credit. The reason for this is to allow instructors to provide feedback on Excel tools if the answers are not correct - we need to see how the results were obtained.
In starting the analysis on a research question, we focus on overall descriptive statistics and seeing if differences exist. Probing into reasons and mitigating factors is a follow-up activity.
1 The first step in analyzing data sets is to find some summary descriptive statistics for key variables. Since the assignment problems will focus mostly on the compa-ratios, we need to find the mean, standard deviations, and range for our groups: Males, Females, and Overall. Sorting the compa-ratios into male and females will require you copy and paste the Compa-ratio and Gender1 columns, and then sort on Gender1.
The values for age, performance rating, and service are provided for you for future use, and - if desired - to test your approach to the compa-ratio answers (see if you can replicate the values).
You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. The range can be found using the difference between the =max and =min functions with Fx functions or from Descriptive Statistics.
Suggestion: Copy and paste the compa-ratio data to the right (Column T) and gender data in column U. If you use Descriptive statistics, Place the output table in row 1 of a column to the right. If you did not use Descriptive Statistics, make sure your cells show the location of the data (Example: =average(T2:T51)
A key issue in comparing data sets is to see if they are distributed/shaped the same.
At this point we can do this by looking at the probabilities that males and females are distributed in the same way for a grade levels.
2 Empirical Probability: What is the probability for a:
a. Randomly selected person being in grade E or above?
b. Randomly selected person being a male in grade E or above?
c. Randomly selected male being in grade E or above?
d. Why are the results different?
3 Normal Curve based probability: For each group (overall, females, males), what are the values for each question below?:
Make sure your answer cells show the Excel function and cell location of the data used. A The probability of being in the top 1/3 of the compa-ratio distribution.
Note, we can find the cutoff value for the top 1/3 using the fx Large function: =large(range, value).
Value is the number that identifies the x-largest value. For the top 1/3 value would be the value that starts the top 1/3 of the range,
For the overall group, this would be the 50/3 or 17th (rounded), for the gender groups, it would be the 25/3 = 8th (rounded) value. i. How nany salaries are in the top 1/3 (rounded to nearest whole number) for each group?
ii What Compa-ratio value starts the top 1/3 of the range for each group?
iii What is the z-score for this value?
iv. What is the normal curve probability of exceeding this score?
B How do you interpret the relationship between the data sets? What does this suggest about our equal pay for equal work question?
4 Based on our sample data set, can the male and female compa-ratios in the population be equal to each other?
A First, we need to determine if these two groups have equal variances, in order to decide which t-test to use.
What is the data input ranged used for this question:
Step 1:
Ho:
Ha:
Step 2:
Decision Rule:
Step 3:
Statistical test:
Why?
Step 4: C
Conduct the test - place cell B77 in the output location box.
Step 5: Conclusion and Interpretation
What is the p-value:
Is the P-value < 0.05 (for a one tail test) or 0.025 (for a two tail test)?
What is your decision:
REJ or NOT reject the null?
What does this result say about our question of variance equality?
B Are male and female average compa-ratios equal?
(Regardless of the outcome of the above F-test, assume equal variances for this test.)
What is the data input ranged used for this question:
Step 1:
Ho:
Ha:
Step 2: Decision Rule:
Step 3: Statistical test:
Why?
Step 4: Conduct the test - place cell B109 in the output location box.
Step 5: Conclusion and Interpretation
What is the p-value: Is the P-value < 0.05 (for a one tail test) or 0.025 (for a two tail test)?
What is your decision:
REJ or NOT reject the null?
What does your decision on rejecting the null hypothesis mean?
If the null hypothesis was rejected, calculate the effect size value:
If the effect size was calculated, what doe the result mean in terms of why the null hypothesis was rejected?
What does the result of this test tell us about our question on salary equality?
5 Is the Female average compa-ratio equal to or less than the midpoint value of 1.00?
This question is the same as:
Does the company, pay its females - on average - at or below the grade midpoint (which is considered the market rate)?
Suggestion: Use the data column T to the right for your null hypothesis value.
What is the data input ranged used for this question:
Step 1:
Ho:
Ha:
Step 2: Decision Rule:
Step 3: Statistical test: Why?
Step 4: Conduct the test - place cell B162 in the output location box.
Step 5: Conclusion and Interpretation
What is the p-value: Is the P-value < 0.05 (for a one tail test) or 0.025 (for a two tail test)?
What, besides the p-value, needs to be considered with a one tail test?
Decision: Reject or do not reject Ho?
What does your decision on rejecting the null hypothesis mean?
If the null hypothesis was rejected, calculate the effect size value:
If the effect size was calculated, what doe the result mean in terms of why the null hypothesis was rejected?
What does the result of this test tell us about our question on salary equality?
6 Considering both the salary information in the lectures and your compa-ratio information, what conclusions can you reach about equal pay for equal work?
Why - what statistical results support this conclusion?
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ASH BUS 308 Week 5 DQ 2 NEW
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What are common mistakes in linear regression analysis?
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What are common mistakes in linear regression analysis?
ASH BUS 308 Week 5 DQ 1 NEW Check this A+ tutorial guideline at http://www.uopassignments.com/bus-308-ash/bus-308-week-5-dq-1-recent For more classes visit http://www.uopassignments.com BUS 308 Week 5 DQ 1 NEW Part One – Confidence Intervals Read Lecture Thirteen. Lecture Thirteen introduces you to confidence intervals. What is a confidence interval, and why do some prefer them to single point estimates? Ask your manager what is preferred and why? What are the strengths and weaknesses of using confidence intervals in making decisions? (This should be started on Day 1.)
Part Two – Chi Square
Read Lecture Fourteen. As Lecture Fourteen notes, the chi-square test is—in some ways—fundamentally different than the previous tests we have looked at. In what ways and why is this approach important? Examples were shown of gender-degree distributions and employees per grade. How do these tests help with understanding our equal pay for equal work question? Do they change or reinforce our decision from last week? What situations in your personal or professional lives could use a chi-square approach?
Part Three – Overall Reactions
Has your opinion about statistics changed? How can statistical analysis help your professional career?
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ASH BUS 308 Week 5 Final Paper Statistics Reflection (2 Papers) NEW
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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 • Inferential statistics • Hypothesis development and testing • Selection of appropriate statistical tests • Evaluating statistical results.
Writing the Final Paper
The Final Paper:
- Must be three to- five double-spaced pages in length, and formatted according to APA style as outlined in the Ashford Writing Center.
- Must include a title page with the following:
a. Title of paper
b. Student’s name
c. Course name and number
d. Instructor’s name
e. Date submitted - Must begin with an introductory paragraph that has a succinct thesis statement.
- Must address the topic of the paper with critical thought.
- Must end with a conclusion that reaffirms your thesis.
- Must use at least three scholarly sources, in addition to the text.
- Must document all sources in APA style, as outlined in the Ashford Writing Center.
- Must include a separate reference page, formatted according to APA style as outlined in the Ashford Writing Center.
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ASH BUS 308 Week 4 Quiz (3 Set) NEW
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Question 1. The t Stat value is used to determine the statistical significance of each of the variables listed in a regression analysis.
Question 2. A correlation of .90 and above is generally considered too strong to be of any practical significance.
Question 3. A p-value of 9.22E-36 equals 0.00000000000000000000000000000000000922 and is less than .05
Question 4. If two variables are known to be correlated, it is possible to predict the value of y (dependent variable) from an x (independent) variable.
Question 5. When determining statistical significance of correlations, (as a rule of thumb), variable pairs with coefficients greater than (>) 70% are generally not very valuable for prediction purposes.
Question 6. Which statement does not belong?
Question 7. Pearson Correlation Coefficient is a mathematical value that shows the strength of the linear (straight line) relationship between two variables.
Question 8. A regression analysis uses two distinct types of data. The first are variables that are at least nominal level.
Question 9. The ANOVA table provides the Significance of F to use to see if we reject or fail to reject the null hypothesis of no significance. The Significance of F is also known as the P-value.
Question 10. When performing a regression analysis using the Regression option in Data Analysis, the input for the Y range is the independent variable (can generally control) and the input X range is for the dependent variables.
BUS 308 Week 4 Quiz Set 2
Question 1. When determining statistical significance of correlations, (as a rule of thumb), variable pairs with coefficients greater than (>) 70% are generally not very valuable for prediction purposes.
Question 2. A p-value of 9.22E-36 equals 0.00000000000000000000000000000000000922 and is less than .05
Question 3. Pearson Correlation Coefficient is a mathematical value that shows the strength of the linear (straight line) relationship between two variables.
Question 4. A Pearson correlation of +1.00 is considered a “perfect positive correlation”. This means….
Question 5. Spearman’s rank order correlation (rho) can be performed on ordinal or any ranked data.
Question 6. The t Stat value is used to determine the statistical significance of each of the variables listed in a regression analysis.
Question 7. Pearson’s Correlation requires at least interval level data.
Question 8. If two variables are known to be correlated, it is possible to predict the value of y (dependent variable) from an x (independent) variable.
Question 9. A correlation of .90 and above is generally considered too strong to be of any practical significance.
Question 10. When looking at a regression statistics table, Multiple R displays the percent of variation in common between the dependent and all of the independent variables.
BUS 308 Week 4 Quiz Set 3
Question 1. Pearson’s Correlation requires at least interval level data.
Question 2. A p-value of 9.22E-36 equals 0.00000000000000000000000000000000000922 and is less than .05
Question 3. When plotting variables on a scatter diagram, the variables plotted on the Y-axis is the horizontal axis and the X-axis is the vertical axis.
Question 4. If two variables are known to be correlated, it is possible to predict the value of y (dependent variable) from an x (independent) variable.
Question 5. When determining statistical significance of correlations, (as a rule of thumb), variable pairs with coefficients greater than (>) 70% are generally not very valuable for prediction purposes.
Question 6. A correlation of .90 and above is generally considered too strong to be of any practical significance.
Question 7. A Pearson correlation of +1.00 is considered a “perfect positive correlation”. This means….
Question 8. When looking at a regression statistics table, Multiple R displays the percent of variation in common between the dependent and all of the independent variables.
Question 9. Which statement does not belong?
Question 10. The t Stat value is used to determine the statistical significance of each of the variables listed in a regression analysis.
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ASH BUS 308 Week 4 Problem Set (Regression and Correlation) NEW
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Problem Set Week Four
This week we get to answer our equal pay for equal work question by looking at relationships between and among the different variables.
The first question this week looks at correlations and the creation of a correlation table for our variables. The second question asks for a regression equation showing how the different variables impact the compa-ratio measure. The third questions asks you to discuss the benefits of using a regression equation approach over the single variable tests we have been doing.
The forth question asks for what other information you would have liked to have analyzed in our research. The fifth question asks for your answer to the equal pay for equal work question of: Is the company paying fairly or not? If not, who benefits and why?
Regression and Corellation
Remember to show how you got your results in the appropriate cells. For questions using functions, show the input range when asked.
1. Create a correlation table using Compa-ratio and the other interval level variables, except for Salary.
Suggestion, place data in columns T - Y
a What range was placed in the Correlation input range box: Place C9 in output box.
b What are the statistically significant correlations related to Compa-ratio? T = Significant r =
c Are there any surprises - correlations you though would be significant and are not, or non significant correlations you thought would be?
d Why does or does not this information help answer our equal pay question?
2 Perform a regression analysis using compa as the dependent variable and the variables used in Q1 along with including the dummy variables. Show the result, and interpret your findings by answering the following questions. Suggestion: Place the dummy variables values to the right of column Y. What range was placed in the Regression input range box: Note: be sure to include the appropriate hypothesis statements.
Regression hypotheses
Ho:
Ha:
Coefficient hyhpotheses (one to stand for all the separate variables)
Ho:
Ha:
Place B36 in output box.
Interpretation: For the Regression as a whole:
What is the value of the F statistic:
What is the p-value associated with this value:
Is the p-value < 0.05?
What is your decision:
REJ or NOT reject the null?
What does this decision mean?
For each of the coefficients: Midpoint Age Perf. Rat. Service Gender Degree
What is the coefficient’s p-value for each of the variables: Is the p-value < 0.05?
Do you reject or not reject each null hypothesis:
What are the coefficients for the significant variables?
Using the intercept coefficient and only the significant variables, what is the equation?
Compa-ratio =
Is gender a significant factor in compa-ratio?
Regardless of statistical significance, who gets paid more with all other things being equal?
How do we know?
3 What does regression analysis show us about analyzing complex measures?
4 Between the lecture results and your results, what else would you like to know before answering our question on equal pay? Why?
5 Between the lecture results and your results, what is your answer to the question of equal pay for equal work for males and females? Why?
ASH BUS 308 Week 4 Problem Set (Regression and Correlation) NEW
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ASH BUS 308 Week 4 DQ 2 NEW
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DQ #2: Webliography
Post a question that you had related to the material this week. Conduct research to provide the answer to the question and provide the source.
ASH BUS 308 Week 4 DQ 2 NEW
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DQ #2: Webliography
Post a question that you had related to the material this week. Conduct research to provide the answer to the question and provide the source.
