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BUS 308 Week 1 DQ 1 Language
Numbers and measurements are the language of business.. Organizations look at results, expenses, quality levels, efficiencies, time, costs, etc. What measures does your department keep track of ? How are the measures collected, and how are they summarized/described? How are they used in making decisions? (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.)
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-1-DQ-1-Language BUS 308 Week 1 DQ 2 Levels
Managers and professionals often pay more attention to the levels of their measures (means, sums, etc.) than to the variation in the data (the dispersion or the probability patterns/distributions that describe the data). For the measures you identified in Discussion 1, why must dispersion be considered to truly understand what the data is telling us about what we measure/track? How can we make decisions about outcomes and results if we do not understand the consistency (variation) of the data? Does looking at the variation in the data give us a different understanding of results?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-1-DQ-2-Levels
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BUS 308 Week 1 DQ 1 Language
Numbers and measurements are the language of business.. Organizations look at results, expenses, quality levels, efficiencies, time, costs, etc. What measures does your department keep track of ? How are the measures collected, and how are they summarized/described? How are they used in making decisions? (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.)
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-1-DQ-1-Language BUS 308 Week 1 DQ 2 Levels
Managers and professionals often pay more attention to the levels of their measures (means, sums, etc.) than to the variation in the data (the dispersion or the probability patterns/distributions that describe the data). For the measures you identified in Discussion 1, why must dispersion be considered to truly understand what the data is telling us about what we measure/track? How can we make decisions about outcomes and results if we do not understand the consistency (variation) of the data? Does looking at the variation in the data give us a different understanding of results?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-1-DQ-2-Levels
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BUS 308 Week 1 Problem Set Week One
1. Using the Excel Analysis ToolPak function descriptive statistics, generate descriptive statistics for the salary data. Which variables does this function not work properly for, even though we have some excel generated results?
2. Sort the data by Gen or Gen 1 (into males and females) and find the mean and standard deviation for each gender for the following variables: sal, compa, age, sr and raise. Use the descriptive stats function for one gender and the Fx functions (average and stdev) for the other.
3a. Randomly selected person being a male in a specific grade?
3b. Randomly selected person being in a specific grade?
4a. The z score for each male salary, based on only the male salaries. Step 1: Mean and Standard deviation of male salaries
4b. The z score for each female salary, based on only the female salaries. Step 1: Mean and Standard deviation of female salaries
5a. The z score for each male compa, based on only the male compa. Step 1: Mean and Standard deviation of male compa
5b. The z score for each female compa, based on only the female compa. Step 1: Mean and Standard deviation of female compa
6. What conclusions can you make about the issue of male and female pay equality? Are all of the results consistent? If not, why not? Overall analysis
Grade wise Average raise (using Pivot)
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-1-Problem-Set-Week-One
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BUS 308 Week 1 Problem Set Week One
1. Using the Excel Analysis ToolPak function descriptive statistics, generate descriptive statistics for the salary data. Which variables does this function not work properly for, even though we have some excel generated results?
2. Sort the data by Gen or Gen 1 (into males and females) and find the mean and standard deviation for each gender for the following variables: sal, compa, age, sr and raise. Use the descriptive stats function for one gender and the Fx functions (average and stdev) for the other.
3a. Randomly selected person being a male in a specific grade?
3b. Randomly selected person being in a specific grade?
4a. The z score for each male salary, based on only the male salaries. Step 1: Mean and Standard deviation of male salaries
4b. The z score for each female salary, based on only the female salaries. Step 1: Mean and Standard deviation of female salaries
5a. The z score for each male compa, based on only the male compa. Step 1: Mean and Standard deviation of male compa
5b. The z score for each female compa, based on only the female compa. Step 1: Mean and Standard deviation of female compa
6. What conclusions can you make about the issue of male and female pay equality? Are all of the results consistent? If not, why not? Overall analysis
Grade wise Average raise (using Pivot)
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-1-Problem-Set-Week-One
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BUS 308 Week 1 Quiz
1. Technically, “statistic” refers to which?
A sample characteristic
A measure of variability
A population characteristic
A particular scale of data
2. The tails of a normal distribution never touch the abscissa.
True
False
3. The z-score indicates where an individual data value lies within the data set.
True
False
4. Which statement explains what inferential statistical analysis is? @Answer found in section 1.4 Describing or Inferring?, in Statistics for Managers
The process of describing the sample.
Understanding the population through the sample.
The accumulation of population characteristics.
Comparing dissimilar samples.
5. A parameter refers to a sample characteristic.
True
False
6 In statistical notation, M is to μ as s is to σ.
True
False
7. Which of the following defines standard deviation?
The difference between the highest and lowest values.
The arithmetic average of a set of values.
The average difference between a set of values and the mean.
The average of the highest and lowest values in a set.
8. Which of the following is NOT a measure of variability? @Answer found in section 1.5 Descriptive Statistics, in Statistics for Managers
The standard deviation
The range
The variance
The median
9. In a frequency distribution such as a bell-shaped curve, what does the vertical height of the curve indicate?
The different score values
The frequency of score occurrence
The probability of score occurrence
The normality of the distribution
10. A probability is found by dividing the number of possible outcomes (o) by the number of successes € = o/e.
True
False
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-1-Quiz
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BUS 308 Week 1 Quiz
1. Technically, “statistic” refers to which?
A sample characteristic
A measure of variability
A population characteristic
A particular scale of data
2. The tails of a normal distribution never touch the abscissa.
True
False
3. The z-score indicates where an individual data value lies within the data set.
True
False
4. Which statement explains what inferential statistical analysis is? @Answer found in section 1.4 Describing or Inferring?, in Statistics for Managers
The process of describing the sample.
Understanding the population through the sample.
The accumulation of population characteristics.
Comparing dissimilar samples.
5. A parameter refers to a sample characteristic.
True
False
6 In statistical notation, M is to μ as s is to σ.
True
False
7. Which of the following defines standard deviation?
The difference between the highest and lowest values.
The arithmetic average of a set of values.
The average difference between a set of values and the mean.
The average of the highest and lowest values in a set.
8. Which of the following is NOT a measure of variability? @Answer found in section 1.5 Descriptive Statistics, in Statistics for Managers
The standard deviation
The range
The variance
The median
9. In a frequency distribution such as a bell-shaped curve, what does the vertical height of the curve indicate?
The different score values
The frequency of score occurrence
The probability of score occurrence
The normality of the distribution
10. A probability is found by dividing the number of possible outcomes (o) by the number of successes € = o/e.
True
False
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-1-Quiz
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BUS 308 Week 2 DQ 1 t-Tests
In looking at your business, when and why would you want to use a one-sample mean test (either z or t) or a two-sample t-test? Create a null and alternate hypothesis for one of these issues. How would you use the results?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-2-DQ-1-t-Tests BUS 308 Week 2 DQ 2 Variation
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 variation is important? How could using a mean difference test help?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-2-DQ-2-Variation
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BUS 308 Week 2 DQ 1 t-Tests
In looking at your business, when and why would you want to use a one-sample mean test (either z or t) or a two-sample t-test? Create a null and alternate hypothesis for one of these issues. How would you use the results?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-2-DQ-1-t-Tests BUS 308 Week 2 DQ 2 Variation
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 variation is important? How could using a mean difference test help?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-2-DQ-2-Variation
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BUS 308 Week 2 Problem Set Week Two
The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)?
Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
1. Is either the male or female salary equal to the overall mean salary?
2. Are the male and female salaries statistically equal to each other?
3. Are the male and female compas equal to each other?
4. If the salary and compa mean tests in questions 3 and 4 provide different equality results, which would be more appropriate to use in answering the question about salary equity? Why?
5. What other information would you like to know to answer the question about salary equity between the genders? Why?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-2-Problem-Set-Week-Two
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BUS 308 Week 2 Problem Set Week Two
The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)?
Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
1. Is either the male or female salary equal to the overall mean salary?
2. Are the male and female salaries statistically equal to each other?
3. Are the male and female compas equal to each other?
4. If the salary and compa mean tests in questions 3 and 4 provide different equality results, which would be more appropriate to use in answering the question about salary equity? Why?
5. What other information would you like to know to answer the question about salary equity between the genders? Why?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-2-Problem-Set-Week-Two
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BUS 308 Week 2 Quiz
1. The one-sample t-test differs from the z-test in which way? @Answer found in section 4.3 The One-sample t-Test, in Statistics for Managers
There are no parameter values involved in a t-test.
The t-test is more sensitive to minor differences between sample and population.
With the t-test one can be confident of the normality of the data.
The t-test requires no <known> parameter standard error of the mean.
2. If a certifying agency raises the requirements for real estate agents, what sort of decision error is the agency protecting against?
Type I
Type II
Type III
Type IV
3. What is the alternate hypothesis in a problem where sales group two is predicted to be “. . . significantly less productive than sales group one?” @Answer found in sections 4.3 The One-sample t-Test and 4.4 Hypothesis Testing, in Statistics for Managers
HA: μ1 ≠ μ 2
HA: μ 1= μ 2
HA: μ 1> μ2
HA: μ 1< μ 2
4. Which of the following defines statistical significance?
The outcome is unlikely to have occurred by chance.
The outcome is important.
The outcome is unusual.
The outcome is one that can be explained by normal circumstances.
5. The standard error of the mean can be calculated by dividing μ by the square root of the number of values in the distribution.
True
False
6. How does variability in the distribution of sample means compare to variability in a population based on individual scores?
Samples tend to vary less than individual scores.
Samples exaggerate differences among scores.
Individual scores tend to be more stable over time than samples.
Sample means vary less than individual scores.
7. The z-test can be used to test mean differences even when the initial data set is not normally distributed.
True
False
8. What is the advantage of a one-tailed test over a two-tailed test? @Answer found in section 4.3 The One-sample t-Test, in Statistics for Managers
Less data variability in the groups involved.
Smaller critical values indicate significance.
Rejecting at HO = .05 involves less chance of error.
There are fewer calculations to make.
9. The z- test requires an estimate of the population standard deviation.
True
False
10. The standard error of the mean is actually the standard deviation of all of the means that make up the distribution of sample means.
True
False
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-2-Quiz
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BUS 308 Week 2 Quiz
1. The one-sample t-test differs from the z-test in which way? @Answer found in section 4.3 The One-sample t-Test, in Statistics for Managers
There are no parameter values involved in a t-test.
The t-test is more sensitive to minor differences between sample and population.
With the t-test one can be confident of the normality of the data.
The t-test requires no <known> parameter standard error of the mean.
2. If a certifying agency raises the requirements for real estate agents, what sort of decision error is the agency protecting against?
Type I
Type II
Type III
Type IV
3. What is the alternate hypothesis in a problem where sales group two is predicted to be “. . . significantly less productive than sales group one?” @Answer found in sections 4.3 The One-sample t-Test and 4.4 Hypothesis Testing, in Statistics for Managers
HA: μ1 ≠ μ 2
HA: μ 1= μ 2
HA: μ 1> μ2
HA: μ 1< μ 2
4. Which of the following defines statistical significance?
The outcome is unlikely to have occurred by chance.
The outcome is important.
The outcome is unusual.
The outcome is one that can be explained by normal circumstances.
5. The standard error of the mean can be calculated by dividing μ by the square root of the number of values in the distribution.
True
False
6. How does variability in the distribution of sample means compare to variability in a population based on individual scores?
Samples tend to vary less than individual scores.
Samples exaggerate differences among scores.
Individual scores tend to be more stable over time than samples.
Sample means vary less than individual scores.
7. The z-test can be used to test mean differences even when the initial data set is not normally distributed.
True
False
8. What is the advantage of a one-tailed test over a two-tailed test? @Answer found in section 4.3 The One-sample t-Test, in Statistics for Managers
Less data variability in the groups involved.
Smaller critical values indicate significance.
Rejecting at HO = .05 involves less chance of error.
There are fewer calculations to make.
9. The z- test requires an estimate of the population standard deviation.
True
False
10. The standard error of the mean is actually the standard deviation of all of the means that make up the distribution of sample means.
True
False
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-2-Quiz
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BUS 308 Week 3 DQ 1 ANOVA
In many ways, comparing multiple sample means is simply an extension of what we covered last week. What situations exist where a multiple (more than two) group comparison would be appropriate? (Note: Situations could relate to your work, home life, social groups, etc.). Create a null and alternate hypothesis for one of these issues. What would the results tell you?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-3-DQ-1-ANOVA BUS 308 Week 3 DQ 2 Effect Size
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?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-3-DQ-2-Effect-Size BUS 308 Week 3 Final Outline Draft
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-3-Final-Outline-Draft
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BUS 308 Week 3 DQ 1 ANOVA
In many ways, comparing multiple sample means is simply an extension of what we covered last week. What situations exist where a multiple (more than two) group comparison would be appropriate? (Note: Situations could relate to your work, home life, social groups, etc.). Create a null and alternate hypothesis for one of these issues. What would the results tell you?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-3-DQ-1-ANOVA BUS 308 Week 3 DQ 2 Effect Size
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?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-3-DQ-2-Effect-Size BUS 308 Week 3 Final Outline Draft
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-3-Final-Outline-Draft
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BUS 308 Week 3 Problem Set Week Three
1. Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.) Set up the input table/range to use as follows: Put all of the salary values for each grade under the appropriate grade label.
2. The factorial ANOVA with only 2 variables can be done with the Analysis ToolPak function 2-Way ANOVA with replication. Set up a data input table like the following:
3. Repeat question 2 for the compa values.
4. Pick any other variable you are interested in and do a simple 2-way ANOVA without replication. Why did you pick this variable and what do the results show?
5. What are your conclusions about salary equity now?
The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-3-Problem-Set-Week-Three
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BUS 308 Week 3 Problem Set Week Three
1. Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.) Set up the input table/range to use as follows: Put all of the salary values for each grade under the appropriate grade label.
2. The factorial ANOVA with only 2 variables can be done with the Analysis ToolPak function 2-Way ANOVA with replication. Set up a data input table like the following:
3. Repeat question 2 for the compa values.
4. Pick any other variable you are interested in and do a simple 2-way ANOVA without replication. Why did you pick this variable and what do the results show?
5. What are your conclusions about salary equity now?
The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-3-Problem-Set-Week-Three
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BUS 308 Week 4 DQ 1 Confidence Intervals
Earlier we discussed issues with looking at only a single measure to assess job-related results. Looking back at the data examples you have provided in the previous discussion questions on this issue, how might adding confidence intervals help managers understand results better?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-4-DQ-1-Confidence-Intervals BUS 308 Week 4 DQ 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?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-4-DQ-2-Chi-Square-Tests
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BUS 308 Week 4 DQ 1 Confidence Intervals
Earlier we discussed issues with looking at only a single measure to assess job-related results. Looking back at the data examples you have provided in the previous discussion questions on this issue, how might adding confidence intervals help managers understand results better?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-4-DQ-1-Confidence-Intervals BUS 308 Week 4 DQ 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?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-4-DQ-2-Chi-Square-Tests
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BUS 308 Week 4 Problem Set Week Four
1. Is the probability of having a graduate degree independent of the grade the employee is in?
2. Construct a 95% confidence interval on the mean service for each gender? Do they intersect?
3. Are males and females distributed across grades in a similar pattern?
4. Do 95% confidence intervals on the mean length of service for each gender intersect?
5. How do you interpret these results in light of our equity question?
The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-4-Problem-Set-Week-Four
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BUS 308 Week 4 Problem Set Week Four
1. Is the probability of having a graduate degree independent of the grade the employee is in?
2. Construct a 95% confidence interval on the mean service for each gender? Do they intersect?
3. Are males and females distributed across grades in a similar pattern?
4. Do 95% confidence intervals on the mean length of service for each gender intersect?
5. How do you interpret these results in light of our equity question?
The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-4-Problem-Set-Week-Four
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BUS 308 Week 4 Quiz
1. Compared to the ANOVA test, Chi-Square procedures are not powerful (able to detect small differences).
True
False
2. The percent confidence interval is the range having the percent probability of containing the actual population parameter.
True
False
3. The distribution for the goodness of fit test equals k-1, where k equals the number of categories.
True
False
4. The Chi-square test can be performed on categorical (nominal) level data.
True
False
5. A confidence interval is generally created when statistical tests fail to reject the null hypothesis – that is, when results are not statistically significant.
True
False
6. In confidence intervals, the width of the interval depends only on the variation within the data set.
True
False
7. For a two sample confidence interval, the interval shows the difference between the means.
True
False
8. If the confidence interval for mean differences contains a 0, the associated t-test would have shown a significant difference.
True
False
9. Statistical significance in the Chi-square test means the population distribution (expected) is not the source of the sample (observed) data.
True
False
10. The Chi-square test is very sensitive to small differences in frequency differences.
True
False
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-4-Quiz
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BUS 308 Week 4 Quiz
1. Compared to the ANOVA test, Chi-Square procedures are not powerful (able to detect small differences).
True
False
2. The percent confidence interval is the range having the percent probability of containing the actual population parameter.
True
False
3. The distribution for the goodness of fit test equals k-1, where k equals the number of categories.
True
False
4. The Chi-square test can be performed on categorical (nominal) level data.
True
False
5. A confidence interval is generally created when statistical tests fail to reject the null hypothesis – that is, when results are not statistically significant.
True
False
6. In confidence intervals, the width of the interval depends only on the variation within the data set.
True
False
7. For a two sample confidence interval, the interval shows the difference between the means.
True
False
8. If the confidence interval for mean differences contains a 0, the associated t-test would have shown a significant difference.
True
False
9. Statistical significance in the Chi-square test means the population distribution (expected) is not the source of the sample (observed) data.
True
False
10. The Chi-square test is very sensitive to small differences in frequency differences.
True
False
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-4-Quiz
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BUS 308 Week 5 DQ 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?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-5-DQ-1-Correlation BUS 308 Week 5 DQ 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?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-5-DQ-2-Regression BUS 308 Week 5 Final Paper
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-5-Final-Paper
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BUS 308 Week 5 DQ 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?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-5-DQ-1-Correlation BUS 308 Week 5 DQ 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?
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-5-DQ-2-Regression BUS 308 Week 5 Final Paper
http://entirecourse.com/course/BUS-308-Statistics-For-Managers/BUS-308-Week-5-Final-Paper
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