BUS 308 Discussion Questions Week 1 – 5
BUS 308 Discussion Questions Week 1 – 5 (Summer 2016)
BUS 308 Week 1 Post Your Introduction – Discussion
On the first day of class, introduce yourself to your instructor and classmates by sharing a little about yourself. In addition, include in your introduction one of the options below:
- Your goals and experience with statistics and how it has affected some of the decisions you have made.
- A “startling” statistic that you find interesting and what the effect of that statistic might indicate. Be sure to cite the source of your statistic.
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BUS.308 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.)
BUS 308 Discussion 1-2/ Probability
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?
BUS-308 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?
BUS 308 Discussion 2-2/ 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 observed differences over time are important? How could using a mean difference test help?
BUS.308 Discussion 3-1/ ANOVA
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?
BUS 308 Discussion 3-2/ 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?
BUS-308 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.
BUS 308 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?
BUS.308 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?
BUS-308 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? BUS 308 Discussion Questions Week 1 – 5
ADDITIONAL INFORMATION;
Discuss Chi-Square Tests
Introduction
Chi-square tests are useful statistical methods for analyzing categorical data. The chi-square distribution is a special case of the normal distribution and has many uses in statistics. In this article, you’ll learn about how to use R to analyze chi-squared tests and some important factors for these tests.
Chi-Square Tests with R
The R language is a programming language that has been used for statistical analysis and data manipulation since the late 1980s. It is free and open source, so you can use it without paying any license fees or royalties.
R was created by Ross Ihaka, Robert Gentleman and Peter Langston at Auckland University in New Zealand in 1989 as a software package for statistical computing with focus on graphics capabilities; it was later extended to include symbolic differentiation (for which it has been used extensively), other mathematical functions (such as statistics) and plotting techniques. As of 2016 it had over 2 million users worldwide[1] including researchers at universities such as Harvard University[2] who have built large-scale machine learning models using R.[3][4]
R is an open source language, which means it is free to use and modify. This also means that there are many different versions of R available that are created by people all around the world.
Significant Factors for Chi-Square Tests
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Chi-Square Tests:
Chi-square tests are used to test the independence of two variables. A chi-square test is used to determine if a difference between two groups is statistically significant. The chi-square test also determines whether there is a significant difference between the actual frequency and expected frequency (expected value). Chi-square tests are frequently used in business and marketing because they provide useful insights into relationships between variables that may not be obvious at first glance.
The test can be used to determine whether a difference between two groups is statistically significant. A chi-square test is also used when you want to determine if certain types of individuals are more likely than others to exhibit a particular behavior or perform a specific task. In statistics, this is referred to as contingency tables.
Independence of Variables for Chi-Square Tests
In the Chi-Square test, the null hypothesis is that all of the variables are independent and this means that they do not share any common causes. The alternative hypothesis is that at least one of the variables is dependent on another variable. The test statistic for this type of analysis is called a chi-square statistic (also known as Pearson’s χ2). A chi-square distribution can be used to calculate this statistic from data set, which follows an F distribution with degrees of freedom equal to df = n – 1 – k + 1 where n = sample size; k = number of categories in your data set; df = degrees of freedom; and p(chi2) = probability function given by:
where P-value is the probability of obtaining a chi-square value more extreme than your observed value if there is no relationship between the variables. The degrees of freedom are the number of categories minus one and will be equal to k + 1, where k is the number of categories. For example, if you have three groups (eg, A, B and C) then df = 3 – 1 = 2. This means that there are two degrees of freedom.
Chi-square tests are useful statistical methods for analyzing categorical data.
The chi-square test is used to determine if there is a relationship between two variables. It can be used for both categorical or quantitative data, and it’s especially useful when you have several variables that have different levels.
Chi-square tests are also known as goodness of fit tests, because they compare your observed frequencies with the expected frequencies based on an assumption about how your data should look (for example, if you expect 10 people in one group and 50 in another). The result of this comparison will give you an indication of whether there really is an association between the two variables being tested.
Conclusion
These tests are useful statistical methods for analyzing categorical data. It can be used to test whether there is a relationship between two variables, as well as determine if one variable is more important than another in predicting an outcome.
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