- Description
QNT/275
STATISTICS FOR DECISION MAKING
The Latest Version A+ Study Guide
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QNT 275 All Participations Link
https://hwsell.com/category/qnt-275-participations/
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QNT 275 Week 1 Practice: Week 1 Discussion
Review the Discussion FAQs Module.
Choose one statistical tool that you read about this week.
Consider a decision you need to make at work or at home.
Explain how this tool will help you make that decision.
Note: This must be original; do not use examples from the internet.
Role of Statistics
Hi Class,
The statistical terms you read in the earlier chapters of the book may be new to many of you. In this week, you will be introduced to the ideas of research, data collection, analysis of data and information, and informed decision making. I would like you to start thinking about the role of business research in your companies or other business with which you are familiar.
Statistics plays an important role in our decision-making in our daily life, as you might notice. If you like to watch sports, you probably already have been quoting some of the statistics on a sport team, a player currently and historically. Often times, statistics bring information to you in its unique format, i.e. as data, then you absorb and understand it, finally you make a decision. As a statistician working at FDA, I do this every day. We review the statistical results and decide whether a drug is going to be safe and effective, and therefore “approveable” for use by the patients.
Do you have any examples on how statistics plays a role from your daily life or work place?
Sample and Population
What is the difference between a sample and a population?
QNT 275 Week 2 Practice: Week 2 Discussion
Review the Discussion FAQs Module.
Research the internet for an example of a pie chart or bar chart.
Post a copy along with its source.
Include a question regarding the chart for your classmates to respond to.
Respond to a classmate’s question.
Hi Class,
Which is the best measure of central tendency depends on the shape/distribution of the data. When data is skewed, a mean is probably not a good measure. For example, if we have a data like this 1, 100, 100, 100,100, 101,101,101. What would be the mean and the median, which one do you think is a better measure of central tendency? Below are the answers:
Mean=(1+100+100+100+100+101+101+101)/8=88
Median=100
As we see the value “1” skewed data. In this data set, median is a better measure to represent the central tendency.
Please chime in your thought!
QNT 275 Week 3 Practice: Week 3 Discussion
Review the Discussion FAQs Module.
Reference the Week 2 Case Study.
Respond to one of the following:
- Regarding requirement #4: What impact would an outlier have?
- Regarding requirement #5: Why is it important to look at dispersion? Why is standard deviation a better measure of dispersion than the range?
Week 3 assignment hints
- Hi Class,
- In case you didn’t see, I posted some hints related to Week 2 apply exercises in the “Questions/Comments/Discussions” activity forum. I encourage you to share your questions and learning to help each other!
Distribution
Why it is important to find the shape of data distribution before computing descriptive statistics? Do all variables follow normal distribution? Explain why or why not. Explain with examples.
QNT 275 Week 4 Practice: Week 4 Discussion
Review the Discussion FAQs Module.
Reference the Week 3 Case Study.
Respond to one of the following:
- Regarding requirement #2: What does the mean and standard deviation mean in terms of expected sales?
- Regarding requirement #2: How can this information be used to benefit the business?
- Regarding requirement #3: Can you conclude whether the data is normally distributed?
- Regarding requirement #4: What can be interpreted by that percentage?
- One-sided test vs. two-sided test
- Hi Class,
- Let me talk a little bit about one-sided and two-sided tests (note I use “side” and “tail” interchangeably sometimes – they mean the same thing). Whether the test or hypothesis should be one-sided (left or right) or two-sided depend on what we are interested in, i.e the research question. It is against the statistical principal if one looks (peeks) at the data and then determine the hypothesis, or the test statistics.
- For example, we are interested in if replacement with new appliances in the house can increase the house price (this is the research question), then the hypotheses are:
- H0: Replacement of new appliances will not increase house selling price
- Or price increment<=0
- H1: Replacement of new appliances will increase house selling price
- Or price increment >0
- This is a right-sided test, because in our research question, we don’t think (or care) that replacement of new appliances would have negative impact on the house price.
- For another example, we are interested in seeing if boys and girls have similar performance in the spelling test in a school. Some people might think girls are better, some might think the other way. So it’s better to have a two-sided test:
- H0: boys’ average score = girls’ average score
- H1: boys’ average score <> girls’ average score
- As a result we found out girls did better, but to plan a study in the beginning, it’s probably more appropriate to use a two-sided hypothesis.
- How do we know it’s one-sided or two sided, the cue is in the ALTERNATIVE.
- If there is a “>” sign, it’s one-sided and right-tailed test
- If there is a “<” sign, it’s one-sided and left-tailed test
- If there is an unequal sign “<>”, it’s two-sided.
Hypothesis Testing
Class, this week, we are jumping into one of the two things the inferential statistics is about : hypothesis testing. (the other one is estimation)
There are some new terms that we need to get familiar with. Let’s start with null and alternative hypotheses:
The null and alternative hypotheses put the research/business question in a form of statement so that they can be either “rejected” or “not rejected” with a statistical hypothesis testing. The null and the alternative
should be mutually exclusive. Sometimes people get confused about which statement they should put in the null hypothesis (H0) and the “opposite” in the alternative hypothesis (H1). In researches and business, it is the common practice that we put what we hope to prove/see in the alternative hypothesis statement. For example:
If the research question is “Is the drug effective?” then we use
H0: The drug is not effective
H1: The drug is effective
“Would advertising increase retail sales?” then
H0: Advertising does not increase retail sales
H1: Advertising increases retail sales
“Does the new teaching method improve Spanish score in students?”
Then
H0: The new method does not improve Spanish score in students.
H1: The new method improves Spanish score in students
It’s sort of like we would like to look for alternatives – “Change is always good” ?
Class, please feel free to chime in your thoughts!
QNT 275 Week 5 Practice: Week 5 Discussion
Review the Discussion FAQs Module.
Reference the Week 4 Case Study.
Respond to one of the following:
- Regarding requirement #2: What impacted the reps’ average weekly performance to be greater than the population mean?
- Regarding requirement #3: Considering your hypothesis statements, provide an example of a Type I and a Type II error.
- Regarding requirement #3: Considering the p-values, is a statistically significant difference between the two reps being considered for the manager’s position? Explain.
- Regarding requirement #3: Who would you recommend being promoted to Sales Manager: Rep A or Rep B? Why?
- Regarding requirement #4: Considering the outcome of the hypothesis test, is your new Sales Manager outperforming the sales force?
Time Series
Time flies! We are at the last week of the course! I wanted to take my chance to push in some discussions on time series, a special type of data that are collected over a course of time. We build model based on these data for predictions through observing different components: trend, cycle, seasonal, irregular. Here are some applications of time series data forecasting/prediction in the real world:
Sales forecasting
1) Declining sales
2) Seasonal peaks and valleys
Staffing requirements
1) Absenteeism
2) Contract or permanent employee
Budgeting
1) Revenue or expenditures over time
2) Annual appropriations for government agencies
Potential global market expansion
1) Sales force estimation
2) Potential market demand
Material requirements
1) Cost of goods sold
2) Matching production schedules with raw material requirements
Please chime in and share what you learned!
Choosing the right test
Hi Class,
It’s impossible for us to understand the variety of statistical tests that you are newly exposed in one week or even 5 weeks – one is enough to make our heads spinning 🙂 I would like to, however, share with you some articles about choosing the appropriate hypothesis test in a bigger picture. Here is the link http://www.diss-stat.com/choosing.pdf
The choice of statistical test depends on the research goal/question and the nature of the data collected. If we would like to find the association between two variables, we would calculate the correlation. If our goal is to compare the means between two groups, we use a T or Z test. If we want to compare two proportions, then we also perform a z-test. If we want to compare the means across multiple groups (more than 2), we use ANOVA test.
I do not expect that you understand them all, rather to give you an idea of it. Cheers!