Week 2 Discussion 1 & 2 BUS 624/625
kingstonman41
BUS625Week2ResponseforDiscussion12.docxBUS 625 Week 2 Response for Discussion 1 & 2
Week 2 Discussion 1 Response
Guided Response: Your initial response should be a minimum of 300 words in length. Respond to at least two of your classmates by commenting on their posts. In your response, provide your own interpretation of their distribution graph. Note any differences between your classmate’s interpretation and your own. Though two replies are the basic expectation for class discussions, for deeper engagement and learning you are encouraged to provide responses to any comments or questions others have given to you. Continuing to engage with peers and the instructor will further the conversation and provide you with opportunities to demonstrate your content expertise, critical thinking, and real-world experiences with the discussion topics.
Below there are two of my classmate’s discussion that needs I need to response to their names are Kristopher Wentworth and Ashley Thiberville
This graph is a representation of single people versus married couples from the year 1950 to the year 2019. This information was gathered and presented by the U.S. Department of Commerce and the U.S. Census Bureau who have a good record of presenting accurate data and are highly credible. The U.S. Department of Commerce is responsible for promoting economic growth in the united states. The U.S. Census Bureau is an agency of the Federal government that is responsible for producing data about the people of America and the economy.
So, the graph that I chose to talk about is one showing the gap between how many people are married and how many people are single in the united states from 1950 - 2019. I chose this graph because it caught my attention right away because of the contrasting colors but also because of the information displayed. It is crazy to think that since 1950 the American population has more than doubled according to this graph and with the growing population, the numbers of married couples and singles rise too. However, if you look at the percentages of singles they haven't changed all too much. For example, the number of single Americans in 1950 was 37.3M and in 2019 it was 125.7M. Even with such a large population boom the percentage that was never married really hadn't changed going from 69% to 68%.
The presentation of this graph is excellent with the line graph being yellow and on a blue backdrop, it allows it to really stand out. The shape of the graph shows a sharp incline as the population in us explodes. Since this graph is focused on the single population of America it puts the focus on that with stats like "never been married, divorced, widowed" because there are multiple ways to be single and really only one way to be married.
The above histogram was compiled by the United States Census Bureau to show the rise of one-person households in the US. The Census Bureau is a branch of the Department of Commerce within the United States government and is a credible source.
The data is time series and shows a single quantitative variable that is measured in 10-year intervals from 1969 to 2019. This graph shows a steady increase of one-person households since 1969. The largest leap occurred from 1969 to 1979 with a 5.5% increase in the number of one-person households. From 1979 the growth rate steadily rose about 2% every 10 years up to 2019.
Shape, Center and Spread
The shape of the graph is unimodal, meaning there is no discernable mode to the graph (Sharp, Veaux & Velleman, 2019). The graph is also skewed slightly to the right, if folded along the center line the two sides would not be a mirror image of one another. The median of the histogram is 25.05% and a mean of 24.07% showing that the graph is skewed. Quartile 1 is 21.13 and Quartile 3 is 27 with an IQR of 5.87. While the graph is uniform it does show a steady increase in number of one-person households. The IQR shows that growth between Q1 and Q3 has been somewhat significant.
Sharpe, N. D., De Veaux, R. D., & Velleman, P. F. (2019). Business statistics (4th ed.). Retrieved from https://www.redshelf.com (Links to an external site.)
United States Census Bureau. “One Person Households on the Rise.” https://www.census.gov/library/visualizations/2019/comm/one-person-households.html
BUS 625 Week 2 Discussion 2 Response
Guided Response: Your initial response should be a minimum of 300 words in length. Respond to at least two of your classmates by commenting on their posts. In your response, identify the lurking variable in your classmate’s study, and explain how the lurking variable may cause a false correlation.
Below there are two of my classmate’s discussion that needs I need to response to their names are Hallie Minch and Robert Mcalexander
Correlation means association. It refers to the measure of the extent to which two variables are related. There are three different types of correlation.
Positive correlation: the relationship between two variables in which both variables either increase or decrease at the same time.
Negative correlation: a relationship between two variables in which one variable will increase due to a decrease in the other variable, or vice versa.
Zero correlation: occurs when there is no direct relationship between the two variables.
(McLeod, 2018)
A lurking variable is one other than x and y that simultaneously affects both variables of x and y. (Sharpe et al., 2019).
After doing some research and learning about correlation and all the different types along with variables and lurking variables, I discovered that one of the most common lurking variables seem to be time of year or weather. I saw several examples where each variable, x and y, both either increased or decreased together, due to the time of year or due to the year itself. While the two variables have absolutely nothing to do with one another, the graphs show similarities.
The example I used is the number of drownings rise at the same time that ice cream sales rise. This could be confusing because one might look at this data and think that because ice cream sales increased, more people are drowning. In reality, the time of year plays the role of the lurking variable. When the weather starts to get warmer throughout the year, more people usually start to swim, as well as buy more ice cream. The rising heat is the cause of the rise of both of these variables. (Kenton, 2019).
One thing to take away from these types of examples is that correlation does not necessarily mean causation. This is where false correlation stems from.
Good morning everyone,
Correlation is something that gets misconstrued a lot in this day and age. There are constant ideological battles in the media about certain taboo topics. It is so important to take a second look at the data and come into it with some sort of skepticism. One of the better lessons from chapter four was the concept of a lurking variable. This is “a variable other than x and y that simultaneously affects both variables, accounting for the correlation between the two.” (Sharpe 139) A very silly and common example of lurking variables would be something like the number of bear attacks in the United States and the overall sale of sunscreen. As you can see from the chart, I looks as though the more sun screen people in the US buy, the more bears seem to lash out and attack them. There could be multiple examples of lurking variables in this instance. One of these would be where people are getting attacked. Someone in Georgia is probably not running into a whole lot of grizzly bears, but might be buying a fair amount of sun block as the seasons change. It also doesn’t help that most bears hibernate in the winter months. This would help explain why attacks and sale of sun screen go down during the fall and winter months. Another lurking variable would be that when bears are most active, summer and fall, people are most active as well. There are going to be a lot more interaction between the two purely based on the time of the year. Hikers will be exploring mountains that would normally be covered in snow in the winter months. Bears would be out fattening up for winter because there is so much more food available to them in these months. The bear attacks have no real correlation to people buying sun block.
Sharpe, N. D., De Veaux, R. D., & Velleman, P. F. (2019). Business statistics (4th ed.). Retrieved from: https://platform.virdocs.com/r/s/0/doc/509177/sp/68046574/mi/291159423?cfi=%2F4%2F2%5BP7001015989000000000000000002D4D%5D%2F4%5BP7001015989000000000000000002D50%5D%2F1%3A0&menu=search&q=lurking
