MAT 540 WEEK 1 DQ 1 & 2 Responses needed

The Quantitative Analysis Approach [WLO: 1] [CLO: 1]

1. For this discussion, begin by reviewing the technique of quantitative analysis in your textbook. Then, keeping this technique in mind, read the following quotes:

· “Data do not give up their secrets easily. They must be tortured to confess.”—Jeff Hopper, Bell Labs

· “Statistics is a body of methods for learning from experience.”—Lincoln Moses

· “The time may not be very remote when it will be understood that for complete initiation as an efficient citizen of one of the new great complex worldwide states that are now developing, it is as necessary to be able to compute, to think in averages and maxima and minima, as it is now to be able to read and write.”—H.G. Wells

· “The coming century is surely the century of data.”—David Donoho (2000)

· “Another mistaken notion connected with the law of large numbers is the idea that an event is more or less likely to occur because it has or has not happened recently. The idea that the odds of an event with a fixed probability increase or decrease depending on recent occurrences of the event is called the gambler’s fallacy. For example, if Kerrich landed, say, 44 heads in the first 100 tosses, the coin would not develop a bias towards the tails in order to catch up! That’s what is at the root of such ideas as ‘her luck has run out’ and ‘he is due.’ That does not happen. For what it’s worth, a good streak doesn’t jinx you, and a bad one, unfortunately, does not mean better luck is in store.”―Leonard Mlodinow, The Drunkard’s Walk: How Randomness Rules Our Lives

· “A certain elementary training in statistical method is becoming as necessary for everyone living in this world of today as reading and writing.”―H.G. Wells, World Brain

· “The non-scientist in the street probably has a clearer notion of physics, chemistry and biology than of statistics, regarding statisticians as numerical philatelists, mere collector of numbers.”―Stephen Senn, Dicing with Death: Chance, Risk and Health

Quotes retrieved from  www.goodreads.com/quotes/search?utf8=%E2%9C%93&q=statistics&commit=Search (Links to an external site.)Links to an external site.

Based on the above quotes, along with this week’s assigned readings and Instructor Guidance, discuss why quantitative analysis is important for describing data sets and presenting distribution information.

Guided Response: Review the posts from your classmates, and respond to at least three, comparing your classmates’ reviews of quantitative analysis with your own.

Respond to Roland Manayon post

The Quantitative Analysis Approach

In statistics, there are two broad types of data variables can be collected and analyzed – qualitative and quantitative variables.  According to Lind, Marchal, and Wathen (2017), the difference between qualitative and quantitative variables is that qualitative variables are not numerical, nominal and ordinal data fall under this category, while quantitative variables are numerical.  

Analyzing each requires different approaches. Qualitative variables such as names or types of cars sold, location of sale, and names of dealerships can be grouped into mutually exclusive and collectively exhaustive classes showing the number of observations in each class i.e, frequency table, or with a relative class frequencyshowing the fraction of the total number of observations in each class (Lind, Marchal, & Wathen, 2017). Resulting analysis can be graphically presented on a bar or pie chart.

Quantitative analysis, on the other hand, utilizes frequency distribution and relative frequency distribution which can be derived by dividing each class frequencies by the total number of observations. Frequency distribution groups the quantitative data into mutually exclusive and collectively exhaustive classes showing the number of observations in each class (Lind, Marchal, & Wathen, 2017, Ch. 2, p. 26). To present quantitative data analysis, there are few graphical methods that can be employed including histogram, frequency polygon, cumulative frequency distribution, and cumulative relative frequency distribution.  

Reference

Lind, D. A., Marchal, W. G., & Wathen, S. A. (2017). Statistical techniques in business and economics. (17th ed.). Retrieved from http://connect.mheducation.com/class/

Respond to Jenifer Barber post

When one stops and thinks about how a business makes decisions that lead to favorable outcomes it may seem like they are often just lucky. However, in reality, while there is some luck, there is also a large amount of data behind many decisions. Take Starbucks and the release of the unicorn Frappicioun a few years ago. It may have seemed like a whimsical idea that blew up bigger than anyone could have expected especially given how fast it sold out in many areas making the company look like it was not prepared for the craze. However, when one looks deeper and finds out that the company had told its employees not to open the product or sample it before the release date and then launched a viral social media campaign, it begins to appears that they knew how popular it would be and created the craze themselves based on data they had accumulated.

It just goes to show that companies use quantitative analysis to make many decisions from marketing to hiring to budgeting. “You know your customers, have your pulse on the beat of the marketplace, know what you can get out of your staff and have innovative ideas for improving your product, processes and marketing methods.” (Ashe-Edmunds, n.d). Take Facebook as another example of a company that takes quantitative analysis and applies it to business and consumers. Have you ever gone to a shopping website or ran a google search then gone onto your FaceBook feed only to find ads for either the company whose site you just visited or for similar products? This is just another example of a company takes data and uses it to target consumers directly and how statistics surround us on a daily bases even if we do not fully understand the concepts or are afraid of the scary word statistics.

Reference

Ashe-Edmunds, S. (n.d). Quantitative Analysis for Business Decision-Making. Retrieved from HoustonChronicle.com: https://smallbusiness.chron.com/quantitative-analysis-business-decisionmaking-69027.html

Respond to Yasmin El Sherif post

Quantitative analysis is when the variable is reported as a numerical (Lind, Marchal, & Wathen, 2017). These variables can be discrete or continuous. Discrete variables take on only certain values with gaps between the possible values (Lind, Marchal, & Wathen, 2017). Most likely the discrete values are counted (Lind, Marchal, & Wathen, 2017). Continuous variables, on the other hand, take on any value within a particular selection (Lind, Marchal, & Wathen, 2017). The continuous variables are most likely measurable (Lind, Marchal, & Wathen, 2017). “Almost all quantitative variables are recorded on the ratio level of measurement. The ratio level is the “highest” level of measurement (Lind, Marchal, & Wathen, 2017).” The zero point and the ratio between the two numbers are significant (Lind, Marchal, & Wathen, 2017).

Looking at the first quote we see is “Data do not give up their secrets easily. They must be tortured to confess” - Jeff Hopper, Bell Labs. This explains one of the steps that are acquiring input data the quantitative analysis approach requires data to understand information and receiving this data takes time and research to clearly receive.

Lind, D. A., Marchal, W. G., & Wathen, S. A. (2017).  Statistical techniques in business and economics  (17th ed.). Retrieved from http://connect.mheducation.com/class/