THE ANSWERS FOR CHAPTER EXERCISES MQF1033

CHAPTER EXERCISES MQF1033

5. Explain the differences between qualitative and quantitative variables. Give an example of qualitative and quantitative variables.

Generally, in statistics study there are two types of variables such as qualitative and quantitative. What is meant of qualitative? Basically, qualitative variables are regarding about nonnumeric and categorical. A quantitative variable is the information is reported numerically. It is called a quantitative variable when the variable studied indicates how many and how much. When the data are qualitative, we are usually interested in how many or what percent in each category, but the quantitative data on contrary. Normally, quantitative variables are either discrete or continuous which is able to calculate. Typically, discrete variables result from counting whereas the continuous variables result from measuring. For the example of qualitative is a type of car and for the quantitative variables is the number of runner in the Berlin marathon.

6. Explain the difference between a sample and a population

The study of statistics is normally divided into two categories: descriptive statistics and inferential statistics. But the category which is explained about a sample and a population is inferential statistics. The main focus in inferential statistics is finding something about a population from a sample taken from that population. For instance, a recent study showed that 80 percent of football players in the world cup 2010 in South Africa have an outstanding stamina and other 20 percent of football players have a good stamina. What is meant of population and sample? Population is the entire of individuals or objects of interest or the measurements obtained from all individuals or objects of interest. Basically, anything could be a population. As I’ve mentioned before a sample taken from population, so that’s mean the sample is a proportion or part of the population of interest.

7. Explain the difference between a discrete and a continuous. Give an example of each not included in the text.

In study of statistics, a type of variable that regarding about numerically is called quantitative which is consists of discrete and continuous variables. Discrete variables can assume only certain values, and there are gap between values. There is no decimal in the discrete variables. An example of discrete variables is the number of students who obtained grade A in Business Statistics subject for 2010/2011 sessions. Typically, discrete variables result from enumeration. Continuous variables can be enumerated in decimal point. An example of continuous variables is the amount of currency exchange between Malaysia and the United States of America. Let’s RM 3.21 equal to 1.00 US dollars. Normally, continuous variables result from measuring.

10. A review solicits a large number of college undergraduates for information on the following variables: the name of their cell phone provider (AT&T, Verizon, and so on), the numbers of minutes used last month (200, 400, for example), and their satisfaction with the service (terrible, adequate, Excellent, and so forth). What is the data scale for each of these three variables?

a. the name of their cell phone provider ( AT&T, Verizon and so on)- the level of scale for this data is NOMINAL because it is categorically divide, there is no particular order to the name of their cell phone provider. College undergraduates can either arrange Verizon first rather than AT&T due to no natural order.

b. the numbers of minutes used last month (200, 400, for example)- the level of measurement for this variables is RATIO. It is because the variable is quantitative data. The two numbers is also meaningful. Let’s, Fitri used 200 minutes and Razak used 400 minutes last month, then Razak used twice as much as Fitri. The zero point is meaningful, let’s Ahmad didn’t make any phone call last month, then there is no minutes (0 minute) used by Ahmad last month .

c. their satisfaction with the service (terrible, adequate, Excellent and so forth) – the level of measurement for this data is ORDINAL. It is because data classifications are represented by sets of names (terrible, adequate, Excellent and so fotrth). Besides that, it has relative values; the data can be arranged and ranked into classes. However we are not able to determine the magnitude of the differences between groups.

13. Place these variables in the following classification tables, for each table, summarize your observation and evaluate if the results are generally true, for example, salary is reported as a continuous quantitative variable, it is also a continuous ratio scaled variable.

a. Salary

b. gender

c. Sales volume of MP3 players

d. Soft drink preference

e. Temperature

f. SAT scores

g. student rank in class

h. rating of finance professor

i. Number of home computers

Discrete variable		Continuous Variable
Qualitative	b. Gender c. Soft drink preference
Quantitative	f. SAT scores g. Student rank in class i. Number of home computers	a. Salary e. Temperature c. Sales Volume of MP3 players

Discrete		Continuous
Nominal	b. Gender
Ordinal	c. Soft drink preference h. Rating of a finance professor g. student rank in class
Interval	g. SAT scores	e. temperature
Ratio	i. Number of home computers	a. Salary b. Sales volume of MP3 players

p/s : All of these answers had been discussed