business and statistics research

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ovtoryy224

Business Finance

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Refer to attached readings and address the following in 1,050 words:

  • Describe the role and purpose of statistics.
  • Explain the primary types of statistics.
  • Contrast a population and a sample.
  • Compare the difference between a qualitative and quantitative variable. Provide one example of each.
  • Describe the four different levels of measurements. Give one example of each.


Please use APA Format and must be original. Thank you

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WHY STUDY STATISTICS? If you look through your university catalogue, you will find that statistics is required for many college programs. As you investigate a future career in accounting, economics, human resources, finance, or other business area, you will also discover that statistics is required as part of these college programs. So why is an education in statistics a requirement in so many disciplines? LO1-1 Explain why knowledge of statistics is important. A major driver of the requirement for statistics knowledge is the technologies available for capturing data. Examples include the technology that Google uses to track how Internet users access websites. As people use Google to search the Internet. Google records every search and then uses these data to sort and prioritize the results for future Internet searches. One recent estimate indicates that Google processes 20,000 terabytes of information per day. Big-box retailers like Target Walmart, Kroger, and others scan every purchase and use the data to manage the distribution of products, to make decisions about marketing and sales, and to track daily and even hourly sales. Police departments collect and use data to provide city residents with maps that communicate information about crimes committed and their location. Every organization is collecting and using data to develop knowledge and intelligence that will help people make informed decisions, and to track the implementation of their decisions. The graphic to the left shows the amount of data generated every minute (www.domo.com). A good working knowledge of statistics is useful for summarizing and organizing data to provide information that is useful and supportive of decision making. Statistics is used to make valid comparisons and to predict the outcomes of decisions. Best Business Climate 1. Texas 2. Utah 3. Virginia 4. Florida 5. Louisiana 6. Indiana 7. South Carolina 8. Tennessee 9. Georgia 10. Nebraska In summary, there are at least three reasons for studying statistics: (1) data are collected everywhere and require statistical knowledge to make the information useful. (2) statistical techniques are used to make professional and personal decisions, and (3) no matter what your career, you will need a knowledge of statistics to understand the world and to be conversant in your career. An understanding of statistics and statistical method will help you make more effective personal and professional decisions. Page LO1-2 Define statistics and provide an example of how statistics is applied. WHAT IS MEANT BY STATISTICS? This question can be rephrased in two, subtly different ways: what are statistics and what is statistics? To answer the first question, a statistic is a number used to communicate a piece of information. Examples of statistics are: . The inflation rate is 2% . Your grade point average is 3.5. . The price of a new Tesla premium electric sedan is $85.400. TYPES OF STATISTICS LO1-3 Differentiate between descriptive and inferential statistics. When we use statistics to generate information for decision making from data, we use either descriptive statistics or inferential statistics. Their application depends on the questions asked and the type of data available. Descriptive Statistics Masses of unorganized data—such as the census of population, the weekly earnings of thousands of computer programmers, and the individual responses of 2.000 registered voters regarding their choice for president of the United States—are of little value as is. However, descriptive statistics can be used to organize data into a meaningful form. We define descriptive statistics as: DESCRIPTIVE STATISTICS Methods of organizing, summarizing, and presenting data in an informative way. The following are examples that apply descriptive statistics to summarize a large amount of data and provide information that is easy to understand . There are a total of 46,837 miles of interstate highways in the United States. The interstate system represents only 1% of the nation's total roads but carries more than 20% of the traffic. The longest is 1-90, which stretches from Boston to Seattle, a distance of 3,099 miles. The shortest is 1-878 in New York City, which is 0.70 mile in length. Alaska does not have any interstate highways. Texas has the most interstate miles at 3,232, and New York has the most interstate routes with 28. • The average person spent $103.00 on traditional Valentine's Day merchandise in 2013. This is an increase of $0.50 from 2012. As in previous years, men spent nearly twice the amount women spent on the holiday. The average man spent $135.35 to impress the people in his life while women only spent $72.28. Family pets also feel the love; the average person spent $3.27 on his or her furry friends, up from $2.17 last year. Statistical methods and techniques to generate descriptive statistics are presented in Chapters 2 and 4. These include organizing and summarizing data with frequency distributions and presenting frequency distributions with charts and graphs. In addition, statistical measures to summarize the characteristics of a distribution are discussed in Chapter 3. Page 5 Inferential Statistics Sometimes we must make decisions based on a limited set of data. For example, we would like to know the operating characteristics, such as fuel efficiency measured by miles per gallon, of sport utility vehicles (SUVs) currently in use. If we spent a lot of time, money, and effort, all the owners of SUVs could be surveyed. In this case, our goal would be to survey the population of SUV owners. POPULATION The entire set of individuals or objects of interest or the measurements obtained from all individuals or objects of interest. However, based on inferential statistics, we can survey a limited number of SUV owners and collect a sample from the population. SAMPLE A portion, or part of the population of interest. Samples are often used to obtain reliable estimates of population parameters. (Sampling is discussed in Chapter 8). In the process, we make trade-offs between the time, money, and effort to collect the data and the error of estimating a population parameter. The process of sampling SUVs is illustrated in the following graphic. In this example, we would like to know the mean or average SUV fuel efficiency. To estimate the mean of the population, six SUVs are sampled and the mean of their MPG is calculated. TYPES OF VARIABLES There are two basic types of variables: (1) qualitative and (2) quantitative (see Chart 1-2). When an object or individual is observed and recorded as a nonnumeric characteristic, it is a qualitative variable or attribute. Examples of qualitative variables are gender, beverage preference, type of vehicle owned, state of birth, and eye color. When a variable is qualitative, we usually count the number of observations for each category and determine what percent are in each category. For example, if we observe the variable eye color, what percent of the population has blue eyes and what percent has brown eyes? If the variable is type of vehicle, what percent of the total number of cars sold last month were SUVs? Qualitative variables are often summarized in charts and bar graphs (Chapter 2). Types of Variables Qualitative Quantitative Discrete Continuous • Brand of PC • Marital status • Hair color • Children in a family • Strokes on a golf hole • TV sets owned • Amount of income tax paid • Weight of a student • Yearly rainfall in Tampa, FL CHART 1-2 Summary of the Types of Variables When a variable can be reported numerically, the variable is called a quantitative variable. Examples of quantitative variables are the balance in your checking account, the ages of company presidents, the life of a car battery (such as 42 months), and the number of people employed by a company. Page 7 Quantitative variables are either discrete or continuous. Discrete variables can assume only certain values, and there are "gaps between the values. Examples of discrete variables are the number of bedrooms in a house (1. 2. 3. 4. etc.), the number of cars arriving at Exit 25 on 1-4 in Florida near Walt Disney World in an hour (326, 421, etc.), and the number of students in each section of a statistics course (25 in section A, 42 in section B. and 18 in section C). We count, for example, the number of cars arriving at Exit 25 on 1-4, and we count the number of statistics students in each section. Notice that a home can have 3 or 4 bedrooms, but it cannot have 3.56 bedrooms. Thus, there is a gap between possible values. Typically, discrete variables are counted. Observations of a continuous variable can assume any value within a specific range. Examples of continuous variables are the air pressure in a tire and the weight of a shipment of tomatoes. Other examples are the ounces of raisins in a box of raisin bran cereal and the duration of flights from Orlando to San Diego. Grade point average (GPA) is a continuous variable. We could report the GPA of a particular student as 3.2576952. The usual practice is to round to 3 places—3.258. Typically, continuous variables result from measuring. LEVELS OF MEASUREMENT Data can be classified according to levels of measurement. The level of measurement determines how data should be summarized and presented. It will also indicate the type of statistical analysis that can be performed. Here are two examples of the relationship between measurement and how we apply statistics. There are six colors of candies in a bag of M&Ms. Suppose we assign brown a value of 1. yellow 2. blue 3, orange 4. green 5, and red 6. What kind of variable is the color of an M&M? It is a qualitative variable. Suppose someone summarizes M&M color by adding the assigned color values, divides the sum by the number of M&Ms, and reports that the mean color is 3.56. How do we interpret this statistic? You are correct in concluding that it has no meaning as a measure of M&M color. As a qualitative variable, we can only report the count and percentage of each color in a bag of M&Ms. As a second example, in a high school track meet there are eight competitors in the 400-meter run. We report the order of finish and that the mean finish is 4.5. What does the mean finish tell us? Nothing! In both of these instances, we have not used the appropriate statistics for the level of measurement. There are four levels of measurement: nominal, ordinal, interval, and ratio. The lowest, or the most primitive, measurement is the nominal level. The highest is the ratio level of measurement. Nominal-Level Data For the nominal level of measurement, observations of a qualitative variable are measured and recorded as labels or names. The labels or names can only be classified and counted. There is no particular order to the labels. NOMINAL LEVEL OF MEASUREMENT Data recorded at the nominal level of measurement is represented as labels or names. They have no order. They can only be classified and counted. The classification of the six colors of M&M milk chocolate candies is an example of the nominal level of measurement. We simply classify the candies by color. There is no natural order. That is, we could report the brown candies first, the orange first, or any of the other colors first. Recording the variable gender is another example of the nominal level of measurement. Suppose we count the number of students entering a football game with a student ID and report how many are men and how many are women. We could report Page 8 either the men or the women first. For the data measured at the nominal level, we are limited to counting the number in each category of the variable. Often, we convert these counts to percentages. For example, a study of the color of M&M candies reports the following results (www.sensationalcolor.com/color-trends/most-popular-colors-177/mam-colors.html): Color Blue Green Orange Yellow Red Brown Percent in a bag 24% 20 16 14 13 13 To process the data for a variable measured at the nominal level, we often numerically code the labels or names. For example, if we are interested in measuring the home state for students at East Carolina University, we would assign a student's home state of Alabama a code of 1, Alaska a code of 2. Arizona a 3. and so on. Using this procedure with an alphabetical listing of states, Wisconsin is coded 49 and Wyoming 50. Realize that the number assigned to each state is still a label or name. The reason we assign numerical codes is to facilitate counting the number of students from each state with statistical software. Note that assigning numbers to the states does not give us license to manipulate the codes as numerical information. Specifically, in this example, 1 + 2 = 3 corresponds to Alabama + Alaska = Arizona. Clearly Bookmarks 1 level of measurement does not permit any mathematical operation that has any valid interpretation. Ordinal-Level Data The next higher level of measurement is the ordinal level. For this level of measurement a qualitative variable or attribute is either ranked or rated on a relative scale. ORDINAL LEVEL OF MEASUREMENT Data recorded at the ordinal level of measurement is based on a relative ranking or rating of items based on a defined attribute or qualitative variable. Variables based on this level of measurement are only ranked or counted. Best Business Climate 1. Texas 2. Utah 3. Virginia 4. Florida 5. Louisiana 6. Indiana 7. South Carolina 8. Tennessee 9. Georgia 10. Nebraska For example, many businesses make decisions about where to locate their facilities; in other words, where is the best place for their business? Business Facilities (www.businessfacilities.com) publishes a list of the top 10 states for the best business climate." The 2012 rankings are shown to the left. They are based on the evaluation of 20 different factors, including the cost of labor, business tax climate, quality of life, transportation infrastructure, educated workforce, and economic growth potential to rank states based on the attribute "best business climate." This is an example of an ordinal scale because the states are ranked in order of best to worst business climate. That is, we know the relative order of the states based on the attribute. For example, in 2012 Texas had the best business climate. Louisiana was fifth, and that was better than South Carolina but not as good as Virginia. Notice that we cannot say that Texas's business climate is five times better than Louisiana's business climate because the magnitude of the differences between the states is not known. Page 9 Another example of the ordinal level measure is based on a scale that measures an attribute. This type of scale is used when students rate instructors a variety of attributes. One attribute may be: "Overall, how do you rate the quality of instruction in this class?" A student's response is recorded on a relative scale of inferior, poor, good, excellent, and superior. An important characteristic of using a relative measurement scale is that we cannot distinguish the magnitude of the differences between groups. We do not know if the difference between "Superior" and "Good" is the same as the difference between "Poor" and "Inferior." Table 1-1 lists the frequencies of student ratings of instructional quality for Professor James Brunner in an Introduction to Finance course. The data are summarized based on the order of the scale used to rate the instructor. That is, they are summarized by the number of students who indicated a rating of superior (6) good (28), and so on. We can also convert the frequencies to percentages. About 37.8% of the students rated the instructor as good. TABLE 1-1 Rating of a Finance Professor Frequency 6 Rating Superior Good Average Poor Inferior 28 25 12 3
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Explanation & Answer

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Running head: BUSINESS AND STATISTICS RESEARCH

Research
Institution:
Date:

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BUSINESS AND STATISTICS RESEARCH

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While carrying out research, a researcher collects data through various methods. The data
collected is analyzed to come up with conclusions. To analyze data, statistics is used. There are
two primary types of statistics which are mostly used. This paper, therefore, seeks to explore the
two primary types of statistics and their roles and purposes, give the difference between
population and a sample, qualitative and quantitative variables as well as describe the levels of
measurements which are ordinal, nominal, interval and ratio level of measurement.
The science in which data is collected, scrutinized and inferences made from the same
data is called statistics. Ideally, statistics is a paramount branch of mathematics which is studied
theoretically by advanced mathematicians as well as used by researchers to come up with an
organized, analyzed and summarized information. To this end, statistics has many roles and
purposes. For example, statistics is useful in any field of study as it gives proper and efficient
planning of the data. For instance, in social sciences, a researcher carrying out a research about
the effects of poverty on a particular community uses statistics to plan and accurately analyze the
data before presenting it. This means that statistics has an imperative role in the field of research.
Additionally, statistics helps in understanding nature phenomenon in a better way. This is
because of the way statistics presents the descriptions o...


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