Quantitative reasoning is an application of mathematical concepts and skills to solve real problems. The mathematical concepts are formulated to enable users solve their day to day problems easily. Every single day each of us deals with a mathematical problem whether knowingly or unknowingly. In order to work perfectly in any profession, the quantitative reasoning techniques should be used. This helps in finding real life solutions competently.

Quantitative reasoning can be applied in order to get the variable by itself; in technical terms, you are isolating the variables. In everyday life, there are a lot of things one can do and which can be analyzed by quantitative reasoning. For example, each day one receives phone calls. The phone calls are either received at night or at day time. In other words, the phones calls can be before noon (AM) or after (PM).

One, therefore, can analyze this data. In this case, the phone calls I receive can be collected and tabulated below. The phone calls are for ten consecutive days from my mobile phone. They are recorded as received. These phone calls also included those known as missed calls. I did it for the reason that the calls could have been answered and, therefore, failure to include them would mean that those calls were not received.

The duration of the phone calls does not matter here since the data required is only to analyze their number. Considering the number of minutes will lead to another area of analysis. In my ten days of recording phone calls, I got the following data

Table 1

Number of Phone Calls Received by Day and Time

 Day AM PM 1 18 28 2 16 33 3 13 37 4 16 31 5 18 29 6 12 34 7 15 28 8 7 16 9 17 23 10 23 18

From the above table, one can realize that I received most of my phone calls  during the afternoons. This is due to my availability at this time of day. In the morning most people who usually call do not make a lot of phone calls since they are engaged in educational activities and work. As for me, I also do not have chance to answer phone calls in the morning since I am a student.

The data above can be analyzed further by what is normally referred to measures of central tendency. There are various measures of central tendency applied in statistical data. The most common of all include the mean or average, the mode, and the median. There are other complicated measures of central tendency such as the deviation, variance and standard deviation. One can illustrate the importance of each of the measures after calculating it. In most cases, the measures of central tendency are applied in statistical data. This is mostly done by scholars dealing with econometrics or mathematicians with a bias in statistics. The measures also play a major role for most of the business in the world since they use them to analyze the markets. The measures are, however, not .limited to those areas alone. There are also ones developed and used by other institutions like schools and hospitals for various reasons.

SLP 5

Every day there are a lot of things one can do which can be analyzed by quantitative reasoning. For example, each day one receives phone calls. The phone calls are either received at night or at day time. In other words, the phones calls can be before noon (AM) or after (PM).

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One, therefore, can analyze this data. In this case, the phone calls I receive can be collected and tabulated below. The phone calls are for ten consecutive days from my mobile phone. They are recorded as received. These phone calls also included those known as missed calls. I did it for the reason that the calls could have been answered and, therefore, failure to include them would mean that they were not received.

The duration of the phone calls does not matter here since the data required is only to analyze their number. Considering the number of minutes will lead to another area of analysis. In my ten days of recording phone calls I came up with the following data.

Table 2

Calculation of Quantitative Features by Days and Time

 Day AM x- mean X squared PM x- mean X squared 1 18 2.5 6.25 28 0 0 2 16 0.5 0.025 33 5 25 3 13 -1.5 2.25 37 9 81 4 16 0.5 0.25 31 3 9 5 18 2.5 6.25 29 1 1 6 12 3.5 12.25 34 5 25 7 15 -0.5 0.25 28 0 0 8 7 -8.5 72.25 16 -12 144 9 17 -1.5 2.25 20 -8 64 10 23 -7.5 56.25 18 -10 100

From the above table, one can realize that most of my phone calls were received during the afternoons. This is due to my availability at this time of day. In the morning most of the people who usually call do not make a lot of phone calls since they are engaged in educational activities and work. As for me, I also do not have chance to answer phone calls in the morning since I am a student.

In further analysis of the data, one can calculate many other things with the help of the given numbers. There are several quantitative features one can use in a bit to analyze some data or information. These include the mean or average, variance, standard deviation and many others. From the above table, we are going to find each of the mentioned measures of dispersion. First of all, one can find the mean of the observations. That is the mean of the number of phone calls per session (morning or evening).

From the above table, the calculated mean is 15.5 for morning calls and 28 for evening ones.

The mode of the data is 18 calls for all phone calls; still, the mode for morning calls is 16 and for afternoon calls is 28.

The median for morning calls is 16 phone calls, while that for evening or afternoon ones is 29 phone calls.

Variance for morning phone calls is 15.8275; the variance for afternoon calls is 44.9.

The standard deviation for the morning calls is 3.9784, while for the afternoon calls it is 6.9007.

From this measures of central tendency one can, therefore, choose the method that best suites his/her lifestyle. One is able to choose the best of the measures without having any doubt of the inaccurate figures or result. From my own experience, the standard deviation describes my lifestyle the best. In most cases, the number of phone calls I get in the morning is usually in the range of plus or minus four calls from the previous calls. I can, therefore, agree with this measure. This is also the same case with the number of phone calls I get in the evening. Their number usually differs with six calls at most.

In conclusion, measures of central tendency play a major role in any statistical data. However, one should be able to know which measure suites him or her the best. A good choice will mean better analysis and therefore a good understanding of situation.

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