In this paper Team B will examine the data that we have collected and we will draw a conclusion based on your findings, to get to the conclusion we will analyze the data using descriptive statistics, we will calculate the measures of central tendency, and dispersion, we will also show all the information with graphics and tables for a better understanding of the date, after all these steps are executed we will draw our conclusion. Data Analysis Using Descriptive Statistics
Through this paper, Team B will use the philosophy of descriptive statistics to describe the main feature of the collection of data quantitatively (Main PS 1995).
Descriptive statistics will provide simple summaries about the samples and the measures. In essence, we will show a summary of the data that the team has collected. Some of the ways that we will show the data collected will be in the form of first central tendency which is the distribution locates the center of a distribution of values.
The three types of the central tendency are the mean, the median and the mode. Secondly, we will look at the dispersion which is the spread of values around the central tendency (Dodge 2003). The standard deviation is a more accurate way of measuring the dispersion. Thirdly, the use of graphics and tables to illustrate the data collected in a visual background. Fourthly there is the frequency distribution. In the frequency distribution is a summary of the ranges of values.
Frequency distribution can be shown by the team in graphic or tables that will be presented.
Finally the use of the histogram will be depicted in the team analysis of the data that was collected. |Descriptive statistics | | | | | | |# 1 | |count |11 | |Central Tendency |1,918,494. 273 | |sample variance |340,018,702,631. 6180 | |sample standard deviation |583,111. 2266 | | | | |skewness |-0. 2082 | | | | | | | | | |1989 |512,930 | | | | |1990 |578,930 | | | | |1991 |891,188 | | | | |1992 |1,084,408 | | | | |1993 |1,120,254 | | | | |1994 |1,188,679 | | | | |1995 |1,071,029 | | | |1996 |1,176,967 | | | | |1997 |1,383,578 | | | | |1998 |1,441,406 | | | | |1999 |1,720,050 | | | | |2000 |1,988,034 | | | | |2001 |2,264,403 | | | | |2002 |2,383,235 | | | | |2003 |2,555,476 | | | | |2004 |2,486,609 | | | | |2005 |2,632,655 | Central Tendency and Dispersion
The best measures of central tendency and dispersion based on the skew value and histogram are the mean, median, range, sample variance, and the sample standard deviation. By looking at the histogram, the median exceeds the mean that shows the data as negatively skewed. The mean is one of the best measures of central tendency because it is the most familiar and uses all the sample information. The median is a good measure of central tendency when extreme data values exist. The range is one of the best measures of dispersion because it is the difference between the largest and the smallest observation. The sample variance and the sample standard deviation are both a good measure of dispersion because they help to show how individual values in a data set vary from the mean.
The data collected by Team B and the sample size chosen does not seem to give a reasonable depiction of the skew for the histogram and distribution graph. Therefore, Team B can conclude that the information contained is biased and does not provide a proper skew coefficient and the information can be deduced as being biased. Team B included only two data points salary and winning teams. If Team B can increase the data samples then perhaps the frequency levels will conclude a proper outcome. Therefore, the results were uncertain for the data set shown and the hypothesis is uncertain. Reference: Mann PS (1995), Introductory Statistics, 2nd Edition, Wiley. Dodge, Y (2003), The Oxford Dictionary of Statistics Terms, OUP