...Central tendency and dispersion Upon performing data analysis using descriptive statistics, skew values and central tendencies, as well as, dispersion of data was visual evident. Central tendency is basically the cluster of data values while dispersion deals with the range of data values and how they are spread out. Additionally, dispersion reflects the amount of variation in the data. In the category of education, the central tendency focused on range of 10 – 12 years at 51 percent and 12 – 18 years slightly below but still significant at 39 percent. These values make up 90 percent of the data for this category. Regarding salary, dispersion of data played a significant role as the data was widely distributed ranging from $10,000 to $83,000 annually. The central tendency revolved around the range of $15K - $36K, for a total of 63 percent while greater than $36K made up 24 percent and less than $15K comprised 13 percent. Another area which illustrates central tendency and dispersion of data is age with a range in years of 18 to 64. Forty-seven percent fell in the age range of 32 – 53 while those younger than 32 represented 33 percent and those older than 53 characterized the lowest percentage for this category. Other demographic criteria such as Hispanic versus Non-white, Female, Southern Resident, Union, Married, and type of Industry were skewed due to limited options of variability with the exception of the Female category which had a balanced outcome of 47 percent female...
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...“Measure Of Central Tendency”? A particular value that attempt to illustrate a set of data by classifying a central position within the set of data is known as the measure of central tendency. The central tendency is distinguish with its dispersion or in other words the variability. It is important to summarize a set of data with a single value of data when interpreting and analyzing data, as it serves the purpose and give a representative value for the overall data. The most common measures of central tendency are the arithmetic mean, the median and the mode. A central tendency can be calculated for either a finite set of values or for a theoretical distribution, such as the normal distribution. Occasionally authors use central tendency to denote "the tendency of quantitative data to cluster around some central value." [1][2] Mean: It is also known as the arithmetic average, mean is the sum of value of all the observation in a set of data divided by the total number of the observations. Median: when values are arranged in a descending or ascending order, the middle value in the distribution is known as the median. Mode: It is the most repeated or commonly occurring value in the distribution. [1] Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP for International Statistical Institute. ISBN 0-19-920613-9 (entry for "central tendency") [2]Upton, G.; Cook, I. (2008) Oxford Dictionary of Statistics, OUP ISBN 978-0-19-954145-4 (entry for "central tendency") ...
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...The real estate market has changed since the time when “location, location, location” was the only factor affecting house pricing. Today potential home buyers take into considerations different variables ,such as number of bedrooms, square footage and distance from the city.. The variable that team B found to be the most important when it comes to buying a home is; home selling price, number of bedrooms, and location. For all of the chosen variables, Team b calculated the measures of central tendency and dispersion. Team b did the calculations on the two most common measures of central tendency, the mean, and median. In the real estate data researched by team B, the team found that the average price that homebuyers are looking for starts at around $190,000 with a maximum price of $ 300.000. These are the prices that the average homebuyer is looking to pay. This gives a difference of $110,000 between the two prices. The mean or average price that buyers are looking for would be $200,000. According to "Trulia" (2012), In Houston Texas the average selling price for a single family home is between $ 111,000 to 136,000. The high end selling price on average for a single family home in Houston Texas is $173,000. The mean or average home selling price for a single family home in Houston Texas is $140,000. This price is still within the $190,000 to $300,000 range that most buyers are looking for. The variance in this data would be $ 973, 000,. The standard deviation would...
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...CENTRAL TENDENCY: Mean, Median, Mode New Statistical Notation • Σ : sigma – The symbol Σ means to sum (add) the scores Central Tendency What Is Central Tendency? • A score that indicates where the center of the distribution tends to be located. • Tells us about the shape and nature of the distribution. d b Measures of Central Tendency • Mode • Median • Mean The Mode • The most frequently occurring score. • Typically useful in describing central tendency when the scores reflect a nominal scale of measurement. l f The Mode • It does not make sense to take the average in nominal data. – Gender: 67 males --- 1 50 females ---- 2 14 13 14 14 10 15 13 12 17 15 13 14 11 14 14 15 13 15 Score S 17 16 15 14 13 12 11 10 f 1 0 4 6 4 1 1 1 What is the mode? N=18 Unimodal Distributions When Wh a polygon h l has one hump (such as on the normal curve) the distribution is called unimodal. 14 15 15 14 10 15 13 12 17 15 13 12 11 12 15 12 13 12 Score S 17 16 15 14 13 12 11 10 f 1 0 5 2 3 5 1 1 What is the mode? N=18 Bimodal Distributions When distribution Wh a di ib i has two scores that are most frequently occurring, it is called d bimodal. Example Score S 7 6 5 4 3 2 1 f 1 4 5 4 6 7 9 N=36 What is the mode? Uses of The Mode • In nominal data – Since we cannot use mean or median • Also in ordinal, interval or ratio data, along with mean and median ih d di Problems...
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...Integrated Managed Services, Inc for the Douglas Medical Center location decided to conduct a survey in regards to the research question asking, why the turnover rate jumped from averaging 55-60% to 64%. A survey was given to each employee of BIMS that support the Douglas Medical Center and of the 5,300 employees only 78 participated in the survey leaving a very low response rate or 17.3%. This immediately tells the observers that the morale of the employees is not very high and one can only hope that the information provided by the 78 employees helps to point to why they are feeling this way. The following information will show how the responses were computed using descriptive statistics in the form of tables, charts, measures of central tendency, and variability. Charts In regards to the charts created from the responses to the survey, some inferences can be made and some conclusions can be drawn. One such inference could be that since more than half of the employees participating in the survey replied that they do not enjoy working for BIMS, this could also be a realistic ratio for the whole staff as many did not feel that it was important enough to respond to the survey. Another inference could be that the managers and supervisors are not reacting to the scheduling needs of their employees from the beginning of their employment. This could be concluded because 51 of 78 responses were negative in regards to, if the employees felt their request for their desired shift...
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...Define the following terms: Descriptive statistics is the term to describe the main or basic features of a research study. Scales of measurement is the term to describe the four scales of measurements: Nominal, Ordinal, Interval, and Ratio. Measures of central tendency is the term to describe the mean, median, and mode. Frequency distributions is the term to describe the process in dividing the groups in the research. Correlation coefficient is the term to describe the Pearson’s r (strength of the relationship between two things). Effect size is the term to describe the strength of an event. Multiple regression is the term to describe the process of the prediction of one value, based on two or more other values. How are group means, percentages, and correlations used to describe research results? Group means is used to take the responses of two or more groups, and find the “middle ground” between them. Utilizing group percentages means that the researcher takes the number of participants and turns that into a percentage for descriptive purposes. Individual correlations are utilized when the researcher compares the individual based on two variables in the study. (Cozby, 2009) How can graphs be used to describe and summarize data? The researcher can put the gathered information together a graph, making it easier for the reader to understand the outcome of the study, visually. A researcher is studying reading rates in milliseconds per syllable. What scale...
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...Real Estate Research Process RES 341 / Research and Evaluation 1 March 28, 2011 Professor Real Estate Research Process Introduction Every individual whether they are aware of it or not, base their decision-making on some form of statistical data. Simple everyday decisions are made through rationalizing a problem or opportunity, forming a hypothesis, analyzing information, and determining a decision based on the gathered information. For the purpose of practicality, Team A has chosen real estate market data gathered from the website for the Statistical Techniques in Business and Economics (2008) textbook to formulate and define a chosen problem, attempt to delineate the purpose of the research into the variables that affect the problem, propose a hypothesis and its research conduction, and briefly discuss the remaining steps in the research process the team will follow to complete the final project. Purpose of the Research According to Sekaran (2003), knowing the purpose of the research being conducted is the first step to understanding and defining the problem and reaching a hypothesis. Articles of clothing are usually priced for the quality of the material, the amount of material being used, and the perhaps the brand of the maker. However, this common-sense rule does not apply in real estate all of the time. The real estate market has always been an intricate web of real estate agents, negotiations, and many confusing rules. Buyers may wonder why a one-bedroom...
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...the numerical values or responses are labels, categories or names. Some examples would be gender, religion, favorite color, etc. The key point about nominal measurement is that it does not imply any ordering among the responses. The responses are merely named uniquely. Ordinal level of measurement is the next level. Data that is noted or recorded at this level is ranked or counted. The data is arranged in some type of order however the difference between the data values cannot be determined or are meaningless. The differences in two ordinal scales cannot be assumed, they are only ranked or rated on a relative scale. Ordinal scales are usually non-numeric thoughts like satisfaction, happiness or discomfort. You can determine central tendency on a set of ordinal data using mode or median. Interval level of measurement is the next level and can be described as measurement based on a scale with a known unit of measurement. In interval measurement we are able to determine the order as well as the exact difference between the values. An example would be time because the increments are known, consistent and are measurable. The difference between interval values is meaningful, however, with interval measurement there is no “true zero.” Therefore, it is impossible to compute ratios with interval data,...
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...preference is over another, not how much more is preferred. Quantitative attributes are all measurable on interval scales, as any difference between the levels of an attribute can be multiplied by any real number to exceed or equal another difference. A highly familiar example of interval scale measurement is temperature with the Celsius scale. The thermometer represents equal volumes of mercury between each interval on the scale. The "zero point" on an interval scale is arbitrary; and negative values can be used. Other examples for everyday interval scales are SAT scores. Ratios scale measurements allow all the possible mathematical operations and data analysis and measures how many times greater one case is than another The central tendency of a variable measured at the interval level...
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...prevention department at Sierra View Medical Center, identify one example of descriptive statistics, identify one example of inferential statistics, explain data at each of the four levels of measurement and describe the advantages of accurate interpretation of statistical information to improve decision making. The purpose of infection prevention and control is to put into place policies and procedures that minimize the spread of infections, especially in the hospital setting. The primary function of infection prevention and control surveillance is to reduce the occurrence of infections by using risk factors and implementation of risk-risk reduction measures and the effectiveness of interventions (Centers for Disease Control, 2012). Central line associated blood stream infections, (CLABSI) and catheter related urinary tract...
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...Descriptive Statistics Pain Study “The kappa opioid nalbuphine produces gender-and dose-dependent analgesia and antianalgesia in patients with postoperative pain” was a study that was performed to observe gender-specific patient response to varied doses of nalbuphine, an opioid pain medication, following oral surgery (Gear, Miaskowski, Gordon, Paul, Heller, & Levine, 1999). In this study, the researchers asked participants to rate their pain on a 10 cm visual analog scale (VAS) just before drug administration to obtain a baseline measurement, and again at 20 minute intervals thereafter (Gear et al., 1999). The demographic characteristics and descriptive statistics of the 131 participants are provided in Table 1 of the study (Gear et al., 1999). To aid in interpretation of the data collected in the research experiment, the researchers provide the reader with information using both ratio and ordinal data measurements. The weight of the participants is given as a mean, or average, and is considered ratio measurement. This is important data because weight is a variable that is considered when calculating dosage requirements. For each dose of pain medication given, as well as the placebo, the weight in kilograms for both men and women is averaged in the table. The data appropriate and meaningful since the average weight of participants in each dose category is similar save for the weight differences between men and women. Ratio measurement is considered the highest...
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...TYPE OF DATA Times Series Data A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements Cross-Section Data Cross-sectional data, or a cross section of a study population, in statistics and econometrics is a type data collected by observing many subjects (such as individuals, firms, countries, or regions) at the same point of time, or without regard to differences in time. Analysis of cross-sectional data usually consists of comparing the differences among the subjects. For example, if we want to measure current obesity levels in a population, we could draw a sample of 1,000 people randomly from that population (also known as a cross section of that population), measure their weight and height, and calculate what percentage of that sample is categorized as obese. This cross-sectional sample provides us with a snapshot of that population, at that one point in time. Note that we do not know based...
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...Data From Water Park | Type of Guest | % of visitors during peak season | Singles under 25 | 25 | Singles over 25 | 12 | Couples under 25 | 6 | Couples over 25 | 18 | Family groups | 39 | Statistics Assignment Part 1 a) We have obtained the data from both hotel and water park on the basis of number of visitors categorised according to their age groups during peak season. The data has been graphically presented below to compare and see if water park is a suitable excursion place for holiday makers to stay at the hotel. Data From Hotel | Type of Guest | % of visitors during peak season | Singles under 25 | 17 | Singles over 25 | 9 | Couples under 25 | 3 | Couples over 25 | 20 | Family groups | 51 | Graphical representation of both the data obtained from hotel and the water park are given below; From the graphs above, it is clear that the number of visitors according to their categories are similar in both hotel and the water park. Both for hotel and water park data we see that good number of visitors in hotel as well as water park are couples and family group. The graphical representation for the number of visitors for both hotel and water park shows increasing order from couples under 25 to couples over 25 and then to family groups. Whereas, the graphical representation of visitors number who are single is declining for hotel and water park too. This shows that the water park is attraction point for couples and mainly family group...
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...statistics and the four levels of measurement. Within Mercy Medical Center, nursing care is centered around the use of evidence based research thus making it important to understand the basic use of statistics in evaluating research. Hospitals must monitor for the amount of infections which occur in various units continuously since an infection can adversly afffect a patients treatment and may possibly lead to death. One of the infections which can occur is central line acquired blood stream infections (CLABSI). The raw data that is gathered from CLABSI is an example of descriptive statistics. The term descriptive statistics is given to the analysis of data which helps to describe, show, or summarize data in a meaningful way so that patterns may emerge from the data making it easier to visualize the information (Lund Research, 2013). There are two types of statistics which are used to describe data, measures of central tendency and measures of spread. With measures of central tendency, this is a way of describing the central position of a frequency distribution for a collection of...
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...Descriptive statistics do not, however, allow us to make conclusions beyond the data we have analysed or reach conclusions regarding any hypotheses we might have made. They are simply a way to describe our data. There are two general types of statistic that are used to describe data: Measures of central tendency and Measures of spread. Description of variables and types of data given are as follows: StartSal – Numerical and can be treated as continuous. OnRoad Pct – Numerical and continuous StateU – Categorical and can be treated as nominal CISDegree – categorical and can be treated as nominal Stayed3yrs – Categorical, ordinal and coded Tenure – Numerical and continuous With the given variables, here are the descriptive statistics: Note for non numeric data variables, the descriptive analysis could not be computed. These are: StateU, CISDegree and Stayed3yrs. Variable | Mean | StDev | Variance | Sum | Minimum | Maximum | Range | StartSal | 37986.36 | 1927.05 | 3713503.50 | 2507100.00 | 33800.00 | 42300.00 | 8500.00 | OnRoadPct | 0.5206 | 0.1783 | 0.0318 | 34.3600 | 0.1600 | 1.1100 | 0.9500 | Tenture | 15.294 | 6.622 | 43.850 | 520.000 | 5.0 | 31.000 | 26.000 | The central tendencies for the two variables (StartSal and OnRoadPct) are as follows: StartSal: Mean = 37986.36, Mode = 36100.00 and Median = 37700.00. OnRoadPct : Mean = 0.5206, Mode = 36100.00 and...
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