...TABLE OF CONTENTS 1.0 Introduction 2.0 Individual Variable: Income 3.0 Individual Variable: Size 4.0 Individual Variable: Credit Balance($) 5.0 Pairing of Variables: Income($1000) vs. Years 6.0 Pairing of Variables: Credit Balance($) vs. Income($1000) 7.0 Pairing of Variables: Credit Balance($) vs. Location 8.0 Conclusion 9.0 References List 1.0 Introduction This report is done base on a sample of 50 credit customers with AJ DAVIS is selected with data collected base on five variables as following: • Location (Rural, Urban, Suburban) • Income (in $1,000's) • Size (Household Size, meaning number of people living in the household) • Years (the number of years that the customer has lived in the current location) • Credit Balance (the customers current credit card balance on the store's credit card, in $). To have more understand of what the data truly mean, three of individual variable and three of pairing variable are analyzed by using numerical techniques of summarization as and graphical such as stem-leaf diagram, histogram, boxplot, and bar chart on this report. Since a bar graph is useful...
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...our results from the TKI to learn new conflict resolution skills. Frequently, our emotions and desires can make communication difficult. Use the Thomas-Kilmann questionnaire to learn what others are doing in those situations and learn to understand your own behavior during tense moments. You can master these challenges with knowledge and practice. The Five Conflict-Handling Modes The Thomas-Kilmann Conflict Mode Instrument (TKI) assesses an individual’s behavior in conflict situations—that is, situations in which the concerns of two people appear to be incompatible. In conflict situations, we can describe a person’s behavior along two basic dimensions*: (1) assertiveness, the extent to which the individual attempts to satisfy his or her own concerns, and (2) cooperativeness, the extent to which the individual attempts to satisfy the other person’s concerns. These two dimensions of behavior can be used to define five methods of dealing with conflict. These five conflict-handling modes are shown below: C O M P E T I N G Competing is assertive and uncooperative, a power-oriented mode. When competing, an individual pursues his or her own concerns at the other person’s expense, using whatever power seems appropriate to win his or her position. Competing might mean standing up for your rights, defending a position you believe is correct, or simply trying to win. C O L L A B O R A T I N G Collaborating is both assertive and cooperative. When collaborating...
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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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...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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...With the launching of Heavenly Chocolates’ website and online sales exceeding company’s expectations, the managerial team is interested in learning about online shoppers behaviors so they can make better managerial decisions based on its findings. This report is based on a sample of 50 online transactions picked randomly from Heavenly Chocolates’ website and uses descriptive statistics methods to learn more about customers. More specifically, it will cover the time spent on website, number of pages viewed, and money spent during their visit to the website, as well as the relationships among these variables. In addition, it discusses the effect that the day of the week and the type of browser have on sales. Online shoppers spend a median time of 12 minutes browsing on website In this particular case, the median is a better measure of central tendency because the data presents extreme values (e.g.32.9) that pull the mean towards the right causing skewness in the distributional shape. About 78% of online shoppers spend between 4 and 16 minutes navigating the website. The upper 25th percentile spend between 15.30 and 32.9 minutes navigating the website. As we can see from previous values mentioned, there is high variability in the dispersion of this data with a standard deviation of 6.06. The number of pages viewed is approximately bell-shaped with a slight rightward skewness of 0.65. Although there is no great difference between the mean (4.82) and the median 4.5, the median is...
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...Question 1 a) EMPLOYMENT CATEGORY Frequency Percent Valid Percent Cumulative Percent Valid CLERICAL 227 47.9 47.9 47.9 OFFICE TRAINEE 136 28.7 28.7 76.6 SECURITY OFFICER 27 5.7 5.7 82.3 COLLEGE TRAINEE 41 8.6 8.6 90.9 EXEMPT EMPLOYEE 32 6.8 6.8 97.7 MBA TRAINEE 5 1.1 1.1 98.7 TECHNICAL 6 1.3 1.3 100.0 Total 474 100.0 100.0 In the left most column, all job category are listed. The Frequency column records the number of observations that fall within a particular job category. As I have sorted our using SPSS, Clerical, Office Trainee, Security Officer, College Trainee, Exempt Employee, MBA Trainee, and Technical job category represent 227, 136, 27, 41, 32, 5, and 6 respectively out of the total employees of 474. The percent column shows how much each job category possesses of total employees. As you can see, as Clerical, Office Trainee, Security Officer, College Trainee, Exempt Employee, MBA Trainee, and Technical employment category represent 47.9%, 28.7%, 5.7%, 8.6%, 6.8%, 1.1% and 1.1% respectively. The Cumulative frequency column lists the total of each frequency added to its predecessor, such as, Clerical job category itself represents 47.9% of all employees and then Office Trainee job category represents 28.7%, but both Clerical and Office Trainee represents 76.6% of the total population. If we consider, Clerical, Office Trainee, and Security Officer; all of them represent 82.3% of the total employees. This is what cumulative percent...
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...I've selected GENDER and EXTRINSIC from data sheet. Here's Question:QMB350-1001A-07 Statistical Analysis Assignment Name: Unit 1 Individual Project Deliverable Length: 3 pages Details: The data set for our course is a sample of a survey conducted on the population of the American Intellectual Union (AIU). It is available via the following link: DataSet with DataSet Key which contains the following nine sections of data that will be used throughout our course: (1) Gender (2) Age (3) Department (4) Position (5) Tenure (6) Overall Job Satisfaction (7) Intrinsic Job Satisfaction – Satisfaction with the actual performance of the job (8) Extrinsic Job Satisfaction- Things external to the job, e.g., office location, your work colleagues, your own office (cubicle/hard walled office, etc), and (9) Benefits – Health insurance, pension plan, vacation, sick days, etc. In each of the assignments in this course you will be dealing with the following scenario: American Intellectual Union (AIU) has assembled a team of researchers in the United States and around the world to study job satisfaction. Congratulations, you have been selected to participate in this massive global undertaking. The study will require that you examine data, analyze the results, and share the results with groups of other researchers. Job Satisfaction is important to companies large and small and understanding it provides managers with insights into human behavior that can be used to strengthen the company's bottom line...
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...| 3.Midday | 4 | 4.Early Afternoon | 4 | 5.Late Afternoon | 1 | 6.Night Time | 0 | Mean | Median | Mode | 3.5 | 3.5 | 2 | Results: Question 1 examines the time of the day university students tended to visit Monash Cafes. Ten students were asked what was the main type of day they would visit Monash Cafes, more than one option could have been chosen. The evidence and results obtained as presented above shows that more students visit Monash Cafe’s earlier in the day. The mode of the data (most picked time of day) was Mid Morning. Only 1 student said that they visit a Monash Cafe Late Afternoon and not 1 person said they visit Monash Cafes Night Time. Q2 Which of the following options best represents your most current purchase behaviour at Monash Cafeterias? | Behaviour | Frequency | 1.Always take away | 5 | 2.Usually take away | 3 | 3.Mix of eat in and take away | 2 | 4.Usually eat in | 1 | 5.Always eat in | 0 | Mean | Median | Mode | | | 1 | Results: Question 2 dealt with the behaviour of customers after their purchase in regards to eating in or takeaway. The results shows that the majority of customers of Monash Cafeteria’s tended to take away their purchase. Sample size: 50 Score | Frequency | 1 | 0 | 2 | 0 | 3 | 2 | 4 | 11 | 5 | 17 | 6 | 13 | 7 | 7 | Mean | Median | Mode | 4 | 4 | 5 | Study 3: Study 3 combined all questions relating to the tangible aspects of Monash Cafeteria’s...
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...topic, it allows managers to gain awareness into the human behaviors that can build up the company’s bottom line. This paper will be examining gender and intrinsic job satisfaction while discussing the results of this evaluation in full detail. Method The data that has been selected for qualitative data is gender. The reason why gender was selected for qualitative data is because this may have an impact on job satisfaction in businesses. In many cases, the average calculation for the mean, median, and mode are unsatisfactory methods to determine qualitative data. However, the gender within this study is labeled with 1 and 2, and this would be considered appropriate. The mean that was calculated in this assignment was 1.275 rounded to 1.28 which demonstrated that the sample consisted of more men than women. The median was found by taking all of the numbers and putting them into numerical order and finding the number located in the middle of the data. This number was 1. The mode would be the most occurring number within the sequence. With analyzing gender, the most commonly used number in this sequence is 1. The variance for this sequence was 0.20. This was found by taking a set of the data values from the mean and squaring the average deviation. The standard deviation was...
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...Running Head: A VARK Analysis A VARK Analysis of One Student’s Learning Style LorieAnn T Dailey Grand Canyon University: NRS-429v April 22, 2012 A VARK Analysis of One Student’s Learning Style This paper will discuss Fleming and Bonwell’s VARK analysis of learning styles. (2002). It will also discuss the results from this student’s use of the VARK instrument and will compare those results to this student’s own observations about her preferred learning styles or methods. Next, this paper will discuss possible changes this student could make in her learning methods that might tend to make her a more successful student in view of her results on VARK questionnaire. Finally, this paper will briefly address the analysis of this student’s learning style in the specific context of e-learning or distance learning. VARK, an acronym for Visual, Aural, Read/write, and Kinesthetic, is a system developed in its current form by Neil Fleming which uses a sixteen-question instrument to evaluate a part of student’s learning styles. (Fleming & Bonwell, 2002, FAQs (Frequently Asked Questions)). While VARK is commonly referred to as an analysis of learning styles, that is technically inaccurate. While “learning style”, as the term is generally understood, refers to several different factors such as working with others vs. working alone, physical conditions of the learning environment, and even biorhythms, VARK analyzes only one aspect of learning styles: the way a student receives...
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...8.2 MEASURES OF CENTRAL TENDENCY Measures of central tendency are also called because they describe values which stand for magnitudes near the center of the distribution or around which the other values tend to cluster. Examples: Mean, median and mode 8.2.1 The Arithmetic Mean This is commonly known as the average. An ordinary average is one which is obtained by simply adding all the values observed, then dividing he sum by the number of values added. WEIGHTED ARITHMETIC - Another type of average. - obtained by first multiplying each observed value by a corresponding assigned weight before summing up the products then dividing the sum of he weights. In both instances, the arithmetic mean was computed simply by summing up then dividing the sum by the number of values added up. Symbolically, if X stands for the mean and each X¡ represents individual values to be added up to n number of variants and [∑] represents summation sign, then: 8.2.1.1 Ungrouped Data Raw data are values obtained directly either from observations or from the questionnaire results. Nothing has not yet been done to convert the data into any form, except to copy or to record them as values obtained from the direct observations. Example: TABLE 8.4 8.2.1.2 Grouped Data Values which have undergone some treatment, possibly in terms of arrangement into similar categories called groups or classes. To do this the following terms should be clearly understood: Class – refers to the...
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...12). Equal Interval - Numeric variable in which differences between values correspond to differences between values correspond to differences in the underlying thing being measured Ex. 45 - 90 degrees Fahrenheit. Ranked Order - Numeric variable in which values correspond to the relative position of things measured. Ex. Ranking of someone in a race (1st, 2nd, 3rd). Officer ranking (1st Lieutenant, 2nd Lieutenant). Nominal - Variables in which the values are categories. Ex. Religion, recipes, food types, etc. These types of variables measure the quantities or features of a type of group. Ratio Scale - Equal-interval variables are measured are measured on this type of scale. It has a absolute zero point, or a complete absence of the variable. Ex. Temperature of 0.0 , height, etc. Continuous - Limits the variable ranges, if any value is present. Ex. Number of people in a theater, restaurant, play, etc. 10 | | | | | | 9 | | | | | | 8 | | | | | | 7 | | | | | | 6 | | | | | | 5 | | | | | | 4 | | | | | | 3 | | | | | | 2 | | | | | | 1 | | | | | | 0 | 2.5 | 5.0 | 7.5 | 10.0 | 12.5 | 15). The flow the central tendency indicates that each individual clocked in under a distinctive speed within a 35 -mph range. Most of the ratings prove that a vast amount of people fell into speeds between 20 and 35 while a few went way over the speed limit. Speed Score | Frequency | Percentage | 15...
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...count of responses analyzed was 78 with a mean of 2.82, a median of 3.00 and a mode of 2.00. Table 1 illustrates this sample, including a standard deviation of 1.21 with the majority of responses on the negative side of the scale. This indicates that the average response was slightly negative concerning the level of training currently held by each employee. Table 1 Descriptive Statistics Q1 count 78 mean 2.82 median 3.00 mode 2.00 standard dev 1.21 Question 2 – The company provided the needed training. The second question in the survey concerned how well trained the employee was by the company. The total count of responses analyzed was 78 with a mean of 2.82, a median of 3.00 and a mode of 3.00. Table 2 illustrates the sample, including a standard deviation of 1.18, with the majority of responses being neutral. This indicates that the average response was slightly negative concerning the level of training received for each employee. Table 2 Descriptive Statistics Q2 count 78 mean 2.82 median 3.00 mode 3.00 standard dev 1.18 Question 3 – You were fairly paid for the work you did. The third question in the survey concerned how well paid the employee was for their position in the company. The total count of responses analyzed was 78 with a mean of 2.94, a median of 3.00 and a mode of 2.00. Table 3 illustrates the sample, including a standard deviation of 1...
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...Euy Hyun Chong N9718605 Euy Hyun Chong N9718605 Numerical descriptive measures Take Home Assignment - Topic 2 Numerical descriptive measures Take Home Assignment - Topic 2 Question 1: Data Set | | | | | | | | | 11 | 12 | 14 | 15 | 16 | 18 | 24 | 25 | 28 | 32 | Min | Quartile 1 | | Median | | Quartile 3 | Max | Table [ 1 ]. Ages of 10 randomly selected students in a Judo school in Brisbane. (a) Mean (x) : 19.5 Standard Deviation (S) : 7.24568 (b) Median: 16+182=17 When comparing the mean and the median it tells the distribution is skewed to the right, which is positively skewed. The skewness of the dataset is positive due to bigger mean value than the median, as mean pulls the dataset to the right. (c) Coefficient of Variation: CV= Sx= 7.2519.5=0.37157 (d) Using Excel: * First Quartile (Q1) = 13.5 [=QUARTILE.EXC(array,quart)] * Third Quartile (Q3) = 25.75 [=QUARTILE.EXC(array,quart)] * 80th Percentile = 27.4 [=PERCENTILE.EXC(array,k)] Working out by common sense: * First Quartile (Q1) = 12+142=13 * Third Quartile (Q3) = 25+282=26.5 * 80th Percentile = Lp=n+1×P100=10+1×80100=8.8 ≈9th value * Therefore, the approximate of 80th percentile is 28 (9th value in data set). (e) Range: highest value – lowest value = 32 – 11 = 21 IQR: Q3-Q1=12.25 (Used excel values) (f) Data...
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...Executive Summary Paws Palace Pet Clinic is experiencing a high influx of new patients for Dr.Bill Schulke and his wife/manager, Sue Schulke. The opportunity of prospective new patients and profits is wonderful if all new patients could be attended to. The problem lies in the simple question, would a veterinary assistant be necessary in order to treat all patients and still keep profits soaring up for the clinic? Using Monte Carlo simulation and distributions fitting using the @Risk software, the below report will showcase how a veterinary assistant is the best option for the clinic’s future and will double their profits annually. Background Paws Palace Pet was initiated by Veterinarian, Bill Schulke, along with his wife/office manager, Sue Schulke. In the beginning two years of their practice, they would work regularly scheduled business hours of 8:30am to 5:30pm. Over the last year they have experienced a growth in patients and have found the need to work past regular hours. Bill has created a policy for self-preservation, he will take the last patient at 7:00pm and if there are any remaining patients, they would need to return the following day. Although working past hours has allowed profit and gain in patients, at the end of the day, Bill still has 2-5 patients left without attention. This then results in lost patients who do not return again the following day and it also results in loss in potential profits. His wife, Sue, was instructed to keep track of 100...
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