...on the data we examine. 1. The variables I selected were: Qualitative data Gender (Male=1, Female=2) • I selected gender because it provides us with some demographical data to utilize that contracts the male and female population. Quantitative data Intrinsic Job Satisfaction (Scale 1-7) • I chose to select intrinsic job satisfaction because the data gathered from the survey can be utilized as a dependent variable. • This data concludes that the overall job satisfaction with Intrinsic and Extrinsic measures of the employees. • The measures of a central tendency date is listed below. For variable Gender: Median 2 Mode 2 You can conclude that since the data of variable gender is a qualitative data, the mean therefore cannot be a measure of central tendency. For variable Intrinsic job satisfaction score: Mean 5.32 Mode 5.2 Median 5.3 The data of the variable intrinsic job satisfaction score is recorded in a scale. The Mean, Median and Mode are the good measures of the central tendency. The mean satisfaction score is 5.32 with mode as 5.2 and Median, 5.3. It indicates that the intrinsic job satisfaction results indicate that the employees are most satisfied. For variable Gender: Variance 0.25 Standard Deviation 0.50 For variable Intrinsic job satisfaction score: Variance 0.16 Standard deviation 0.40 Skewness 0.52 Kurtosis 0.95 Range 1.5 Min 4.7 Max 6.2 The above data indicates that the variability in the data of the intrinsic job satisfaction score...
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...SHARMEEN ARSHAD 12133009 3RD SEMESTER MBA(BANKING & FINANCE) 12133009@GIFT.EDU.PK 3RD HASSAN 12133012 3RD SEMESTER MBA(BANKING & FINANCE) 12133012@GIFT.EDU.PK Acknowledgement In the name of ALLHA ALMIGHITY the lord of the world who has bestowed us with abilities and blessed with knowledge so that we can make best of opportunities provide to us. First of all we are indebted toward ALLHA ALMIGHTY who has created us and made capable enough to with stand in the competitive world. If words could pay gratitude then we would like to pay our esteem gratitude to our most respected SIR ABID AWAN for assigning us this project of BUSINESS QUANTITATIVE TECHNIQUES. Throughout the course period he has been extremely cooperated with us and guided us at every single step he has been very encouraging and kind to us. At the end we just want to thanks to our families, because they tolerated us when we don’t able to give time to them and become one of the main reasons of cancelation of many outdoor trips. ------------------------------------------------- OBJECTIVE Our basic objective to work on this report is to evaluate the how to statistical tools applied on practical life and how these tools helps us...
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...of Business Finance & Accounting, 28(5) & (6), June/July 2001, 0306-686X), the bias in analysts’ forecasts is positive in all (European) countries and the naïve forecast bias is negative in all countries. Moreover, they state that analysts’ forecasts are substantially more accurate than the naïve model. We find this investigation interesting because it is nice to know if the Naïve model is better than the analysts’ forecasts. In that case we could consider whether there is a role for the analysts at all. Also it is nice that the data is Finnish so it might be that there is not much investigation done before. * 2 DATA ANALYSIS Table 1 | Minimum | Maximum | Mean | Median | St.deviation | Skewness | Kurtosis | EPS2012 | -0,900 | 4,658 | 0,658 | 0,380 | 0,878 | 1,625 | 4,264 | EPS2011 | -0,520 | 4,240 | 0,692 | 0,410 | 0,801 | 1,710 | 3,914 | SALES2012 | 4831000 |...
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...using credit. A sample of 50 credit customers were selected based on five variables which included location, income, size, years, and credit balance. Location (Rural, Urban, Suburban) Income (in $1,000s) Size (Household Size) Years (Number of Years That the Customer Has Lived in the Current Location) Credit Balance (The Customers Current Credit Card Balance on the Store’s Credit Card) We will take a look at three different variables at this time. Location The location variable has three subcategories which include rural, urban, and suburban. This variable is looking at where customers live. A pie chart is a circular graph which divides information into sections based on numerical proportions. Frequency distribution tables allow us to look at variables and their frequencies (or how many times the variables occur). Frequency Distribution Location Frequency (# of Customers) Urban 21 Suburban 15 Rural 14 Interpretation: Based on the information shown in both the pie chart and the frequency distribution chart, we can see that more of the customers (21/50 = 42%) are from urban areas. Suburban areas are next with (15/50) 30% of the customers and rural areas have the least amount of customers with (14/50) 28%. Credit Balance The credit balance is the amount of funds that are currently charged to the credit card. Credit balance is a quantitative variable which means we actually can get an exact numerical value. A histogram is a graph...
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...LAB 1 –Mohammed Abdo 1.Analyze and discuss the results shown in the Statistics table (including definitions of the following statistical measures: Mean, Std. Error of Mean, Median, Mode, Std. Deviation, Variance, Skewness, Std. Error of Skewness, Kurtosis, Std. Error of Kurtosis, Range, Percentiles) (15%) Statistics | | Variable 1Life expectancy at birth (years), 2006 | Variable 2 Adult literacy rate (% aged 15 and above), 2006 | Variable 2 Combined gross enrolment ratio in education (%), 2006 | Variable 4GDP per capita (PPP US$), 2006 | N | Valid | 179 | 172 | 179 | 179 | | Missing | 1 | 8 | 1 | 1 | Mean | 67.7291 | 83.8767 | 71.5654 | 12258.81 | Std. Error of Mean | .80424 | 1.44937 | 1.33369 | 1066.857 | Median | 71.3000 | 91.2000 | 73.5000 | 6679.00 | Mode | 71.30a | 99.90 | 59.60a | 630a | Std. Deviation | 10.76001 | 19.00828 | 17.84362 | 14273.577 | Variance | 115.778 | 361.315 | 318.395 | 203735005.245 | Skewness | -.901 | -1.378 | -.470 | 1.811 | Std. Error of Skewness | .182 | .185 | .182 | .182 | Kurtosis | -.168 | 1.156 | -.040 | 3.633 | Std. Error of Kurtosis | .361 | .368 | .361 | .361 | Range | 42.20 | 77.10 | 88.70 | 76808 | Minimum | 40.20 | 22.90 | 25.50 | 281 | Maximum | 82.40 | 100.00 | 114.20 | 77089 | Percentiles | 10 | 50.1000 | 54.3300 | 45.1000 | 888.00 | | 20 | 57.8000 | 69.6200 | 57.3000 | 1592.00 | | 25 | 62.0000 | 73.7500 | 60.8000 | 1965.00 | | 30 | 64.5000 | 80.0500 | 63.2000 | 2489.00 | | 40 | 68...
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...volatility smile and the size of the variance risk premium relate to parameters of GARCH-type time-series models measuring how conditional volatility responds to return shocks. Markets in which return shocks lead to large increases in conditional volatility tend to have larger variance risk premia than markets in which the impact on conditional volatility is slight. Markets in which negative (positive) return shocks lead to larger increases in future volatility than positive (negative) return shocks tend to have downward (upward) sloping implied volatility smiles. Also, differences in how volatility responds to return shocks as measured by GARCH-type models explain much, but not all, of the variations in excess kurtosis and multi-period skewness across different markets. Ó 2013 Elsevier B.V. All rights reserved. Article history: Received 11 October 2012 Accepted 14 April 2013 Available online 17 May 2013 JEL classification: G13 G10 G12 Keywords: Implied volatility Volatility smile Variance risk premium GARCH Conditional heteroskedasticity 1. Introduction Along with jump risk, a leading explanation for both the implied volatility smile and the variance risk premium is stochastic...
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...[pic] A STATISTICAL RESEARCH PAPER Submitted to Mr. Marcelo C. Mendoza In partial Fulfillment to the requirements In Math 22 Submitted by GROUP: PLUS ONE Leader: MARK ANTHONY QUIJANO Co-Leader: ROBERT SANTOS Analyzer: MICHAEL CATABRAN The Race for 2010 Presidential Election Rationale: As we approach the May 2010 election. Our group conducted a quick survey to test the Filipinos’ maturity and integrity in choosing their candidates in our democratic institution. Thus, this project will try to determine the bet of some of our fellow countrymen for the presidential race in the May 2010 election. Statement of the Problem: The aim of this first term statistical research project is to find out who is JRU students’ choice for president in 2010. Specifically, it will try to find the following: 1] The profile of the respondents according to a] gender b] age 2] JRU students’ choice for president 3] Male JRU students’ choice for president 4] Female JRU students’ choice for president 5] JRU students’ perception of their candidates’ chance of winning the presidency in the 2010 election 6] Male JRU students’ perception of their candidate’s chance of winning the presidency in the 2010 election 7] Female JRU students’ perception of their candidate’s chance of winning the presidency in the 2010 election 8] JRU students’ degree of optimism that their choice...
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...Financial Modelling and Forecasting Lecture 1 Introduction and Descriptive Statistics The need for forecasts A forecast helps deal with an uncertain future by making decisions today No single forecasting method will lead to an accurate forecast. Forecasts can be wrong! “What’s the point of forecasting?” A business requires predictions as inputs E.g., Inventory, Personnel, Ordering, Production planning. Governments require forecasts to guide monetary and fiscal policy Lecture 1 2 Lecture 1 1 Forecasting Considerations Application to Finance A sensible forecast allows proactive decisions to be made today Without it, management decisions are reactive. Need to ensure sales forecasts can actually be satisfied Eliminate bias Sometimes forecasting can be too difficult Correct model selection is an important factor Financial management decisions are often classified into Investment and Financing The investment decision relates to the analysis and selection of ‘good’ assets One critical input the CFO must consider are the future sales of a new project Financial analysts value a business by forecasting the future cash flows of the entire firm Lecture 1 4 The entire firm is a just collection of assets Not all relationships are linear Lecture 1 3 Quantitative Forecasting Example Forecasts can be classified as quantitative or qualitative. Quantitative forecasting...
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...Descriptive statistics (see “Key Formulas” in your textbook for a list) for each of the four variables along with an explanation of what the descriptive statistics tell us about the motion picture industry. When comparing the motion picture industry for their opening gross there are several central tendency measurements. The mean is 9.37, median is .3935, and mode is .03. We are comparing 100 different movies. The range of opening gross is 108.43, which is from .01 to 108.44. The standard deviation or SD is 18.87. The middle 50% of the values was skewed. This was determined by using the appropriate measure of variability. The interquartile range for opening gross is 12.37. Opening Gross Histogram: When comparing the motion picture industry for their total gross there are several central tendency measurements. The mean is 33.0384, median is 5.852, and mode is .03, .04, .05, .11, .13, .42, and 56.07 (these all appear twice, therefore they are all equal). We are still comparing 100 different movies. The range for the total gross is 380.15, which is from .03 to 380.18. The standard deviation or SD is 63.16. The median total gross was 5.85; 50% of the total gross values were less than 5.85, 50% were above 5.85. The skewness of total gross is 3.28. The kurtosis of total gross was 12.32. The interquartile range for total gross is 47.03. Total Gross Histogram: When comparing the motion picture industry for the number of theaters there are several central tendency measurements...
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...Nova Southeastern University H. Wayne Huizenga School of Business & Entrepreneurship Assignment for Course: | (Winter 2014Business Modeling) | Submitted to: | (Dr. Phillip Rokicki) | Submitted by: | (Harry Cupp, Jessica Alvarez, Panashe Muwunganirwa) | | | | | | | | | Date of Submission: 2/01/14 Title of Assignment: ZZ Airlines CERTIFICATION OF AUTHORSHIP: I certify that I am the author of this paper and that any assistance I received in its preparation is fully acknowledged and disclosed in the paper. I have also cited any sources from which I used data, ideas or words, either quoted directly or paraphrased. I also certify that this paper was prepared by me specifically for this course. Student's Signature: ______________________________ ***************************************************************** Instructor's Grade on Assignment: Instructor's Comments: Executive Summary ZZ Airlines is currently deciding whether or not to hire an additional Call Service Representative. The airline does not want the wait time in the call center, between midnight and 6 A.M, to be more than 3 to 4 minutes, in order to provide good customer service. Additionally, the airline is planning to do an advertising campaign which is expected to increase the number of callers, based on similar past campaigns. Based on the statistical simulations, and its subsequent results, an analysis will determine whether an additional Call Service Representative is...
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...2. One of the most popular TV Shows in America, Wheel of Fortune, has been around for a while, but it is still a very popular show. What is unique about this show is the fact that letters play a bigger role than anything else. In the bonus round of the show, contestants are automatically given the letters R, S, T, L, N, E because back when the show was started, early contestants mostly chose those letters. This is case with my data as well. As you can see on the data table, the most common letters from the article are R, S, T, L, N and E. Today, the contestants are given additional 3 consonants and an additional vowel. If I was a contestant in the show, I would choose G, H, M as my consonants since they are the mostly used consonants after R, S, T, L, N and E in my data. Letter | Frequency | Relative Frequency | A | 122 | 8.5% | B | 23 | 1.6% | C | 51 | 3.6% | D | 46 | 3.2% | E | 193 | 13.4% | F | 24 | 1.7% | G | 57 | 4.0% | H | 65 | 4.5% | I | 129 | 9.0% | J | 4 | 0.3% | K | 17 | 1.2% | L | 61 | 4.2% | M | 32 | 2.2% | N | 126 | 8.8% | O | 11 | 0.8% | P | 26 | 1.8% | Q | 2 | 0.1% | R | 87 | 6.1% | S | 113 | 7.9% | T | 133 | 9.3% | U | 41 | 2.9% | V | 15 | 1.0% | W | 25 | 1.7% | X | 2 | 0.1% | Y | 31 | 2.2% | Z | 0 | 0.0% | 3. Looking at the stem-and-leaf plot, it is obvious there is a big difference between some of the letters. The percentage of relative frequency for the letters ranges from 0% to 13.4% meaning that while...
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...promotion Hawaii as preferred destination Total No. of days in Hawaii Traveler behaviour Facilitating tourist spending NON-NUMERIC VARIABLES Reason for trip Package to offer to tourists Meeting tourist preferences Where I plan to stay Accommodation planning Promotions coverage 2.0 DESCRIPTIVE STATS For the purpose of this report we will focus only on: Total No. of days in Hawaii (numeric) Reason for Trip (non-numeric) 2.1 Total No. of Days Correct average formula is – Total days stayed\Visitors per country From our raw data (Grp Task 1) I’ve reduced it to 20 countries (and taken out Taiwan 70\Indonesia 169\Malaysia153 - as these ave. days stayed are too high) Country Ave. 1. US 1 2. Canada 2 3. Germany 2 4. China 3 5. Australia 3 6. Korea 6 7. UK 7 8. France 8 9. Philippines 8 10. Switzerland 9 11. Brazil 10 12. Japan 12 13. Mexico 14 14. Italy 17 15. Singapore 20 16. Ireland 21 17. Argentina 25 18. Hong Kong 28 19. Thailand 29 20. New Zealand 37 Total – 20countries 262 Measure Calculation Value Sample mean (total\n) 262 20 13.1 Median (sort data in order) (ave of 10th and 11th) 10th Switzerland 11th Brazil 9+10 2 9.5 Mode (most common) 2 6 8 Multi-modal 2-6-8 Range (highest – lowest) 37-1 36 IQR (inter-quartile range) Q1 (ave 5th and 6th) = 3+6\2 = 4.5 Q3 (ave 15th and 16th) = 20+21\2 = 20.5 IQR = 20.5 – 4.5 16 Standard deviation data No. Data Mean...
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...Bottling Company Case Study | MAT 300: StatisticsProfessor: Dr. Negash Begashaw September 2, 2014 | Maggie L. Moore | Customers have begun to complain that the bottles of Lonice’s Soda contain less than the advertised sixteen ounces of soda. Today I asked my employees to randomly pull thirty bottles off the lines at random from all the shifts at the Lonice’s bottling plant. After asking them to measure the amount of soda in each bottle we came up with the following data. (See attached spread sheet). To calculate the mean, median and standard deviation for the ounces in the bottles I first imported the data in excel. After importing the data I use the Data analysis function where I choose descriptive statistics. Once choosing descriptive statistics I clicked ok and input my data range. Afterward I selected summary statistics and press OK. The forwarding data was provided; the mean equaling 14.87, median equaling 14.8, standard deviation equaling 0.550. To construct a ninety-five confidence interval for the ounces in the bottles I use the following information. X equal to 14.87 which is the mean of the data from the bottles, n equal to 30, because of the amount of bottles we use, standard deviation equals zero point five five zero which was calculate with the data from the bottles. I use the formula [ ] which gave me the lower limit of 14.673 and an upper limit of 15.067. Finally I conducted a hypothesis test to verify if the claim that a bottle...
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...positive correlation of 0.95 between Weekly Income (WI) and Weekly Expenditure on Food (WEF). This means that a change in Income shall bring about a similar change in the expenditure on food as well. The way we see the data points (80% of the sample) bunched and scattered between the range of WI of 200-400 and just focus on this sub-data set; we would have a very low correlation of 0.32. This would mean that in this range the relation between the Income and Expenditure on Food is really low and there are other factors, which are influencing the expenditure on food. The above data tells us that the small and large families are both spending almost the same amount on food per head each week as a portion of their income (21.4% and 22.9% respectively). However, the outlier here is the medium sized family (3-4), which is spending nearly 30% of their per head income on food. This amount tells us that there are factors other than family size which are impacting this group’ expenditure (This also ties in with the correlation deductions). To understand the three areas of WI, WEF and FS of interest better we put the data into 5 buckets each. 78% of the sample is in the first bucket and are earning less...
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...Running Head: Fundamentals of Statistics Abstract This paper provides an analysis of a small sample on the recent company job satisfaction survey. The focus of the study includes one qualitative data set (company position) and one quantitative data set (intrinsic). To support the analysis the following are also included in this paper: reason why these particular data sets were selected, calculations used, reason why some statistical measures did not apply to certain data sets, and what was learned from the analysis. Additionally, a graphical depiction to support data calculations and a conclusion is included. Introduction A job satisfaction survey is an analysis of employees who are satisfied with their job and the duties they perform. The proceedings of this paper include a qualitative and quantitative data collected during a job satisfaction survey. Quantitative data are data values that are numeric. Whereas, qualitative data are data values that can be place into distinct categories according to some of their character tics or attributes. The study serves as a foundation for future analysis in an effort to make accurate conclusions in regards to understanding global job satisfaction. A logical explanation for selecting the data sets analyzed as well as what was derived from the selected data sets will be provided. Qualitative Data: Company Position For this analysis I have chosen to use the company position data set for my qualitative assessment...
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