...result. Shape and skewness: Negatively skewed: mean < median Symmetry: mean = median Positively skewed: mean > median Mode = the score that occurs most frequently. Median = middle score when scores are ranked in order. Mean = the average score, the media. Interpreting the standard deviation: -the empirical rule (frequency distribution is bell shaped and symmetric) 68% values: -1 - +1 95% values: -1 - +1 99.7 % values: -3 - +3 -Chebyshev’s rule (applies to all distributions, regardless their shape) ?% values: -1 - +1 75% values: -2 - +2 88.9% values: -3 - +3 o Choice of numerical measures depends on the measurement level of the variable. o Measurement levels are hierarchical and so are the numerical measures. o ‘Higher’ level measures are generally preferred. o Some measures are sensitive to extreme values. Less sensitive measures are preferred when a distribution is too skewed. o Use the Z-score to determine the relative position of an observation. ( Z = X - / ) Central Limit Theorem: When the sample is sufficiently large (n30) the sampling distribution will be approximately a normal distribution. Confidence interval: Probability that the random selected interval encloses the unknown parameter. Typical values 99%, 95%, and 90%. Z-values large samples, t-values small samples, unknown. 90% : -1.65 95% : -1.96 99%: -2.58 +1.65 +1.96 +2.58 =SEM (std. error of the mean) Hypothesis: An assumption or belief concerning...
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...We knew this was appropriate because question 7 is categorical, and stacked bar graphs are a categorical display. Another reason this is appropriate is because it shows the part to whole relationship comparing each category to the whole. Since there are only three categories, it is easy to tell how much of an influence each category has, and that is shown by the percentages. 3. For question 2, we used mean to find the measure of center, and the mean is 14.25. We chose the mean because there is not a very high range of numbers, and there are no outliers. Since the mean is not resistant to change, outliers greatly affect the mean, so using the mean with outliers is not representative of the measure of center. Since 14.25 is the mean,the amount in each of the eight groups was evenly distributed in order to find the mean. That left each group with 14.25, so that means a full time WMU student who has declared their major takes about 14 credits per semester on...
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...Introduction to Earnings: Earnings are the fundamental indicator of a company’s value. Also referred to as ‘the bottom line’ and the ‘net income’, a company’s earnings is seen as the most important figure in a company’s financial statement as it is the summary measure of a company’s performance using the accrual basis of accounting. The theoretical value of a company’s stock is the present value of future earnings, or its ability to generate profit in the future (Lev, B. (1989). Earnings have a corresponding relationship with the projected value of a company with increased earnings representing an increase in company value which is inversely so for companies with lower earnings. Part A: Earnings Quality Although it is very common for investors to look at earnings in terms of quantity, the amount that the company has earned, developing an understanding of the quality of those earnings is vital to forecasting the quantity and credibility of future earnings of the company (smith). As reported earnings are a predominant driver of success, the reporting of earnings is a crucial business area that requires focus and direction. As so much of a businesses future and value is reflected in its earnings, it is very important that investors are provided with information that is free from mistakes and manipulation in order to get a true illustration of that company’s performance. The extent to which the financial performance is free from these errors and manipulations refers to the...
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...CHAPTER 14 FINANCIAL AND OPERATING LEVERAGE Q.1. A.1. Explain the concept of financial leverage. Show the impact of financial leverage on the earnings per share. The use of fixed-charges sources of funds, such as debt and preference capital, along with owners’ equity in the capital structure is known as financial leverage (or gearing or trading on equity). The financial leverage employed by a company is intended to earn more on the fixed charges funds than their costs. The surplus will increase the return on the owners’ equity. The role of financial leverage in magnifying the return of the shareholders’ is based on the assumptions that the fixed-charges funds (such as the loan from financial institutions and other sources or debentures or bonds) can be obtained at a cost lower than the firm’s rate of return on net assets. So, when the difference between the earnings generated by assets financed by the fixedcharges funds and costs of these funds is distributed to the shareholders, the earnings per share (EPS) (or return on equity, ROE) increases. EPS is calculated by dividing profits after tax (PAT) (net of preference dividend) by the number of shares outstanding. Example: All-equity Debt-equity 1. Investment 500,000 500,000 2. Equity capital 500,000 250,000 3. Debt capital @ 15% 0 250,000 4 EBIT 120,000 120,000 5. Less: Interest 0 37,500 6. PBT 120,000 82,500 7. Less: Taxes @ 50% 60,000 41,250 ------------8. PAT 60,000 41,250 9. No. of equity shares 50,000 25,000 10. EPS (5 ÷...
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...Analyzing Processes Process Diagram: (e.g. Producing Contact Lenses) hold Data information flow Other Common Process Measures • Cycle time – Average time between completion of successive units by the process – Reciprocal of throughput rate Process Layout of a Medical Center – Capacity Analysis Patient Type A B C D Total Patient Mix Wait Area X-ray 0.4 1 1 0.3 1 0.2 1 1 0.1 1 1 1 0.7 1000 400 571 Department Orthopedic Cardinology Neurology Intensive care 1 1 1 1 1 0.4 0.3 0.2 0.4 250 625 500 1667 300 1500 600 1500 add value Utilization = Load Factor = Actual Output Rate Maximum Throughput Demand Rate Capacity Dept. capacity Four General Categories of Production Processes 1. 2. 3. 4. Continuous Flow Process Line Process Batch Process Job Shop Max. # of total 1000 patients arrived before reaching cap. Variability: Classic Queuing arrival rate () in customers/hour • service rate () in customers/hour (avg. time for one customer = 1/) • # servers = s Therefore, capacity = _s*_ • Physics of Queues: M/M/1 system Performance Measures: Utilization: Average time in system = /( Average number in system = /( Average time in line = 1/( Average number in line = 1/( Probability of no waiting = 1 – Process Choice at King Soopers Bakery • Bread process – Flow process – High volume – Difficult and expensive to change capacity Servers (s) customers/hr./server arrivals ( customers/hr.) departures...
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...* Identify the reasons for using process capability studies and the limitations of these studies * Compare the applications of the three types of process capability studies (potential, short term and long term) * Determine whether a process is capable of meeting specified tolerances by using Cp and Cpk indexes * Estimate inherent variability in a process * Use calculations to determine whether a process is centered * Evaluate the potential and short term capability of a process Welcome to Process Capability Studies. In this lesson, we will cover the following topics: * What is Process Capability? * Pittfalls of Process Capability Studies. After completing this lesson, you should be able to: * Identify examples of real world situations in which a process capability study could be used * Identify the equations associated with process capability studies * Select the times during the life of a process where a process capability study can be run * Identify the appropriate equation for long term studies vs. single time studies * Select the common pitfalls or limitations associated with conducting a process capability study * Identify the factors that contribute to the limitations of process capability studies * Analyze a scenario that describes a process capability study. "It is good management to reduce variation of any quality characteristic, whether this characteristic be in a state of control or not...
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...Measures of central tendency are also called measures of central location because they are single values that tends to describe a given set of data by identifying their central locations.In his book introduction to statistical methods and data analysis, R. Ott Longnecker argues that measures of central tendency seek to describe the centre of distribution of measurements and also how the measurement vary about the centre of distribution.Central tendency measures include mean, mode nd median.On the other hand measures of variability describes a given set of of data by analyzing how data varies from its centre of distribution.examples of variability measures include range, standard deviation and variance. There are two main branches of statistics that include descriptive statistics and inferential statistics.Descriptive statistics gives numerical measures that describes the features of a given set of data. Inferential statistics on the other hand takes a sample of a given population, analyses the sample, and from it draw conclusions about the population .Malcolm.O.Asadoorian and Demetrius Kantarelis in their book: Essentials of inferential statistics argue that descriptive statistics organize , summarize and display data whereas inferential statistics utilize probabilistic techniques to analyze sample information from a certain population to improve our knowledge about the population. Measures of central tendancy and variability fall under descriptive statistics. Inferential statistics...
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...in the January 2008 issue of Vision Systems Design, "Quality Numbers: Six Sigma.") In nature and most manufacturing processes no two things are ever exactly the same. There exist small variations from part to part or measure to measure. If you were to acquire metrics on features of 100 "identical" parts and plot the values relative to frequency, you would be plotting a histogram. For stable processes, the curve would most likely be a normal, or bell-shaped, curve. The analysis of the data in this fashion is called descriptive statistics. Data about the entire population is not usually studied. It is more useful to study a sample of that population and infer from the analysis what the entire population most likely looks like. This is inferential statistics. The confidence in the correctness of that prediction is dependent upon the size of the sample and the behavior of the data. Some of the useful characteristics that can be calculated from the data are described below. The average value of the data is called the mean or X-bar. The equation for the mean is (X1+X2+X3+...+XN)/N, also denoted by ΣXi/N. Another measure calculated from the data is the variability, or the degree to which the individuals cluster about the mean. The most common measure of variability is the variance. The variance is calculated by squaring and summing the deviation of the individual data points from the mean. The equation for variance is s²=Σ (X )²/(N 1). The square root of the variance provides the...
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...HOW TO ORGANIZE DATA One way of organizing raw data or observations is through the use of frequency distribution table. One such example is a profile of cooperatives in a province which is given below: |Initial Capital, in Pesos |Number of Cooperatives | |Below 25,000 |43 | |25,000 – 49,999 |28 | |50,000 – 74,999 |17 | |75,000 – and above |12 | STEPS IN CONSTRUCTING A FREQUENCY DISTRIBUTION TABLE: 1. Obtain the number of class intervals to be used. Usually, the number of class intervals should be anywhere from 5 to 20. Too many intervals would result in a loss of organization. Too few intervals, on the other hand, would result in a loss of detail. To obtain a more specific guide, we can use Sturges’ Rule which states that: K = 1 + 3.322*(log10 n) where K = the number of class intervals rounded upwards n = the number of observations 2. Obtain the size of the intervals. To obtain an initial estimate of the size of the intervals we can use the formula: [pic] where ii = the initial estimate of the interval size LO = the largest observed value Ic = the smallest increment of change in data SO = smallest observed value Kr = the K value...
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...Several central tendency measures (i.e., mean, median, and mode) are calculated for opening gross in the Excel file. As can be seen in the below histogram, opening gross has a right tail (i.e., skewed to the right), so median would be a more appropriate central tendency measure than the mean. The median opening gross was 0.39; 50% of the opening gross values were less than 0.39, 50% were above 0.39. The skewness of opening gross was 3.43 indicating a right tail. The kurtosis of opening gross was 13.81 indicating a leptokurtic distribution. The range of opening gross was 108.43, from 0.01 to 108.44. The standard deviation of opening gross was 18.87. However, because the distribution was skewed, interquartile range (IQR), which is the range for the middle 50% of the values, would be a more appropriate measure of variability. The interquartile range for opening gross was 12.37. Opening Gross Outliers: Based on the box-plot, extreme values were 108.44 (Star Wars: Episode II), 102.69 (Harry Potter and the Goblet of Fire), 77.06 (War of the Worlds), 50.34 (Mr. and Mrs. Smith), 48.75 (Batman Begins), and 33.90 (Wedding Crashers). It is also suggested that values that are less than z-score of -3 or larger than z-score of +3 should be considered outliers. Using that criteria 77.06 (War of the Worlds), 102.69 (Harry Potter and the Goblet of Fire), and 108.44 (Star Wars: Episode II) can be considered outliers. Total Gross: Several central tendency measures (i.e., mean, median, and mode)...
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...Introduction: Decision makers make better decisions when they use all available information in an effective and meaningful way. The primary role of statistics is to to provide decision makers with methods for obtaining and analyzing information to help make these decisions. Statistics is used to answer long-range planning questions, such as when and where to locate facilities to handle future sales. Definition: Statistics is defined as the science of collecting, organizing, presenting, analyzing and interpreting numerical data for the purpose of assisting in making a more effective decision. Types of Statistics: There are two types of statistics 1. Descriptive Statistics is concerned with summary calculations, graphs, charts and tables. 2. Inferential Statistics is a method used to generalize from a sample to a population. For example, the average income of all families (the population) in the US can be estimated from figures obtained from a few hundred (the sample) families. Statistical Population: Is the collection of all possible observations of a specified characteristic of interest. An example is all of the students in BUSA 3101 course in this term. Note that a sample is a subset of the population. Variable: A variable is an item of interest that can take on many different numerical values. Types of Variables or Data: 1. Qualitative Variables are nonnumeric variables and can't be measured. Examples include gender, religious affiliation...
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...a second review and increased points. Name: DT1 DESCRIPTIVE STATISTICS Please read and understand before responding Topics: Central Tendency, Variability (Dispersion), Quartiles, normal data and skewed distribution of a single data group. You will want to know what measure to use when data distribution is normal (symmetrical) and when the data distribution is significantly skewed. We will work with six measures, mean, median, mode, standard deviation, interquartile range (IQR), and range. You may use a web search or the textbook. Textbook: McClave Ch. 2 pp. 54-60 (Central Tendency); pp. 64-67 (Variability); pp. 81-83 (Quartiles) Methods for Describing Sets of Data MyStatLab: Week 1 Problems supports this assignment Central Tendency (Data Center) A. Explain the strengths and weaknesses for each measure of central tendency: a) Mean, b) Median, c) Mode Mean Strengths – Shows averages of all number data Weaknesses – Averages can be thrown off with outliers Median Strengths – Shows middle number variable Weaknesses – Isn’t always exact, as there may be outliers < less sensitive to outliers is a strength. Mode Strengths– Shows most achieved results Weakness – Doesn’t paint a good picture. Not all values are used. B. Choose the best measure to use when data is normal? Choose the best measure to use when data is significantly skewed? The mean to me would be the best choice if the data is normal. The mean shows exactly what is going on. However...
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...University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Dissertations and Theses from the College of Business Administration Business Administration, College of 2-1-2011 ESSAYS ON INTERNATIONAL TRADE AND FOREIGN DIRECT INVESTMENT Wanasin Sattayanuwat University of Nebraska - Lincoln, wanasin@yahoo.com Follow this and additional works at: http://digitalcommons.unl.edu/businessdiss Part of the Business Commons Sattayanuwat, Wanasin, "ESSAYS ON INTERNATIONAL TRADE AND FOREIGN DIRECT INVESTMENT" (2011). Dissertations and Theses from the College of Business Administration. Paper 18. http://digitalcommons.unl.edu/businessdiss/18 This Article is brought to you for free and open access by the Business Administration, College of at DigitalCommons@University of Nebraska Lincoln. It has been accepted for inclusion in Dissertations and Theses from the College of Business Administration by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. ESSAYS ON INTERNATIONAL TRADE AND FOREIGN DIRECT INVESTMENT by Wanasin Sattayanuwat A DISSERTATION Presented to the faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy Major: Economics Under the Supervision of Professor Craig R MacPhee Lincoln, Nebraska February 2011 ESSAYS ON INTERNATIONAL TRADE AND FOREIGN DIRECT INVESTMENT Wanasin Sattayanuwat, Ph...
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...Defective from vendor Not to Specifications Material handling problems * Machines (equipment): Out of adjustment Tooling Problem Old/Worn * People (manpower): Poor Supervision Lack of Concentration Inadequate training * Process: Poor process design Ineffective quality management Deficiencies in product design * Measurement: Faulty Toys Incorrect Specifications Improper Functions * Environment: Inaccurate temp control Dust & Dirt Slide 3 Pareto Chart Slide 4 Check Sheet Slide 5 Slide 6 Slide 7 Control Charts can be used for the following: • Early detection of potential problems • Identifying assignable causes to decrease variability • Establishing the process capability • Reducing price adjustment costs • Decreasing inspection frequency • Providing a basis for changing the specification limits • Providing a basis for product acceptance • Permanent record of quality • Instilling quality awareness Part B Slide 1 A primary focus of TQM and most Quality Management Systems is to improve customer satisfaction by having a customer focus and consistently meeting customer expectations. Customers are almost always satisfied when their expectations are met. When they expect a certain product or service, and you deliver it without problems and at a fair price,...
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... Executive Summary ............................................................................................................ v 1. Introduction................................................................................................................. 1 1.1 Objectives of the study........................................................................................ 1 1.2 Approaches and methodology............................................................................. 2 2. Climate variability and impacts .................................................................................. 2 2.1 Climate variability trends.................................................................................... 2 2.2 Areas most vulnerable to climate variability ...................................................... 5 2.3 Future scenarios on climate variability and trends ............................................. 6 2.4 Measures to alleviate adverse impacts of climate variability...
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