Accounting in a Business Context BU2021 Contents Introduction........................................................................................................................................... 1 Ratio Analysis.........................................................................................................................................2 Profitability........................................
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CORRELATION & LINEAR REGRESSION Prof. Jemabel Gonzaga-Sidayen Spearman rank order correlation coefficient rho (rs) • Spearman rho is really a linear correlation coefficient applied to data that meet the requirements of ordinal scaling • Formula: rs = 1 - 6 Σ D i 2 N3 - N – Di = difference between the ith pair of ranks – R(Xi) = rank of the ith X score – R(Yi) = rank of the ith Y score – N = number of pairs of ranks Try this Subject Proportion of Similar Attitudes (X) Attraction (Y) Rank of
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Useful Expressions - Business Language Apologizing I’m sorry. I made a mistake. Please accept my apologies. I’m sorry. I didn’t mean to . . . (I’m) sorry. I didn’t realize that . . . . That’s okay. No problem. Prefacing bad news I’m sorry (I have) to tell you this‚ but . . . I hate to tell you this‚ but . . . I don’t know how to tell you this‚ but . . . I have some bad news. (Formal) written apologies We regret to inform you that . . . Regretfully . . . Unfortunately .
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Measuring the Absorption Coefficient for Some Common Materials Used in Functional Rooms through Standing Wave Ratio Adonis Cabigon1‚ Alaiza Tangaha2 Department of Physics‚ University of San Carlos‚ Nasipit‚ Talamban‚ Cebu City 6000 1adoniscc@yahoo.com 2mayalaiza92@yahoo.com Abstract In this paper‚ we present the measurement of the absorption coefficient α of MDF (Medium Density Fiberboard) and Fiber Cement through an improvised standing wave apparatus consist of an enclosed tube with
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Dilemma Lying in Business Q1: In a business context‚ is it ever okay to lie? If yes‚ what are those situations? Why is it okay to lie in these situations? In my opinion it’s not ok to lie as it is not ethical regardless of the situation. Businesses should be of fair in their dealings. One should always try to turn around the deal by working to improve such factors which would ultimately lead you to a lie in the business. In reality some people do lie in the business context to manipulate the situation
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Homework 1 Due Monday‚ September 17th at the beginning of class. Show your work. 1. Match the differential equation in (a)-(c) to a family of solutions in (d)-(f). The point of this exercise is not to solve the differential equations in a) - c). (a) y = y 2 (b) y = 1 + y 2 (c) yy = 3x (d) y = tan(x + C) (e) 3x2 − y 2 = C (f) y = −1/(x + C) 2. Find the value of k so that y = e3t + ke2t is a solution of y − 2y − 3y = 3e2t . 3. Solve the following differential equations and IVP’s. You may solve these
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diverse as the Riemann hypothesis‚ stochastic differential equations‚ statistical physics‚ chaotic systems‚ numerical linear al- gebra‚ neural networks‚ etc. Recently random matrices are also finding an increasing number of applications in the context of information theory and signal processing‚ which include among others: wireless communications channels‚ learning and neural networks‚ capacity of ad hoc networks‚ direction of arrival estimation in sensor ar- rays‚ etc. The earliest applications
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1 CORRELATION & REGRESSION 1.0 Introduction Correlation and regression are concerned with measuring the linear relationship between two variables. 1.1 Scattergram It is not a graph at all‚ it looks at first glance like a series of dots placed haphazardly on a sheet of graph paper. The purpose of scattergram is to illustrate diagrammatically any relationship between two variables. (a) If the variables are related‚ what kind of relationship it is‚ linear or nonlinear
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14: Correlation Introduction | Scatter Plot | The Correlational Coefficient | Hypothesis Test | Assumptions | An Additional Example Introduction Correlation quantifies the extent to which two quantitative variables‚ X and Y‚ “go together.” W hen high values of X are associated with high values of Y‚ a positive correlation exists. W hen high values of X are associated with low values of Y‚ a negative correlation exists. Illustrative data set. W e use the data set bicycle.sav to illustrate correlational
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Correlation Chapter 10 Covariance and Correlation What does it mean to say that two variables are associated with one another? How can we mathematically formalize the concept of association? Differences between Data Handling in Correlation & Experiment 1. Summarize entire relationship • We don’t compute a mean Y (e.g.‚ aggressive behavior) score at each X (e.g.‚ violent tv watching). We summarize the entire relationship formed by all pairs of X-Y scores. This is the major advantage
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