...Forecasting Gold Prices Using Multiple Linear Regression Method Z. Ismail, 2A. Yahya and 1A. Shabri Department of Mathematics, Faculty of Science 2 Department of Basic Education, Faculty of Education University Technology Malaysia, 81310 Skudai, Johor Malaysia 1 1 Abstract: Problem statement: Forecasting is a function in management to assist decision making. It is also described as the process of estimation in unknown future situations. In a more general term it is commonly known as prediction which refers to estimation of time series or longitudinal type data. Gold is a precious yellow commodity once used as money. It was made illegal in USA 41 years ago, but is now once again accepted as a potential currency. The demand for this commodity is on the rise. Approach: Objective of this study was to develop a forecasting model for predicting gold prices based on economic factors such as inflation, currency price movements and others. Following the melt-down of US dollars, investors are putting their money into gold because gold plays an important role as a stabilizing influence for investment portfolios. Due to the increase in demand for gold in Malaysian and other parts of the world, it is necessary to develop a model that reflects the structure and pattern of gold market and forecast movement of gold price. The most appropriate approach to the understanding of gold prices is the Multiple Linear Regression (MLR) model. MLR is a study on the relationship between a single dependent...
Words: 3920 - Pages: 16
...Regression Analysis Definition: Regression is used to examine the relationship between one dependent and one independent variable. After performing an analysis, the regression statistics can be used to predict the dependent variable when the independent variable is known. Regression goes beyond correlation by adding prediction capabilities. Types Of Regression Analysis: Most widely used two types of regression analysis are- I [pic] Linear Regression Analysis: When the regression is conducted by two variables or factors then is called linear regression analysis. Multiple regression analysis: Multiple regression analysis is a technique for explanation of occurrence and calculation of future actions. A coefficient of correlation among variables X also Y is a quantitative index of connection involving these two variables. In squared type, while a coefficient of purpose specifies the quantity of difference in the principle variable Y that is accounted for through the deviation in the analyst variable X. [pic][pic][pic][pic]Examples for Linear Regression Analysis: ABC a manufacturing co. where the production cost depends on their raw materials cost. Now, For the given set of x(tk in million) and y ( tk in thousand per unit) values, determine the Linear Regression and also find the slope and intercept and use this in a regression equation. |X |Y | |50 |4.2 ...
Words: 797 - Pages: 4
...Intercontinental University Unit 5 Individual Project BUSN311-1301B-10: Quantitative Methods and Analysis Instructor Leonidas Murembya April 23, 2013, Abstract This paper will be discussing regression analysis using AIU’s survey responses from the AIU data set in order to complete a regression analysis for benefits & intrinsic, benefits & extrinsic and benefit and overall job satisfaction. Plus giving an overview of these regressions along with what it would mean to a manager (AIU Online). Introduction Regression analysis can help us predict how the needs of a company are changing and where the greatest need will be. That allows companies to hire employees they need before they are needed so they are not caught in a lurch. Our regression analysis looks at comparing two factors only, an independent variable and dependent variable (Murembya, 2013). Benefits and Intrinsic Job Satisfaction Regression output from Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.018314784 R Square 0.000335431 The portion of the relations explained Adjusted R Square -0.009865228 by the line 0.00033% of relation is Standard Error 1.197079687 Linear. Observations 100 ANOVA df SS MS F Significance F Regression 1 0.04712176 0.047122 0.032883 0.856477174 Residual 98 140.4339782 1.433 Total 99 140.4811 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept...
Words: 830 - Pages: 4
...are countless more you could use. High low method for revenue Ton Miles (High-2,369, low-1,580) by Salary Cost(High-1,607, Low1,037) :(2,369-1,580)/(1,607-1,037)= 789/570=1.384 I selected this driver because the revenue directly correlates to the price you could pay your employees and still turn a profit. Delta can use this to see that they are making a profit when looking at just the salary cost and the revenue they bring in. The advantages of this technique are you have concrete evidence when and if employees ask how much they are getting paid and why and the disadvantages are that the revenue Ton Miles can fluctuate over the course of a year and make it hard to base the decision of salaries on just one variable. 2.Use simple regression to estimate the salary cost function for Delta Airlines. Comment on the statistical validity and significance of your results. What are the advantages of this technique? What are the disadvantages? Is this technique an improvement from the High-Low Method? Revenue Ton Miles and Salary Cost, Y=.3301X+614.33 with its R^2=.1378 This was not the best variable to use considering that the R square for the “Available Ton Mile” is much smaller equaling .5577 this means that the correlation...
Words: 676 - Pages: 3
...functions for the different variable values. 2. Explain the purpose of simple linear regression and scatter diagrams. Please provide a simple linear regression model and define each variable used. A scattered diagram is a statistic tool that is used to show the relationship between two variables. The scattered diagram is a combination of simple linear regression line that is used to fit the model in between two variables. The line that is drawn in the scatter chart is a model that is formed from the simple linear regression the data provides. The line equation is as follows: y=mx+b. When the calculation is done from the line, the values for “m” and “b” (the slope and intercept) are calculated with the data that is used in the simple linear regression. An example of simple linear regression is as follows: car rental for 1 day is $100 M-Thur, weekend rates will vary depending on the demand of the rentals and the number of cars. The total cost for a car rental M-Wed = constant + variable portion = $300 + $25 (number of rentals on lot) Variables: The constant portion = $300 = total for car rental for 3 days The variable portion = $25 = direct cost of car rentals available Unknown variable = supply and demand 3. Describe multiple regression analysis and discuss potential uses for this model Multiple regressions are the extension of a simple linear regression. This regression is used when the need to predict the value of a variable is based on the value of more...
Words: 321 - Pages: 2
...The One-Dimensional Voting Model allows for certain assumptions to hold that result in a Condorcet winner and no cycle. Voters choose among points on a line so that each voter prefers points on the line closest to his or her favorite point on the line. The point on the line that beats all of the other options, under the Majority Rule, is the point that the median voter favors the most. Although this model proves there can be a Condorcet winner under the Majority Rule of voting, it is a simplistic model, since it can only prove this result when certain assumptions are made, such as in a single regression rather than in multiple regressions. In the One-Dimensional Model, the outcome of voting depends on the default outcome and what is on the ballet. Since every voter prefers the majority closest to their first choice option, the median is the Condorcet winner. This is demonstrated in the model below: D L Median Median ≤1/2 # of voters ≤1/2 # of voters In this model, the Legislation can vote yes or no, where the outcome will result in L or D. The yellow triangle represents the median; therefore, less than or equal to half the total number of voters are on either side of the triangle. The orange line represents one majority of voters, which includes all of the voters on the left side of the line and the median. The green line represents another majority of voters, which includes all of the voters on the right side of the line and the median. Therefore,...
Words: 1407 - Pages: 6
...EViews Workshop Program: MSc in Finance 2011-2012 Instructor: Dimitris Tsouknidis, MSc, MBA, PhD Email: dtsouknidis@gmail.com Course Description • Workshop 1 – Introduction, Regression Analysis, Multiple Regression Analysis • Workshop 2 – Issues with the Classical Linear Regression Model and Univariate Time-series Modeling in Finance • Workshop 3 – Multivariate Time-Series Modeling in Finance and Modeling Long-run Relationships in Finance MSc Finance - EViews Workshop 1 - 2012 2 Workshop 1 Introduction, Classical Linear Regression Analysis and Multiple Regression Analysis 19 January 2012 Agenda • • • • • • • Introduction to EViews Importing Data Loading and Saving Datasets Graphical and Statistical Analysis Regression Analysis Multiple Linear Regressions Case Studies – January Effect MSc Finance - EViews Workshop 1 - 2012 4 Introduction • EViews is a menu-driven econometric software. • Open EViews requires: – Start/All Programs/EViews 7 • EViews organizes data, graphs, output, etc. as objects. • Each of these objects can be copied, saved and/or cut-andpasted. • EViews is not case sensitive e.g. INDEX = index. • EViews is producing workfiles (.wf1). • You can import data from Microsoft Excel (.xls) and create workfiles (.wf1). MSc Finance - EViews Workshop 1 - 2012 5 Creating a Workfile • Click: File/New/Workfile. • You can select the type of your workfile (Dated – regular frequency, Balanced panel, Unstructured/Undated ), the frequency...
Words: 2127 - Pages: 9
...ANOVA table. | | | | | | |3.131a |2 |.209 | | |3.433 |2 |.180 | | |.543 |1 |.461 | | |40 | | | Figure 6.1 Cross table of satisfaction and sex at α=0.05 The p-value, which is 0.209, is very obviously greater than our chosen level of significance, 0.05. The null hypothesis is accordingly not rejected, i.e. the level of satisfaction is independent of the sexes. 7. Correlation and regression The MBA programme leader has become so enthralled with the benefits of using statistical analysis as a performance indicator toolkit, then decides that further analyses on student performance, expected and anticipated salaries ought to be carried out. a. Produce a...
Words: 659 - Pages: 3
...Regression Models Student Name Grantham University BA/520 – Quantitative Analysis Instructor Name April 6, 2013 Abstract This paper will refer to regression models and the benefits that variables provide when developing and examining such models. Also, it will discuss the reason why scatter diagrams are used and will describe the simple linear regression model and will refer to multiple regression analysis as well as the potential uses for this type of model. Regression Models Regression models are a statistical measure that attempts to determine the strength of the relationship between one dependent variable (usually denoted by Y) and a series of other changing variables (known as independent variables). Regression models provide the scientist with a powerful tool, allowing predictions about past, present, or future events to be made with information about past or present events. Inference based on such models is known as regression analysis. The main purpose of regression analysis is to predict the value of a dependent or response variable based on values of the independent or explanatory variables. According to Render, Stair, and Hanna (2011) they are two reasons for which regression analyses are used: one is to understand the relation between various variables and the second is to predict the variable's value based on the value of the other. Variables provide many advantages when creating models. One of the advantages...
Words: 1282 - Pages: 6
...occurring periodically. Answer Diff: 2 Page Ref: 682 Main Heading: Forecasting Components Key words: seasonal pattern, forecasting components 3) Random variations are movements that are not predictable and follow no pattern. Answer Diff: 2 Page Ref: 682 Main Heading: Forecasting Components Key words: random variations, forecasting components 4) The basic types of forecasting methods include time series, regression, and qualitative methods. Answer Diff: 2 Page Ref: 683 Main Heading: Forecasting Components Key words: types of forecasting methods 5) Time series is a category of statistical techniques that uses historical data to predict future behavior. Answer Diff: 1 Page Ref: 683 Main Heading: Forecasting Components Key words: time series analysis 6) Regression methods attempt to develop a mathematical relationship between the item being forecast and factors that cause it to behave the way it does. Answer Diff: 2 Page Ref: 683 Main Heading: Forecasting Components Key words: regression methods 7) Qualitative methods use management judgment, expertise, and opinion to make forecasts. Answer Diff: 2 Page Ref: 684 Main Heading: Forecasting Components Key words: qualitative methods 8) Qualitative methods are the least common type of forecasting method for the long-term strategic planning process. Answer Diff: 1 Page Ref: 684 Main Heading: Forecasting Components Key words: qualitative methods ...
Words: 4865 - Pages: 20
...Probability, Statistics, and Forecasting OPRE 433 Fall 2013 Regression Report Xie Gehui (gxx24@case.edu) Dec 2, 2013 I. Introduction The data set given contains more than one independent variable, so the target of our regression analysis is to build an appropriate multiple regression model. To realize this target, we have to build a multiple linear regression model to test the regression assumptions: model appropriateness, constant variance, independence, and normality. Certainly we need to modify the data set or the model itself to satisfy these assumptions, and at last get the model acceptable. In the original data set that we are going to deal with in this report, there are 20,640 observations of 8 explanatory variables labeled X1, X2, X3, X4, X5, X6, X7, X8 and 1 dependent variable labeled Y. All of the 9 variables are continuous. II. Method of analysis To check the model appropriateness assumption, we need to make sure the functional form is correct. The residual plot will show the pattern suggesting the form of an appropriate model. To check the validity of the constant variance assumption, we need to examine residual plots. A residual plot with a horizontal band appearance suggests that the spread of the error terms around 0 is not changing much as the horizontal plot value increases. Such a plot tells us that the constant variance assumption approximately holds. To check the independence assumption, we need to detect if any positive autocorrelation...
Words: 1536 - Pages: 7
...Chapter 3: Answers to Questions and Problems 1. a. When P = $12, R = ($12)(1) = $12. When P = $10, R = ($10)(2) = $20. Thus, the price decrease results in an $8 increase in total revenue, so demand is elastic over this range of prices. b. When P = $4, R = ($4)(5) = $20. When P = $2, R = ($2)(6) = $12. Thus, the price decrease results in an $8 decrease total revenue, so demand is inelastic over this range of prices. c. Recall that total revenue is maximized at the point where demand is unitary elastic. We also know that marginal revenue is zero at this point. For a linear demand curve, marginal revenue lies halfway between the demand curve and the vertical axis. In this case, marginal revenue is a line starting at a price of $14 and intersecting the quantity axis at a value of Q = 3.5. Thus, marginal revenue is 0 at 3.5 units, which corresponds to a price of $7 as shown below. Price $14 $12 $10 $8 $6 $4 $2 Demand $0 0 1 2 3 MR 4 5 6 Quantity Figure 3-1 Managerial Economics and Business Strategy, 7e Page 1 2. a. At the given prices, quantity demanded is 700 units: d Qx = 1000 − 2 (154 ) + .02 ( 400 ) = 700 . Substituting the relevant information into Px 154 = −2 = −0.44 . Since this is less 700 Qx than one in absolute value, demand is inelastic at this price. If the firm charged a lower price, total revenue would decrease. b. At the given prices, quantity demanded is 300 units: d Qx = 1000 − 2 ( 354 ) + .02 ( 400 ) = 300 . Substituting the relevant information...
Words: 4512 - Pages: 19
...Chapter 13 Chapter 13 • Forecasting Forecasting TRUE/FALSE 1. The repeated observations of demand for a product or service in their order of occurrence form a pattern known as a time series. Answer: True Reference: Demand Patterns Difficulty: Easy Keywords: time series, repeated observations 2. One of the basic time series patterns is trend. Answer: True Reference: Demand Patterns Difficulty: Easy Keywords: time series, pattern, trend 3. One of the basic time series patterns is random. Answer: True Reference: Demand Patterns Difficulty: Easy Keywords: time series, pattern, random 4. Random variation is an aspect of demand that increases the accuracy of the forecast. Answer: False Reference: Demand Patterns Difficulty: Easy Keywords: random variation, forecast accuracy 5. Aggregation is the act of clustering several similar products or services. Answer: True Reference: Key Decisions on Making Forecasts Difficulty: Moderate Keywords: aggregation, clustering 6. Aggregating products or services together generally decreases the forecast accuracy. Answer: False Reference: Key Decisions on Making Forecasts Difficulty: Moderate Keywords: aggregation, forecast accuracy 54 Copyright ©2010 Pearson Education, Inc. Publishing as Prentice Hall Chapter 13 • Forecasting 7. Judgment methods of forecasting are quantitative methods that use historical data on independent variables to predict demand. Answer: False ...
Words: 13527 - Pages: 55
...Journal of Data Science 2(2004), 329-346 Estimating Optimal Transformations for Multiple Regression Using the ACE Algorithm Duolao Wang1 and Michael Murphy2 School of Hygiene and Tropical Medicine and 2 London School of Economics 1 London Abstract: This paper introduces the alternating conditional expectation (ACE) algorithm of Breiman and Friedman (1985) for estimating the transformations of a response and a set of predictor variables in multiple regression that produce the maximum linear effect between the (transformed) independent variables and the (transformed) response variable. These transformations can give the data analyst insight into the relationships between these variables so that relationship between them can be best described and non-linear relationships can be uncovered. The power and usefulness of ACE guided transformation in multivariate analysis are illustrated using a simulated data set as well as a real data set. The results from these examples clearly demonstrate that ACE is able to identify the correct functional forms, to reveal more accurate relationships, and to improve the model fit considerably compared to the conventional linear model. Key words: Alternating conditional expectation (ACE) algorithm, nonparametric regression, transformation. 1. Introduction In regression analysis, we try to explain the effect of one or more independent variables (predictors or covariates) on a dependent variable (response). The initial stages of data analysis...
Words: 6000 - Pages: 24
...Hypothesis Test for a Proportion This lesson explains how to conduct a hypothesis test of a proportion, when the following conditions are met: * The sampling method is simple random sampling. * Each sample point can result in just two possible outcomes. We call one of these outcomes a success and the other, a failure. * The sample includes at least 10 successes and 10 failures. (Some texts say that 5 successes and 5 failures are enough.) * The population size is at least 10 times as big as the sample size. This approach consists of four steps: (1) state the hypotheses, (2) formulate an analysis plan, (3) analyze sample data, and (4) interpret results. State the Hypotheses Every hypothesis test requires the analyst to state a null hypothesis and an alternative hypothesis. The hypotheses are stated in such a way that they are mutually exclusive. That is, if one is true, the other must be false; and vice versa. Formulate an Analysis Plan The analysis plan describes how to use sample data to accept or reject the null hypothesis. It should specify the following elements. * Significance level. Often, researchers choose significance levels equal to 0.01, 0.05, or 0.10; but any value between 0 and 1 can be used. * Test method. Use the one-sample z-test to determine whether the hypothesized population proportion differs significantly from the observed sample proportion. Analyze Sample Data Using sample data, find the test statistic and its associated P-Value...
Words: 3650 - Pages: 15
