HW#3 Run regression analysis using the Energy Drinks Data posted on elearning. You can work by yourself‚ or work in a group (up to 5 students per group) and submit one homework per group. 1. (a) Run the linear regression model that express quantity sales (oz) of Full-Throttle as the dependent variable; the list of explanatory variables are price of Full-Throttle‚ the price of Monster‚ price of Red Bull‚ price of Rockstar and customer count. Submit the excel output. What is the R2 value? What
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The methodology of this study is use Augmented Dickey Fuller (ADF) test statistic to determine whether the variables had been used are stationary or non-stationary. Vector Auto Regression (VAR) method is apply in this study. The advantages of VAR is time series can be exhibited at the same time. The VAR methodology is revises for autocorrelation and endogeneity parametrically using vector error correction model (VECM) specification. Base on Johansen (1988; 1995)‚ the benefit of VECM is that it prevents
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LINEAR REGRESSION MODELS W4315 HOMEWORK 2 ANSWERS February 15‚ 2010 Instructor: Frank Wood 1. (20 points) In the file ”problem1.txt”(accessible on professor’s website)‚ there are 500 pairs of data‚ where the first column is X and the second column is Y. The regression model is Y = β0 + β1 X + a. Draw 20 pairs of data randomly from this population of size 500. Use MATLAB to run a regression model specified as above and keep record of the estimations of both β0 and β1 . Do this 200 times. Thus you
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Time Series Regression 3.1 A small regional trucking company has experienced steady growth. Use time series regression to forecast capital needs for the next 2 years. The company’s recent capital needs have been: ══════════════════════════════════════════════ Capital Needs Capital Needs (Thousands Of (Thousands Of Year Dollars) Year Dollars) -------------------------------------------
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Answers to Midterm Test No. 1 1. Consider a regression model of relating Y (the dependent variable) to X (the independent variable) Yi = (0 + (1Xi+ (i where (i is the stochastic or error term. Suppose that the estimated regression equation is stated as Yi = (0 + (1Xi and ei is the residual error term. A. What is ei and define it precisely. Explain how it is related to (i. ei is the residual error term in the sample regression function and is defined as eI hat = Y
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Chapter 4 Simple regression model Practice problems Use Chapter 4 Powerpoint question 4.1 to answer the following questions: 1. Report the Eveiw output for regression model . Please write down your fitted regression model. 2. Are the sign for consistent with your expectation‚ explain? 3. Hypothesize the sign of the coefficient and test your hypothesis at 5% significance level using t-table. 4. What percentage of variation in 30 year fixed mortgage rate is explained
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Regression Modeling for Brand Xmarcom Strategy Analytical approach using Tracking Research data Approach: The analysis of brand Sofy has been done with a two stages of statistics and model building approach. MATRIX IDENTIFICATION At the very first stage the data for Sofy was plotted in scatter graphs for pattern identification. The various combinations of variables for independent and dependent variables were taken to shortlist the variables for further scientific tests. TEST AND ANALYTICS
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SECTION A (You should attempt all 10 questions) A1. Regression analysis is ____________________________________. A) describes the strength of this linear relationship. B) describes the mathematical relationship between two variables. C) describes the pattern of the data. D) describes the characteristic of independent variable. A2. __________________ is used to illustrate any relationship between two variables. A) Histogram B) Pie chart C) Scatter diagram D) Frequency
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Assignment Week 1 Answer the following questions: 1. Describe the rationale for utilizing probability concepts. For practical reasons‚ variables are observed to collect data. The sampled data is then analyzed to elicit information for decision making in business and indeed in all human endeavors. However‚ sampled information is incomplete and not free from sampling error. Its use in decision-making processes introduces an element of chance. Therefore‚ it is important for a decision-maker
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correlated. To choose which variable should be kept‚ two regressions must be run‚ one without each variable. When evaluating these regressions‚ the variable that leads to the higher R Square value needs to
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