Regression Analysis of Army Jackboots Ochirmunkh Boldbaatar‚ Myriam Hirscher‚ Bastian Latz‚ and Manuel Padutsch ECON 510 Aun Hassan November 26‚ 2012 Introduction The German company we established the data from sells cloths and shoes. The customers are not private customers but mostly national divisions like the military or fire departments. The company has around 20 stores in Germany; however‚ the stores have different prices for the same products. The data package we received includes
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Javier Jorge Dr. Moss Managerial Analysis April 11th‚ 2012 Project 3 We are given a linear regression that gives us an equation on the relationship of Quantity on Total Cost. As stated in the project‚ the regression data is very good with a relatively high R2‚ significant F‚ and t-values but we can’t use this model to estimate plant size. When we perform a simple eye test on the residual plot for Q a trend seems to form from positive to negative and back to positive. When we also
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1. Affirmative Action destroys the idea of meritocracy and students should be chosen based on their intelligence instead of their race or gender. “At the University of Wisconsin‚ the median composite SAT score for blacks who were admitted was 150 points lower than for whites and Asians and the Latino median SAT score was 100 points lower”. This quote shows how Affirmative Action destroys the idea of meritocracy and applicants are mainly chosen on someone’s race not intelligence. (http://brandongaille
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Analysis on Inflation Regression Model Done by: Hassan Kanaan & Fahim Melki Presented to: Dr. Gretta Saab Due on: Tuesday‚ January 25‚ 2011 Outline: I. Introduction A. Definition of Variables B. Type of Variables II. Background and Literature Review A. Inflation and Unemployment B. Inflation and Oil Prices C. Inflation and GDP D. Inflation and Money Supply III. Analysis A. SPSS 17 analysis B. E-Views 5 analysis IV. Conclusion and Recommendation V. Indexes
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relationship between CREDIT BALANCE and SIZE 2591+ 403.221 Determine the coefficient of correlation. Interpret. .75/ r-sq(56.6%). There is a mild correlation. Determine the coefficient of determination. Interpret. 56.6% Test the utility of this regression model (use a two tail test with α =.05). Interpret your results‚ including the p-value. P-value=0. Reject the null hpothesis. T value 7.9147 Based on your findings in 1-5‚ what is your opinion about using SIZE to predict CREDIT BALANCE? Size
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| 70 | 29 | E | 22 | 6 | F | 27 | 15 | G | 28 | 17 | H | 47 | 20 | I | 14 | 12 | J | 68 | 29 | | | | | | | a) draw a scatter diagram of number of sales calls and number of units sold b) Estimate a simple linear regression model to explain the relationship between number of sales calls and number of units sold y=2.139x-1.760 Number of units sold=2.139Number of units sold-1.760 c) Calculate and interpret the coefficient of correlation r=0.853=0.9236 (There
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each of the variables specified in the model from the years 2003 to 2005. The question that I will be answering in my regression analysis is whether or not wins have an affect on attendance in Major League Baseball (MLB). I want to know whether or not wins and other variables associated with attendance have a positive impact on a team ’s record. The y variable in my analysis is going to be attendance for each baseball team. I collected the data for each team ’s average attendance for 2003-2005
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P(x) = 300 — 4x. The cost function is c(x) = 500 + 28x where x is the number of units produced. Find x so that the profit is maximum. Question: 1) Find the value of x. 2) In using regression analysis for making predictions what are the assumptions involved. 3) What is a simple linear regression model? 4) What is a scatter diagram method? CASE STUDY : 3 Mr Sehwag invests Rs 2000 every year with a company‚ which pays interest at 10% p.a. He allows his deposit
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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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significant influences on the business cycle. This paper tries to figure out the determinants of the selling price of houses in Oregon. The data set used in this paper has been retrieved from the case study titled “Housing Price” (Case #27 - Practical Data Analysis: Case Studies in Business Statistics- Marlene A. Smith & Peter G. Bryant) The most important factor in determining the selling prices ofhouses is to know the features that drive the selling prices of the house. People tend to have more interest
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