CWRU Regression Project Report OPRE 433 Tianao Zhang 12/5/2011 Introduction According to the data I’ve received‚ there are 6578 observations. The data base is composed by 13 columns and 506 rows. All the explanatory variables are continuous as well as the dependent variable and there are no categorical variables. My goal is to build a regression model to predict the average of Y or particular Y by a given X. 1. Do the regression assumptions such as Constant Variance‚ Normality and Independence
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3.2.3 The Stepwise Regression Analyses Table 4 and 5 showed the result of multiple regression analysis of critical thinking (CT) and speaking Skill (SS) achievement. The correlation among the Debate and context‚ issue‚ implication‚ and assumption was 0.923 or 92.3% and the influence of contribution of the whole aspects of critical thinking (CT) was 0.821 or 82.1%. Partially‚ the contribution of each aspect of critical thinking (CT) toward critical thinking (CT) achievement was as follows: context
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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 fit a linear trend line to the normal probability plot
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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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Linear Least Squares Suppose we are given a set of data points {(xi ‚ fi )}‚ i = 1‚ . . . ‚ n. These could be measurements from an experiment or obtained simply by evaluating a function at some points. You have seen that we can interpolate these points‚ i.e.‚ either find a polynomial of degree ≤ (n − 1) which passes through all n points or we can use a continuous piecewise interpolant of the data which is usually a better approach. How‚ it might be the case that we know that these data points should
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Chapter 2: Linear Functions Chapter one was a window that gave us a peek into the entire course. Our goal was to understand the basic structure of functions and function notation‚ the toolkit functions‚ domain and range‚ how to recognize and understand composition and transformations of functions and how to understand and utilize inverse functions. With these basic components in hand we will further research the specific details and intricacies of each type of function in our toolkit and use
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5645 | 3.17 | 32.11 | 2010 | 4284 | 3.28 | 31.23 | 2011 | 3674 | 2.65 | 24.16 | Using regression analysis we want to determine the relationship between ROA‚ ROE and stock price of PT BCA Tbk. In this case‚ ROA and ROE are the independent or explanatory variable (X)‚ while stock price is the dependent variable that we want to explain (Y). Regression Analysis SUMMARY OUTPUT | | | Regression Statistics | Multiple R | 0.13028475 | R Square | 0.016974116 | Adjusted R Square | -0
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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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Dell? 5 Revitalizing Dell: Forecast Dell’s 2009 and 2010 revenues • Work through the “Proposed Steps” of Case 9-1 Revitalizing Dell in your textbook – Make lagged drivers – Use correlation to pick a lagged driver – Build a linear forecast model using regression‚ perform DW test on residuals – Repeat if residuals do not pass DW test • Forecast revenues and generate 95% prediction intervals for 2009 and 2010 6 Revitalizing Dell: Bright forecast 7 Revitalizing Dell: Harsh reality
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in ‘000’s‚ average population income‚ average number of cars owned by households‚ and median age of dwellings. These quantitative variables are the key determinants‚ which will provide substance for descriptive statistics and the multiple linear regression model. This research reports mainly on statistical analysis‚ providing a direct interpretation of the research results. This process quantitates subjective judgments‚ while offering a scientific method of selecting location when chain convenience
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