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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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 8 Revitalizing
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STA9708 Regression Analysis: Literacy rates and Poverty rates As we are aware‚ poverty rate serve as an indicator for a number of causes in the world. Poverty rates are linked with infant mortality‚ education‚ child labor and crime etc. In this project‚ I will apply the regression analysis learned in the Statistics course to study the relationship between literacy rates and poverty rates among different states in USA. In my study‚ the poverty rates will be the independent variable (x) and literacy
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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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| 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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Introduction The number of empirical work studying house prices and their links to the other factors are persistently on the rise. Housing market research has been topical since its role in the recent global economic crisis‚ specifically referring to the recent boom in house prices in many developed countries following a sharp bust in 2008. Researches and policy makers alike have realized that housing has significant influences on the business cycle. This paper tries to figure out the determinants
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‘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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created an adjusted economic model that I have specified above. In order to test my economic model‚ I have compiled data for 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
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REGRESSION 1. Prediction Equation 2. Sample Slope SSx= ∑ x2- (∑ x)2/n SSxy= ∑ xy- ∑ x*∑ y/n 3. Sample Y Intercept 4. Coeff. Of Determination 5. Std. Error of Estimate 6. Standard Error of 0 and 1 7. Test Statistic 8. Confidence Interval of 0 and 1 9. Confidence interval for mean value of Y given x 10. Prediction interval for a randomly chosen value of Y given x 11. Coeff. of Correlation 12. Adjusted R2 13. Variance Inflation
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SIMPLE VERSUS MULTIPLE REGRESSION The difference between simple and multiple regression is similar to the difference between one way and factorial ANOVA. Like one-way ANOVA‚ simple regression analysis involves a single independent‚ or predictor variable and a single dependent‚ or outcome variable. This is the same number of variables used in a simple correlation analysis. The difference between a Pearson correlation coefficient and a simple regression analysis is that whereas the correlation does
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