13 4 Regression model and diagnostics ......................................................... 14 4.1 Simple linear regression ....................................................................................... 14 4.2 Why the gas pipeline dummy is underestimated.................................................. 15 4.3 Robustness testing ................................................................................................ 19 Appendix A Alternative regression models ...
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analysis of multivariate data is a table of means and standard deviations. Additional analysis recommendations include histograms of all variables with a view for outliers‚ or scores that fall outside the range of the majority of scores. In a multiple regression analysis‚ these score may have a large "influence" on the results of the analysis and are a cause for concern. In the case of the example data‚ the following means and standard deviations were computed using SPSS/WIN by clicking of "Statistics"
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2.1 2.2 | Estimated regression equations. Independent Variable- Annual Income. Independent Variable- Household Size | 7 8 9 | 3 | Better predictor of annual credit card charges | 10 | 4 | Independent variables- Annual income and Household size | 11 | 5 | Forecasting Annual Credit Charge | 12 | 6 | Need for other independent variables | 13 | 7 | Test the significance of the overall regression model | 14 | 8 | Test the significance of the individual regression coefficients | 15-16
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(cents) • X4 = Real retail price of beef per lb. (cents) • X5 = Composite real price of chicken substitutes per lb. Step 1) Check for the overall utility of the model The regression analysis output is given below. Regression Analysis: lbs. per Cap versus Real Disposa‚ Real Retail‚ ... The regression equation is lbs. per Capita Consumed = 39.5 + 0.00204 Real Disposable Income per Capi - 0.129 Real Retail Price of Chicken +
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References: [1] Cellular operators of India www.coai.com [2]Census 2001 censusindia.gov.in [3] Regression selection strategy & revealed priors; Edward Learner‚ J.of American stat Assoc‚ Vol-73 no-363(sept-1978)‚ pp-580-587. (jstor.org/stables/2286604) [4] The Logistic curve: A fitting technique; D.F.Phipps.‚ The Statician‚ Vol-24‚No-2‚(Jan-1975)‚pp-129-136. [5] The Acceptability of Regression solutions : Another look at computational accuracy.‚Albert E. Beaton‚ Donald R. Rubin & Jhon L.Barone
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chicken. The correlation coefficient =.794‚ which means this does have a positive linear correlation. (1.2) Run a linear regression: Chicken price vs. consumption Chicken w/beef+pork+ beef Chicken consumption over time increases Chicken consumption=b0+b1*Income+b2*chicken price+b3*pork price+b4*beef price What’s the estimated regression equation? (5pts.) (1.3) Identify significant and insignificant variables. Does the chicken consumption statistically
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CHAPTER 10 DETERMINING HOW COSTS BEHAVE 10-16 (10 min.) Estimating a cost function. 1. Slope coefficient = = = = $0.35 per machine-hour Constant = Total cost – (Slope coefficient Quantity of cost driver) = $5‚400 – ($0.35 10‚000) = $1‚900 = $4‚000 – ($0.35 6‚000) = $1‚900 The cost function based on the two observations is Maintenance costs = $1‚900 + $0.35 Machine-hours 2. The cost function in requirement
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BUAD 310 Spring 2013 Case Due by 4PM on Friday‚ May 3rd (in BRI 400C) In this case you will apply statistical techniques learned in the Regression part of BUAD 310. Please read the following instructions carefully before you start: • This assignment uses data from the file MagAds13S.XLS‚ which you can download from Blackboard. After you download the file go to Data → Load data → from file in StatCrunch to open it (you don’t need to change any of the options when loading this
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PROJECT PART C: Regression and Correlation Analysis Math-533 Applied Managerial Statistics Prof. Jeffrey Frakes December 12‚ 2014 Jared D Stock 1. Generate a scatterplot for income ($1‚000) versus credit balance ($)‚ including the graph of the best fit line. Interpret. This scatter plot graph is a representation of combining income and credit balance. It shows the income increasing as the credit balance increases. As a result of this data it can be inferred that there is
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zero. Why? This just means that there is no linear correlation between all three variables advertising/location/price. 4) Run regressions for each sales variable (s1‚ s2‚ s3) using P‚ A‚ L and independent variables. What do the regressions imply about the effect on price? Of advertising? Of location? In sales period one the coefficients table of the regression reveals that there is statistical significance between the price variable and sales. It is a negative correlation which implies that
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