Regression Analysis Essay

consumers presenting information on the annual income (referred as Income), size of household (referred as Household), and the annual credit card charges (referred as Charges) for these consumers. A statistical analysis; including a descriptive, simple regression, and multiple regression tests, of this data was performed and the findings are presented below. Due to the

August 26, 2012 MATH 533 Course Project Part C Professor Khago Introduction: The following report displays regression and correlation analysis for AJ Davis Department Stores data on credit balance and size. We will use the data collected from 50 credit customers to complete the following analysis; * Generate a scatterplot for CREDIT BALANCE vs. SIZE, including the graph of the "best fit" line. Interpret. * Determine the equation of the "best fit" line, which describes the relationship

Managerial Statistics April 11, 2012 I’m conducting an analysis between the price of a home in Eastville, Oregon and the factors which develop a home’s price. The data is analyzed using ANOVA (Analysis of Variance) and multiple regression hypothesis testing procedures. The regression analysis will help create a multiple regression fit which will incorporate the ten predictor variables of a home’s price. After the regression analysis is complete, global and local ANOVA tests will help eliminate

1.0 Executive Summary Both descriptive and inferential analysis is carried out to study the factors determining the house price in Perlis. Inferential analysis focuses on analysis of correlation and regression in which independent variables are taken into account to predict the house price. Whereas descriptive analysis is run to see the characteristic of the sample used. 2.0 Introduction This research sets out to helping Wood Development to identify various factors determining the setting of housing

account analysis. 4. Estimate costs using statistical analysis. 5. Interpret the results of regression output. 6. Identify potential problems with regression data. 7. Evaluate the advantages and disadvantages of alternative cost estimates.

occur for different months of the year. The study would provide insights into the matter. It may also induce possible investigations of the causes as well as actions to promote business. The real sales revenue over 60 months will be used for the regression analyses. B. Report data collected 1. Describe the relevant data Sales revenue for 60 months is used. This data is historical sales revenue and will be used to predict the trends or patterns in sales if any. 2. Graphic displays The sales revenue

testing and regression techniques. There is an appendix in your textbook, Appendix C: Using Excel to Conduct Analysis, which may help you with running regressions in Microsoft Excel. You may also wish to use a basic statistics text for guidance if needed. I have also provided you with a table with the t distribution. If you have an older version of EXCEL and have not previously loaded the Analysis ToolPak, go to TOOLS, ADD-INS, Analysis Tool Pak. This will load the regression software that

of retaining existing highly profitable customers, but it also shows that there is plenty of room to make non-profitable customers valuable. Profit reaches as high as $2071 in the sample yet the average is only 5% of that value. 2.2. The regression analysis with profit as the dependant variable and online usage as the independent variable is summarized in Exhibit 3. Special attention should be given to the adjusted r-squared value, the co-efficient of the online variable, and the p-value. The

Literature includes CEO cash compensation as the proxy for the CEO’s level of risk aversion (Rogers, 2002; Nam et al., 2003; Coles et al., 2006). CEOs with higher cash compensation are more likely to become more risk avers. CEO’s with high cash compensation will seek to avoid risk. I measure cash compensation as the natural logarithm of the salary and the natural logarithm bonus. Following existing literature (e.g., Guay, 1999; Rajgopal and Shevlin, 2002) the sensitivity of CEO wealth to stock

econometric variables. It is his intention to base his forecasting decisions and projections for the upcoming year on results derived from analysis surrounding this theory. An initial analysis was conducted on historic sales data using excel to calculate descriptive statistics. From there several other forecasting models were introduced: Regression with seasons, Regression with factors, Holt Winters Additive and Multiplicative were also used in predicting quarterly sales for 2008. Based on the outcomes