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Regression Analysis

Regression and Hypothesis

The director of an investing group believes that the sales generated by a broker is related to the number of new clients a broker brings to the firm. The director believes that a linear regression model would help in the forecasting of expected sales from the brokers. To build the model the director takes a random sample of 12

Regression Model with Advertising Expenditures and Price Index

Laura wanted to build a multiple regression model based on advertising expenditures and coffee times price index. Based on the selection of all normal values she obtained the following: 1) Multiple R = 0.738 2) R-square = 0.546 By using lagged values she came up with the following: 3) Multiple R = 0.755 4) R-square


Find a bivariate data set relating to your work with a sample size of 10 or more. a) Determine which is the dependent variable Y and which is the independent variable X. Explain your reasoning. b) Draw a scatterplot and indicate the relationship if any. Explain. c) Compute the least-squares regression equation. Show majo

Interpret the multiple regression analysis

EXHIBIT 1: A manager at a local bank analyzed the relationship between monthly salary and three independent variables: length of service (measured in months), gender (0=female, 1=male) and job type (0=clerical, 1=technical). The following tables summarizes the regression results: df

Linear Regression

Q1. A firm has the following data on Sales and Advertising. I want to estimate the following regression: sales=B0 + B1*advertising; specifically, what is the estimate of the slope term, B1. The following 10 observations are included: Advertising Sales 339 1103 451 921 504 1154

Regression problem

Thompson Machine Works purchased several new, highly sophisticated machines. The production department needed some guidance with respect to qualifications needed by an operator. Is age a factor? Is the length of service as a machine operator important? In order to explore further the factors needed to estimate performance on the

Regression in SPSS

Assume that you are an analyst for the City of Normalton in 1999. You have been asked by the Mayor to evaluate the effectiveness of two juvenile crime policies: The strict curfew put in place on Jan1, 1994 and The parental responsibility law put in place on March 1, 1994. The police department has provided you with the da


Using the BANK.SAV data file, construct a multiple regression model to test whether there is gender discrimination in the bank. The model should include one or more continuous variables and one or more dichotomous variables. Please submit to me the output you generate Describe your rationale for including the variables tha

Chi-square test and Regression analysis

1) What is the Chi-square test? Where can you apply it? Please site an example of how it was used and the outcome of the test. I think this could help me better than the book, it is difficult to understand. 2) What is regression analysis and multiple regression? How would they be used in an outpatient setting? Please indentif

Statistics Problems - Regression Analysis, Autocorrelation, Multicollinearity

1. Suppose an appliance manufacturer is doing a regression analysis, using quarterly time-series data, of the factors affecting its sales of appliances. A regression equation was estimated between appliance sales (in dollars) as the dependent variable and disposable personal income and new housing starts as the independent varia


Baseballs major leagues honor their outstanding first-year-players with the title "Rookie of the Year." From 1949 to 1994, the overall batting average for Rookies of the Year was .285 far above the major league batting average of .260. However, Rookies of the Year don't do so well in their second year- their overall bating avera

Statistic Multiple Regression Model. Explain how to further optimize the model

Bob wanted to build a multiple regression model based on advertising expenditures and price index. based on the selection of all normal values, he obtained the following: 1) Multiple R - 0.738 2) R-Square - 0.546 By using lagged values, he came up with the following: 3) Multiple R - 0.755 4) R-Square - 0.570 Explai

Explaining results by the simple linear regression

A linear regression analysis produced the equation: Profits = - $950 + $85 * no. of development hours a) How large would the profits or losses be if no time is spent in development? b) On average, an extra 20 hours spent in development produces what increase in profits? c) What is the break-even point - the number of hours f

Case Study - Correlational Research, Causation, Practical Use of Results

Share the practical applications of the study from the Unit 2 Individual Project. How would the results of this survey be used in the workplace? Briefly describe correlational research. Name a variable from this study and one from the workplace that might prove to provide a correlational relationship and explain why you woul

Simple and Multiple Linear Regression

Download the data file ex1.XLS. It contains a variety of aircraft operating cost data and statistics by aircraft type. All figures are averages for all aircraft operated by US carriers, taken from 2003 Form 41 data. Use the data in this file to perform the analysis of operating costs described below: (A) Use Excel to esti

Multiple regression model testing

I need to use my attached database to develop a multiple regression model with the dependent variable being the overall job satisfaction. Use gender, age, department, position and tenure with company as the independent variables. I need to provide output of the regression procedure but also the model equation. Also, I

Correlation and regresssion

The director of a national research institute belives that more comprehensive institutions have lighter teaching responsibility is and more time for research and scholarship. He develops a measure of institutional comprehensiveness and scholarly productivity for psychology departments. He selects 18 colleges and univ. and these

Interpreting integrity tests data on a regression analysis

I need someone to critically assess the relative merits/weaknesses of a economic modeling equation and the subsequent integrity tests performed on the 40 year time series data. Attached is a word document with screenshots of various EViews results/tests/diagrams, etc and an excel file with the associated data. Also attached is t

Linear regression analysis and correlation: Using this information, construct 95% confidence intervals for the regression parameters b0 and b1 . At .05 level of significance, test the null hypothesis that the population correlation coefficient is 0.

1. A computer company wants to study the relationship between the number of microcomputers in use in different areas and the number of software packages the company sells in the areas. A simple linear regression analysis of 21 geographical regions reveals the following: b0=12.43, b1= 1.076, s(b0)=13.65, s(b1)=0.083 , SSE (sum of

Regression Equations of Real World Value

Please help me with these questions: 1. What is the real world value of the y-intercept and slope in a regression equation? 2. Other than statistics problems, why should I care? 3. I know how to calculate, but what does it really mean when you apply it in a business setting? 4. When you use them in forecasting, how

Business Statistics and Multiple Regression

Using the database (attached file), complete the followings: a) Develop a Multiple Regression model with the dependent variable being the Overall Job Satisfaction. Use gender, age, department, position and tenure with company as the independent variables. Comment on your analysis (Rsq, Rsq adj, etc). You are to provide not on

Interpreting Regression Analysis Output from EViews

I need to be able to critically assess a regression analysis printout from EViews (sample attached) and be able to identify possible issues - i.e.: - potential heteroskedasticity - potential autocorrelation - potential multicollinearity problem prior to running the specific tool which provides further analysis for one of

Regression: ANOVA

An experiment was conducted to investigate the effect of four treatments, A, B, C and D on the yield of penicillin in a manufacturing process. It was necessary to use a different blend for each application if the four treatments. The results of the yields for this randomised block experiment are given in the table below.