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Regression Analysis

Regression analysis is a statistical process for estimating the relationships among variables. Regression analysis includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. This analysis also helps one understand how the typical value of the dependent variable changes when any one of the independent variable is varied, while the other independent variable are held fixed. It is used to estimate the conditional expectation of the dependent variables.

Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. It is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In some scenarios, regression analysis can be used to infer causal relationships between the independent and dependent variables.

Regression models involve the following variables, the unknown parameters, β, the independent variable, X, and the dependent variable, Y. In various fields of application, different terminologies are used in place of dependent and independent variables. A regression model relates Y to a function of X and β by the following

Y = f(X, β)

In linear regression, the model specification is that the dependent variable is a linear combination of the parameters. For example, the simple linear regression for modeling n data points there is one independent variable and two parameters. 

Categories within Regression Analysis

Errors and Residuals

Postings: 5

Errors and residuals are measures of the deviation of an observed value of an element of a statistical sample from its "theoretical value".

Regression Model Validation

Postings: 27

Regression model validation is the process of deciding whether the numerical results quantifying hypothesized relationships between variables, obtained from regression analysis, are in fact acceptable as descriptions of the data.

Mixed Effects Models

Postings: 3

A mixed model is one containing both fixed effects and random effects, that is mixed effects.

ANOVA, pivot table, chi square test for independence

ANOVA:   NYU wants to use a new tutorial to teach the students about business ethics.  As an experiment the administrator randomly selected 15 students and randomly assigned them to one of three groups which include either a PowerPoint presentation created by the faculty, AuthorGen Presentation created by the faculty, or a w

Statistical Inference Consumption

Need help answering the following questions: 1. Do you think economics should be used in helping formulate shifts in food consumption or do you think we should leave consumers to make their own choices? Explain your position. 2. Given your knowledge of price elasticity of demand, what do you think the impact of taxing sof

Regression Analysis Calculations

Zagat's publishes restaurant ratings for various locations in the United States. The file RESTRATE.xls contains the Zagat rating for food, décor, service, and the price per person for a sample of 53 restaurants located in New York City and 53 restaurants located in Long Island. You want to develop a regression model to predict

Cancer study in statistics

This question is taken out of "Fundamentals of Biostatistics" which I need help on. The program we are using is R

Interpretation of regression output

Citizens' Forum for Poverty Alleviation has fitted a regression equation to estimate the expenditure on food items of rural households in Karnataka. They had used family size defined as the number of members in the family (Size), Expenditure on education for children (Education), and Income (in hundreds of rupees) as the explana

Step-wise logistic regression using SPSS

An HIV researcher explored the factors that influenced condom use with a new partner (relationship less than 1 month old). The outcome measured was whether a condom was used (Use: condom used =1, used = 0). The predictor variables were mainly scales from the Condom Attitude Scale (CAS) by Sacco, Levine, Reed, and Thompson (1991)

Fit autoregressive model to GE stock price under different order

Problem 16.30 GE.xls Autoregression page 677 page 275 The data in the table represent the January 1 stock price for the 20-year period fro 1987 to 2006 for General Electric, one of the world's largest companies. Year Coded Year GE Stock Price 1987 0 2.25 1988 1 2.36 1989 2

Test for Regression Coefficient using Correlation Coefficient

Use a simple regression model to test the null hypothesis against the alternative Ho: Beta1 = 0 H1: Beta1 Does NOT = 0 with alpha = 0.05 , given the following regression statistics: a. The sample size is 35, SST = 100,000, and the correlation between X and Y is 0.46. b. The sample size is 61, SST = 123,000, and the corr

Regression Analysis: Columbia Drug Stores

Assignment: In five-to-seven pages of double-spaced writing in a Word document, answer the following questions: 1. Based on the text above, build a multiple linear regression population model to analyze the impact of the preceding determinants on Columbia's profitability. What is the multiple linear regression population equ

Simple Regressions and Multiple Regressions

What is simple linear regression? How is regression used to create equations and to make predictions? Should variables be strongly correlated in order to use one to predict the other? How is regression used to generate prediction equations in SPSS? How are prediction equations used to make predictions? What is mu

Xtreg and areg - Robust Regression Analysis with STATA

For this exercise, you will write your do-file from scratch. Don't forget the Help option if you don't know how to do something. Make sure your do-file is well organized and well annotated. Note: At the end of this document, you will find instructions for an alternate way to create a do file. Please submit: a) A print

Fixed model - Robust Regression Interpretation of results

Question One: This problem employs a dataset on labor markets in 23 OECD countries for the years 1980 to 1998. The variables used in the analysis (followed by descriptive statistics) are: 1. Productivity index [prod] = An index measuring country i's economic output (GDP) per hour worked in year t, normalized such that e

OLS and OLS (Robust Regression Analysis) with STATA

Heteroskedasticity Diagnostics and Corrections For this exercise, use newschools9810.dta. Please download the do-file for this assignment, "Class 8 Exercise 2014.do," from the course website, and perform all the required statistical operations as directed below. Please submit: a) A printed Stata log file documenting that

Simple regression analysis using state crime case

We are asked to investigate the hypothesis that the number of crimes is related to police expenditures because states with higher crime rates are likely to increase their police force, thereby spending more on the number of officers on the street. State Number of Crimes (per 100,000 population) Police Protectio

Performing regression analysis using real data

There is thought to be a relationship between a persons' educational attainment and the number of children he or she has. The hypothesis is that as one's educational level increases, he or she has fewer children. Investigate this conjecture with 25 cases. Draw from the 2006 GSS file. The following displays educational attainmen

Calucation of Multiple Regression Model

Please help me solve this problem. All the data is in the file pamsue.xls, find attached also the Table A & B that is referred to in question 2 & 3. This case involves the decision to locate a new store at one of two candidate sites. The decision will be based on estimates of sales potential. Problem 1 Use the final regre

Multiple Regression Analysis for Normal Probability

1) If sample mean plots look essentially parallel, we can intuitively conclude there is an interaction between the two factors. True or false, give a reason why? 2) When we carry out a chi-square test of independence, in the alternative hypothesis we state that the two classifications are statistically independent. True

Calculate R-squared in multiple regression

Let's begin by listing all of the variables. Response: The amount of time spent in the hospital Explanatory: age, cholesterol level, blood pressure, and the hospital you are in. Note that one patient cannot be in two hospitals at once, so there is no interaction term between hospitals. Your regression equation should be in t

Quadratic regression models

Below is a partial multiple regression computer output based on a quadratic regression model to predict student enrollment at a local university. The dependent variable is the annual enrollment given in thousands of students, the independent variable X is the increase in tuition stated in thousands of dollars per year, and X2is

Multiple Regression Analysis: Smart Alex

Complete Smart Alex's Task #1 attached to perform a multiple regression analysis using the Supermodel.sav dataset attached. - State underlying assumptions - Determine whether assumptions have been met - Propose alternatives if assumptions are not met - State null and alternative hypotheses - Analyze data using IBM SPSS S

Multiple Regression: Coding with SPSS

The data file contains 6 variables. The dependent variable is reading comprehension (reading), the independent variables are phoneme awareness (phoneme), visual perception (visual), morpheme awareness (morpheme), gender (1-female, 2-male) and LS (Learning Style:1-visual, 2-auditory, 3-kinesthetic). Use SPSS for this project:

Nonparametric, Regression, and Correlation problems

Dentistry In a study, 28 adults with mild periodontal disease are assessed before and 6 months after implementation of a dental- education program intended to promote better oral hygiene. After 6 months, periodontal status improved in 15 patients, declined in 8, and remained the same in 5. 9.4 Suppose there are two samples of

Economics in American Firms: Multiple Regression Analysis

In recent years, many American firms have intensified their efforts to market their products in the Pacific Rim. A consortium of U.S. firms that produce raw materials used in Singapore is interested in predicting the level of exports from the U.S. to Singapore, as well as understanding the relationship between U.S. exports to Si

Correlation and Regression Questions

In this problem set you will get some practice performing a linear regression analysis. If you use Statdisk or Excel to perform any portion of these analyses, please include the results, label them, and refer to them accordingly in your interpretations. Listed below are the overhead widths (in cm) of seals measured from

Regression Analysis of Computer Software

A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the not complete output from a simple linear regression along with the re

Correlation Matrix and Multiple Regression Output

I am having a difficult time with this question. Variables Defined: Lottery: How many times a sampled customer has purchased California lottery tickets in the past two months Education: How many years of education the sampled customer has completed Age: The age of the sampled customer in years Children: How many chi

Multiple regression: City MPG

Perform a complete multiple regression using City MPG as the response variables. Assess the model using the steps as performed/outlined (correlation matrix, f-test, t-tests, r-sq and standard error). If the full multiple regression needs modification, perform a stepwise regression and select the final model. Briefly note why you

Regression of MedIncome

a. Perform a simple regression using MedIncome as the explanatory variable for Sale/Sq Ft. Assess the model by performing the test of significance on the slope, determining and interpreting R-sq and briefly assessing the standard error of the estimate. b. Perform a simple regression using MedAge as the explanatory variable f

Linear Regression Relationship Analysis

Ten students in a graduate program were randomly selected. Their grade point averages (GPAs) when they entered the program were between 3.5 and 4.0. The following data were obtained regarding their GPAs on entering the program versus their current GPAs: Entering GPA 3.5 3.8 3.9 3.7 4 4 3.6 3.9 3.7