## CHECK THESE SAMPLES OF Multiple Correlation and Regression

...? Investigating the determinants of property crime in the United s Introduction This report is an investigation of the determinants of property crimes in the United States. By property crimes, typically the references is to burglary, larceny, theft and motor vehicle theft. We seek to gain an understanding of what socio-economic characteristics of a particular state make it more susceptible to a larger number of property crimes. Data and methodology By using **multiple** **regression** analysis, the report is in pursuit of obtaining statistical evidence for or against commonly held beliefs regarding causality of various factors and crime. We use a State-wide data set that includes a record of property crimes rates...

6 Pages(1500 words)Essay

...variable, the underlying causal mechanism between these variables may not actually be ascertained (Wang & Jain, 2003). Multicollinearity is also a problem with **multiple** **regression** techniques. Multicollinearity is the problem that surfaces in **multiple** linear **regression** when two or more independent variables are highly **correlated** (Yan & Su, 2009). For example, in predicting a student’s SAT score, with raw score and percentage score as predictors would be redundant because one is merely a different representation of the other. Thus, it is usually prudent to check for collinearity among the independent variables and appropriately decide which predictor to...

20 Pages(5000 words)Term Paper

...?Executive Summary **Multiple** **regression** is an effective technique to identify a relationship between one dependent variable and **multiple** independent variables. It is hypothesized the property crime rates per thousand inhabitants is dependent on **multiple** factors such as per capita income, school dropout percentage, population density, percentage of people living in urban area and so on. In order to establish a certain relationship between the variables, **multiple** **regression** was used. While crimes is the dependent variable, other variables such as state, per capita income, dropouts, average precipitation, public aid recipients, population...

6 Pages(1500 words)Term Paper

...? **Multiple** **Regression** Below is the data that I have been collecting to There is a difference between simple **regression** and **multiple** **regression**. Simple **regression** analysis is used to establish the relationship between one variable and the other. One set of the variables is the dependent variable and the other set is the independent variable. The independent variable changes and in turn affects the dependent variable. Simple **regression** is used to establish whether or not indeed the independent variable determines the change that takes place in the dependent variable. It is also used to establish the way in which the two...

2 Pages(500 words)Coursework

...**Multiple** **Regression** **Multiple** **Regression** is a tool which involves a single dependent variable and two or more independent variables. The general form of the **multiple** **regression** is Y= a +b1X1 +b2X2+b3X3...+bnXn + e which can be estimated by Y= a +b1X1 +b2X2+b3X3...+bnXn, where a is the intercept and bi's are the partial **regression** coefficients.
The various statistics associated with **Multiple** **Regression** are
**Regression** Coefficient: The estimated parameter of b
Standard Error of Estimate:
Is the standard deviation of the actual values of Y from the predicted values of...

10 Pages(2500 words)Essay

...**Multiple** **Regressions** Empirical Project May Table of Contents May Table of Contents 2 List of Tables 2 List of Figures 2 Executive Summary3
Introduction 4
Literature Review 4
Economic theory 4
Data 5
Hypothesis 5
The Model 5
Empirical Results 5
Autocorrelation Test 5
Heteroskedasticity Test 6
**Multiple** **Regression** Analysis 7
Endogenity Test of the Full Model 8
Simple Linear **Regressions** 8
Conclusion 11
References 12
List of Tables
Table 1: Heteroskedasticity Test 6
Table 2: Full Model Coefficients 7
Table 3: Model with Consumption as the Explanatory Variable 8
Table 4: Model with Foreign Direct Investment as the Explanatory Variable 9
Table 5: Model with Net...

10 Pages(2500 words)Essay

...**Correlation** and **Regression** Question The ment “Cigarette Smokers Make Lower Grades than Nonsmokers” reflects a certain relationship between the two activities which are smoking cigarettes and making grades. Therefore, statistically both these activities can be denoted as the two variables i.e. smoking cigarette is one variable and making grades is another variable. By looking at the statement, there seems to be identified a very clear and obvious relationship between cigarette smoking and making grades such that high cigarette smoking leads to lower grades and vice versa. From this, it can be observed that if cigarette smoking is increased, it will result in lower grades and if cigarette smoking is...

2 Pages(500 words)Essay

...**Multiple** **regression** and **correlation** techniques The aimed at addressing controversies and developing innovations in research. Some of the identified controversies regard linking theory to hypothesis and testing of mediator or moderator variables. Linking theory to hypothesis is controversial because of existence of diversified literature between theory and hypothesis. Existence of diversified literature on subjects offers contradictory information that that may confuse researchers’ development of hypothesis through determination of dependent variables. An example of the source of the controversy is existence of direct and indirect factors to explanatory variables that may blur significance...

1 Pages(250 words)Assignment

...for including too many parameters that do not contribute much in explaining the original variance. It is a modification of R2.
R2
Adjusted R2
3. Multicollinearity is a statistical phenomenon in which two or more predictor variables in a **multiple** **regression** model are highly **correlated** resulting to inter-associations among independent variables. This means that one can be linearly predicted from the rest that have non-tribal degree of accuracy. Multicollinearity is a problem because it makes the data unreliable.
Multicollinearity is measured using the variance inflation factor that assesses how much the variance of an estimated **regression** coefficient increases if...

1 Pages(250 words)Assignment

...Equation
Income = +0.63123236633544 Age +1.6310151733801 Educ +0.6060166606298 HRS +0.72543207253468
Variable
Parameter
S.E.
T-STAT
H0: parameter = 0
2-tail p-value
1-tail p-value
Age[t]
0.631232
0.186068
3.392487
0.000793
0.000397
Educ[t]
1.631015
0.202738
8.044927
0
0
HRS[t]
0.606017
0.178607
3.393018
0.000792
0.000396
Constant
0.725432
0.467417
1.552001
0.1218
0.0609
Variable
Partial **Correlation**
Age[t]
0.199383
Educ[t]
0.434562
HRS[t]
0.199413
Constant
0.092682
Critical Values (alpha = 5%)
1-tail CV at 5%
1.65
2-tail CV at 5%
1.96
**Multiple** Linear **Regression** - **Regression** Statistics
**Multiple** R
0.501424
R-squared
0.251426
Adjusted...

1 Pages(250 words)Case Study