* MODEL SUMMARY
Model Summary|
Model| R| R Square| Adjusted R Square| Std. Error of the Estimate| 1| .549a| .301| .292| .59246|
a. Predictors: (Constant), MEAN_OC|
The first table of interest is the Model Summary table. This table provides the R and R2 value. * The R value is 0.549, which represents the simple correlation. * It indicates a average degree of correlation. The R2 value indicates how much of the dependent variable, "Job Satisfaction", can be explained by the independent variable, "Organizational Commitment" or how they depend on each other. * In this case, 30.1% can be explained, which is very small or they both are little bit depends on eachother.|

* ANOVA

ANOVAa|
Model| Sum of Squares| df| Mean Square| F| Sig.|
1| Regression| 11.784| 1| 11.784| 33.572| .000b|
| Residual| 27.378| 78| .351| | |
| Total| 39.162| 79| | | |
a. Dependent Variable: MEAN_JS|
b. Predictors: (Constant), MEAN_OC|

ANOVA TABLE
* This table indicates that the regression model predicts the outcome variable significantly well. * Here, p(sig.) < 0.0005, which is less than 0.05, and indicates that, overall, the model applied can statistically significantly predict the outcome variable. * Or we can say that the “organizational commitment” significantly predict the “Job Satisfaction”.

Coefficients, provides us with information on each predictor variable. * This gives us the information we need to predict “Job Satisfaction” from “Organizational commitment”. * We can see that both the constant and “Organizational commitment” contribute significantly to the model (by...

...Simple Linear Regression in SPSS
1.
STAT 314
Ten Corvettes between 1 and 6 years old were randomly selected from last year’s sales records in Virginia Beach, Virginia. The following data were obtained, where x denotes age, in years, and y denotes sales price, in hundreds of dollars. x y a. b. c. d. e. f. g. h. i. j. k. l. m. 6 125 6 115 6 130 4 160 2 219 5 150 4 190 5 163 1 260 2 260
Graph the data in a scatterplot to determine if there is a possible linear relationship. Compute and interpret the linear correlation coefficient, r. Determine the regression equation for the data. Graph the regression equation and the data points. Identify outliers and potential influential observations. Compute and interpret the coefficient of determination, r2. Obtain the residuals and create a residual plot. Decide whether it is reasonable to consider that the assumptions for regression analysis are met by the variables in questions. At the 5% significance level, do the data provide sufficient evidence to conclude that the slope of the population regression line is not 0 and, hence, that age is useful as a predictor of sales price for Corvettes? Obtain and interpret a 95% confidence interval for the slope, β, of the population regression line that relates age to sales price for Corvettes. Obtain a point estimate for the mean sales price of all 4-year-old Corvettes. Determine a 95%...

...Regression Analysis: A Complete Example
This section works out an example that includes all the topics we have discussed so far in this chapter.
A complete example of regression analysis.
PhotoDisc, Inc./Getty Images
A random sample of eight drivers insured with a company and having similar auto insurance policies was selected. The following table lists their driving experiences (in years) and monthly auto insurance premiums.
Driving Experience (years) Monthly Auto Insurance Premium
5 2 12 9 15 6 25 16
$64 87 50 71 44 56 42 60
a. Does the insurance premium depend on the driving experience or does the driving experience depend on the insurance premium? Do you expect a positive or a negative relationship between these two variables? b. Compute SSxx, SSyy, and SSxy. c. Find the least squares regression line by choosing appropriate dependent and independent variables based on your answer in part a. d. Interpret the meaning of the values of a and b calculated in part c. e. Plot the scatter diagram and the regression line. f. Calculate r and r2 and explain what they mean. g. Predict the monthly auto insurance premium for a driver with 10 years of driving experience. h. Compute the standard deviation of errors. i. Construct a 90% confidence interval for B. j. Test at the 5% significance level whether B is negative. k. Using α = .05, test whether ρ is different from zero.
Solution a. Based on theory and intuition, we...

...Executive Summary
The purpose of the research being undertaken is to fill a void in the literature surrounding organisations’ attitudes towards risk. The report will focus on the recent failure of the large Swiss Bank UBS; whereby an analysis of data of attitudes towards risk before and after the scandal, will give an indication on the effects the UBS bank scandal has had on financial organisations’ attitudes towards risk. In addition, through the use of correlation coefficient andregression analysis whether or not there is a correlation between the risk attitude of companies and their volatilitywill be assessed, and if so to measure this effect.
Both primary and secondary research has been used in gathering the data. The primary research was conducted by distributing a questionnaire to the CEOs; questioning how they consider their attitudes was towards risk. The question ranged from 1 to 30; where 1 is being the most conservative and 30 being the most risky. This primary research displays the financial organisations’ attitudes towards risk after the UBS scandal. For the Primary research to be conducted a sample was collected consisting of 100 CEOs from various financial organisations’. These CEOs were taken from a list of the 100 largest companies in the City of London. As the CEOs were only chosen from the city of London the data collection only represents this area; therefore cannot be used as a benchmark for other financial organisations in other...

...Applied Linear Regression Notes set 1
Jamie DeCoster
Department of Psychology
University of Alabama
348 Gordon Palmer Hall
Box 870348
Tuscaloosa, AL 35487-0348
Phone: (205) 348-4431
Fax: (205) 348-8648
September 26, 2006
Textbook references refer to Cohen, Cohen, West, & Aiken’s (2003) Applied Multiple Regression/Correlation
Analysis for the Behavioral Sciences. I would like to thank Angie Maitner and Anne-Marie Leistico for
comments made on earlier versions of these notes. If you wish to cite the contents of this document, the
APA reference for them would be:
DeCoster, J. (2006). Applied Linear Regression Notes set 1. Retrieved (month, day, and year you
downloaded this ﬁle, without the parentheses) from http://www.stat-help.com/notes.html
For future versions of these notes or help with data analysis visit
http://www.stat-help.com
ALL RIGHTS TO THIS DOCUMENT ARE RESERVED
Contents
1 Introduction and Review
1
2 Bivariate Correlation and Regression
9
3 Multiple Correlation and Regression
21
4 Regression Assumptions and Basic Diagnostics
29
5 Sequential Regression, Stepwise Regression, and Analysis of IV Sets
37
6 Dealing with Nonlinear Relationships
45
7 Interactions Among Continuous IVs
51
8 Regression with Categorical IVs
59
9 Interactions involving Categorical IVs
69...

...for new house or automobile is very much affected by the interest rates changed by banks.
Regression analysis is one such causal method. It is not limited to locating the straight line of best fit.
Types:-
1. Simple (or Bivariate) Regression Analysis:
Deals with a Single independent variable that determines the value of a dependent variable.
Ft+1 = f (x) t Where Ft+1: the forecast for the next period.
This indicates the future demand is a function of the value of the economic indicator at the
present time.
Demand Function: D=a+bP, where b is negative.
If we assume there is a linear relation between D and P, there may also be some random variation in this relation.
Sum of Squared Errors (SSE): This is a measure of the predictive accuracy. Smaller the value of SSE, the more accurate is there regression equation
EXAMPLE:-
Following data on the demand for sewing machines manufactured by Taylor and Son
Co. have been compiled for the past 10 years.
YEAR | 1971 | 1972 | 1973 | 1974 | 1975 | 1976 | 1977 | 1978 | 1979 | 1980 |
DEMAND (in 1000 Units) | 58 | 65 | 73 | 76 | 78 | 87 | 88 | 93 | 99 | 106 |
1. Single variable linear regression
Year = x where x = 1, 2, 3... 10
Demand = y
D = y + ᵋ Where D is actual demand
ᵋ = D –y
To find out whether this is the line of best fitted or not it is to be made sure that this sum of squares is minimum.
2. Nonlinear Regression Analysis...

...Determinants of Production and Consumptions
Determinants of Industry Production (Supply)
Supply is the amount of output of production that producers are willing and able to sell at a given price all other factors being held constant.
The following are the determinants of supply:
Price (P), Numbers of Producers (NP), Taxes (T)
Model Specification
Specification of model is to specify the form of equation, or regression relation that indicates the relationship between the independent variables and the dependent variables. Normally the specific functional form of the regression relation to be estimated is chosen to depict the true supply relationships as closely possible.
The table presented below gives the hypothetical quantity supplied for a particular product (Qs) of a particular place given its price per kilo (P/kl), the Numbers of producers (NP), and tax per kilo (T/kl) for the period 2002 to 2011. (The quantity Supplied is expressed as kilo in millions)
Table
|Year |Qs |P/kl |NP |T/kl |
|2002 |21.4 |23 |39 |1.15 |
|2003 |23.9 |25 |41 |1.25 |
|2004...

...
Logistic regression
In statistics, logistic regression, or logit regression, is a type of probabilistic statistical classification model.[1] It is also used to predict a binary response from a binary predictor, used for predicting the outcome of acategorical dependent variable (i.e., a class label) based on one or more predictor variables (features). That is, it is used in estimating the parameters of a qualitative response model. The probabilities describing the possible outcomes of a single trial are modeled, as a function of the explanatory (predictor) variables, using a logistic function. Frequently (and subsequently in this article) "logistic regression" is used to refer specifically to the problem in which the dependent variable is binary—that is, the number of available categories is two—while problems with more than two categories are referred to as multinomial logistic regression or, if the multiple categories are ordered, as ordered logistic regression.
Logistic regression measures the relationship between a categorical dependent variable and one or more independent variables, which are usually (but not necessarily) continuous, by using probability scores as the predicted values of the dependent variable.[2] As such it treats the same set of problems as doesprobit regression using similar techniques.
Fields and examples of applications[edit]...

...Using SPSS for Data Analysis: Support Document for SPSS Output Tables
1
OFFICE OF PLANNING, ASSESSMENT, RESEARCH AND QUALITY
Using SPSS for Data Analysis: Support Document for SPSS Output Tables
Prepared by: UW-Stout Office of Planning, Assessment, Research & Quality (PARQ)
Tynan Heller Susan Greene Revised on 8/29/12
Prepared for: UW-Stout Campus
Report distributed to: UW-Stout Campus
DOCUMENT NO: BPA-900 APPROVAL: Susan Greene The user is responsible for ensuring this is the current revision. Thank you!
EFFECTIVE: 8/29/2012 SUPERSEDES: Ver 4
2
3 This document provides an explanation on how to read SPSS output tables for a range of analyses (Created using SPSS version 17.0)
To view a topic, please click the appropriate heading in the Table of Contents
Table of Contents
Frequency Analysis ......................................................................................................................... 5 Definitions associated with a frequency analysis ....................................................................... 5 Section 1: Frequencies without missing responses ..................................................................... 7 Section 2: Frequencies without descriptive & with missing responses ...................................... 8 Section 3: Frequencies with descriptive statistics...

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