* 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...

...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...

...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 and regression 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...

...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...

...and the number of construction permits issued at present.
Example 2: The demand 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...

...
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...

...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...

...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:...

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