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IBM SPSS Statistics 26.0 IF006 Win/Linux/Macosx 專業的數據統計分析軟件

專業的數據統計分析軟件，它的主要功能就是數據錄入、資料編輯、數據管理、統計分析、報表製作、圖形繪製等等，並且軟件本身以及功能方面也還在不斷完善、更新。同時使用操作也是非常簡單，非常容易上手

IBM SPSS Statistics 26.0 IF006 |

Languages: English, Português, Français, Deutsch, Italiano, 日本語, 한국어, Polski,

Русский, Español, Simplified 中文, Thaditional 中文.

In April of this year, IBM released the latest version of SPSS Statistics. Version 26 introduces a number of additional analysis procedures as well as new command enhancements.

What’s new in IBM SPSS Statistics v26

This page presents a brief overview of key new features in SPSS v26. These features bring much desired new statistical tests, enhancements to existing statistics and scripting procedures, and new production facility capabilities to the Classic user interface, which all originated from customer feedback. Here is what you can do with the new features in version 26:

New analytical procedures

Quantile Regression

In standard ‘least squares’ regression the model predictions are based on a single regression line. This line can be used to estimate the mean value of the dependent variable as represented by the points clustering about line at a given value of the independent (predictor) variable (see figure 1)

You may note from the chart that there seems to be a slight ‘funnelling’ of the points near the higher values in the scatterplot. Technically, this is referred to as ‘heteroscedasticity’, but more prosaically, it just indicates that the model is likely to be worse at estimating higher values than lower ones since the points vary more about the line. Quantile regression offers us the opportunity to fit the model using a median value rather than a mean. We should bear in mind that a median is also called the 50th percentile and in this context percentile and quantile refer to the same thing. Although there’s no reason to believe that a regression based on line fitted about the median would be more accurate than one based on a mean, quantile regression is flexible enough to allow us to fit a model based on other percentile values. In other words, we can fit separate regression lines for different percentiles. For example, we can request estimates for the lowest 10 percent (quantile = 0.1) or the top 90 percent (quantile = 0.9) of the dependent variable.

The effect of this is that we can produce separate predictions for the different parts of the dependent variable’s distribution. Using standard Linear Regression on the same dataset we get a single formula for estimating a respondent’s current salary. This formula consists of a single coefficient of 1.9, meaning that for every extra dollar of beginning salary, the respondent earns $1.9 dollars in their current salary. The formula also contains a constant value (or intercept) of $1,928. However, there’s no reason to assume that the same formula applies to the data in the top 10% of the current salary distribution, or the say, the bottom 25%. As such, Quantile regression produces separate coefficients and intercept values for each requested quantile. The new Quantile regression procedure even plots these values as shown in figure 3.

The charts even show the parameter values for a standard (OLS) linear regression model for comparison (as indicated by the red line). Figure 3 illustrates that not only do we get different intercept values for data in the 20th percentile (quantile 0.2) vs the 80th percentile (quantile 0.8), but we also get different parameter estimates for the coefficient values.

ROC Analysis

The new ROC procedure makes it easier to assess the accuracy and performance of predictive classification models. ROC (Receiver Operator Characteristic) analysis is specifically concerned with the classification accuracy of models, especially as regards the relationships between the accurate classifications (known as the True Positives and True Negatives) and the inaccurate predictions (the False Positives and False Negatives). These are often represented by a ROC curve that plots the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The new ROC Analysis procedure also includes precision-recall (PR) curves and provides options for comparing two ROC curves that are generated from either independent groups or paired subjects.

Bayesian Statistics

SPSS Statistics v26 also includes enhancements to its suite of bayesian statistical procedures.

One-way Repeated Measures ANOVA

The repeated measures enhancement allows the analyst to adopt a Bayesian approach to comparing any changes in a given factor for the same subject at different time points or conditions. It is assumed that each subject has a single observation for each time point.

One Sample Binomial enhancements

Here the user may apply a Bayesian binomial test to attempt to determine the likelihood that the observed ratio between two groups are the same as an assumed proportion in the population.

One Sample Poisson enhancements

Like the previous procedure, except here the user may compare their data to how well it fits a Poisson distribution. These distributions are a useful modelling for rare events such as accidents or insurance claims. A conjugate prior within the Gamma distribution family is used when drawing Bayesian statistical inference on Poisson distribution.

Reliability Analysis

Some additional enhancements have been made to SPSS Statistics’ reliability procedures.Reliability analysis has now been updated to provide options for Fleiss’ Multiple Rater Kappa statistics. This technique is often employed when assessing the reliability of agreement between a fixed number of raters when assigning categorical ratings to a number of items or classifying items. This contrasts with other kappa values (such as Cohen’s kappa) which only apply to assessments of agreement between a maximum of two raters.

The IBM SPSS software platform offers advanced statistical analysis, a vast library of machine-learning algorithms, text analysis, open-source extensibility, integration with big data and seamless deployment into applications. Its ease of use, flexibility and scalability make SPSS accessible to users with all skill levels and outfits projects of all sizes and complexity to help you and your organization find new opportunities, improve efficiency and minimize risk.

Version 26 introduces a number of additional analysis procedures as well as new command enhancements.

IBM (International Business Machines) ranks among the world's largest information technology companies, providing a wide spectrum of hardware, software and services offerings.

Product: IBM SPSS Statistics

Version: 26.0 IF006 *

Supported Architectures: 32bit / 64bit

Website Home Page : http://www.ibm.com

Language: multilanguage

System Requirements: **

Supported Operating Systems: **

IBM SPSS Statistics 26.0 IF006 |

Languages: English, Português, Français, Deutsch, Italiano, 日本語, 한국어, Polski,

Русский, Español, Simplified 中文, Thaditional 中文.

In April of this year, IBM released the latest version of SPSS Statistics. Version 26 introduces a number of additional analysis procedures as well as new command enhancements.

What’s new in IBM SPSS Statistics v26

This page presents a brief overview of key new features in SPSS v26. These features bring much desired new statistical tests, enhancements to existing statistics and scripting procedures, and new production facility capabilities to the Classic user interface, which all originated from customer feedback. Here is what you can do with the new features in version 26:

New analytical procedures

Quantile Regression

In standard ‘least squares’ regression the model predictions are based on a single regression line. This line can be used to estimate the mean value of the dependent variable as represented by the points clustering about line at a given value of the independent (predictor) variable (see figure 1)

You may note from the chart that there seems to be a slight ‘funnelling’ of the points near the higher values in the scatterplot. Technically, this is referred to as ‘heteroscedasticity’, but more prosaically, it just indicates that the model is likely to be worse at estimating higher values than lower ones since the points vary more about the line. Quantile regression offers us the opportunity to fit the model using a median value rather than a mean. We should bear in mind that a median is also called the 50th percentile and in this context percentile and quantile refer to the same thing. Although there’s no reason to believe that a regression based on line fitted about the median would be more accurate than one based on a mean, quantile regression is flexible enough to allow us to fit a model based on other percentile values. In other words, we can fit separate regression lines for different percentiles. For example, we can request estimates for the lowest 10 percent (quantile = 0.1) or the top 90 percent (quantile = 0.9) of the dependent variable.

The effect of this is that we can produce separate predictions for the different parts of the dependent variable’s distribution. Using standard Linear Regression on the same dataset we get a single formula for estimating a respondent’s current salary. This formula consists of a single coefficient of 1.9, meaning that for every extra dollar of beginning salary, the respondent earns $1.9 dollars in their current salary. The formula also contains a constant value (or intercept) of $1,928. However, there’s no reason to assume that the same formula applies to the data in the top 10% of the current salary distribution, or the say, the bottom 25%. As such, Quantile regression produces separate coefficients and intercept values for each requested quantile. The new Quantile regression procedure even plots these values as shown in figure 3.

The charts even show the parameter values for a standard (OLS) linear regression model for comparison (as indicated by the red line). Figure 3 illustrates that not only do we get different intercept values for data in the 20th percentile (quantile 0.2) vs the 80th percentile (quantile 0.8), but we also get different parameter estimates for the coefficient values.

ROC Analysis

The new ROC procedure makes it easier to assess the accuracy and performance of predictive classification models. ROC (Receiver Operator Characteristic) analysis is specifically concerned with the classification accuracy of models, especially as regards the relationships between the accurate classifications (known as the True Positives and True Negatives) and the inaccurate predictions (the False Positives and False Negatives). These are often represented by a ROC curve that plots the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The new ROC Analysis procedure also includes precision-recall (PR) curves and provides options for comparing two ROC curves that are generated from either independent groups or paired subjects.

Bayesian Statistics

SPSS Statistics v26 also includes enhancements to its suite of bayesian statistical procedures.

One-way Repeated Measures ANOVA

The repeated measures enhancement allows the analyst to adopt a Bayesian approach to comparing any changes in a given factor for the same subject at different time points or conditions. It is assumed that each subject has a single observation for each time point.

One Sample Binomial enhancements

Here the user may apply a Bayesian binomial test to attempt to determine the likelihood that the observed ratio between two groups are the same as an assumed proportion in the population.

One Sample Poisson enhancements

Like the previous procedure, except here the user may compare their data to how well it fits a Poisson distribution. These distributions are a useful modelling for rare events such as accidents or insurance claims. A conjugate prior within the Gamma distribution family is used when drawing Bayesian statistical inference on Poisson distribution.

Reliability Analysis

Some additional enhancements have been made to SPSS Statistics’ reliability procedures.Reliability analysis has now been updated to provide options for Fleiss’ Multiple Rater Kappa statistics. This technique is often employed when assessing the reliability of agreement between a fixed number of raters when assigning categorical ratings to a number of items or classifying items. This contrasts with other kappa values (such as Cohen’s kappa) which only apply to assessments of agreement between a maximum of two raters.

The IBM SPSS software platform offers advanced statistical analysis, a vast library of machine-learning algorithms, text analysis, open-source extensibility, integration with big data and seamless deployment into applications. Its ease of use, flexibility and scalability make SPSS accessible to users with all skill levels and outfits projects of all sizes and complexity to help you and your organization find new opportunities, improve efficiency and minimize risk.

Version 26 introduces a number of additional analysis procedures as well as new command enhancements.

IBM (International Business Machines) ranks among the world's largest information technology companies, providing a wide spectrum of hardware, software and services offerings.

Product: IBM SPSS Statistics

Version: 26.0 IF006 *

Supported Architectures: 32bit / 64bit

Website Home Page : http://www.ibm.com

Language: multilanguage

System Requirements: **

Supported Operating Systems: **

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