| 001 Introduction to Linear Regression Modeling Using SPSS.mp4 | 50.9 MB | ||
| 001 Introduction to Linear Regression Modeling Using SPSS_en.srt | 9.3 KB | ||
| 001 Linear Regression.mp4 | 60.1 MB | ||
| 001 Linear Regression_en.srt | 11 KB | ||
| 002 Stock Return.mp4 | 72.5 MB | ||
| 002 Stock Return_en.srt | 11.3 KB | ||
| 003 T-Value.mp4 | 74.1 MB | ||
| 003 T-Value_en.srt | 10.2 KB | ||
| 004 Scatter Plot Rril vs Rbse.mp4 | 99.7 MB | ||
| 004 Scatter Plot Rril vs Rbse_en.srt | 14.3 KB | ||
| 005 Create Attributes for Variables.mp4 | 85.7 MB | ||
| 005 Create Attributes for Variables_en.srt | 14.8 KB | ||
| 006 Scatter Plot-Rify vs Rbse.mp4 | 43.5 MB | ||
| 006 Scatter Plot-Rify vs Rbse_en.srt | 6.2 KB | ||
| 007 Regression Equation.mp4 | 69.7 MB | ||
| 007 Regression Equation_en.srt | 12.9 KB | ||
| 008 Interpretation.mp4 | 83.4 MB | ||
| 008 Interpretation_en.srt | 12.2 KB | ||
| 009 Copper Expansion.mp4 | 89.2 MB | ||
| 009 Copper Expansion_en.srt | 15.7 KB | ||
| 010 Copper Expansion Example.mp4 | 73.1 MB | ||
| 010 Copper Expansion Example_en.srt | 10.9 KB | ||
| 011 Copper Expansion Example Continue.mp4 | 81.9 MB | ||
| 011 Copper Expansion Example Continue_en.srt | 13.1 KB | ||
| 012 Energy Consumption.mp4 | 90.6 MB | ||
| 012 Energy Consumption_en.srt | 15.9 KB | ||
| 013 Observations.mp4 | 61.7 MB | ||
| 013 Observations_en.srt | 8.5 KB | ||
| 014 Energy Consumption Example.mp4 | 48.1 MB | ||
| 014 Energy Consumption Example_en.srt | 6.3 KB | ||
| 015 Debt Assessment.mp4 | 83.5 MB | ||
| 015 Debt Assessment_en.srt | 13.8 KB | ||
| 016 Debt Assessment Continue.mp4 | 78 MB | ||
| 016 Debt Assessment Continue_en.srt | 7.8 KB | ||
| 017 Debt to Income Ratio.mp4 | 105.3 MB | ||
| 017 Debt to Income Ratio_en.srt | 15.6 KB | ||
| 018 Credit Card Debt.mp4 | 93.9 MB | ||
| 018 Credit Card Debt_en.srt | 10.7 KB | ||
| 019 Predicted values Using MS Excel.mp4 | 53.7 MB | ||
| 019 Predicted values Using MS Excel_en.srt | 9.7 KB | ||
| 020 Predicted values Using MS Excel Continue.mp4 | 48.6 MB | ||
| 020 Predicted values Using MS Excel Continue_en.srt | 7.9 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 44 total files | |||
Linear Regression with SPSS
https://DevCourseWeb.com
Last updated 10/2023
Duration: 3h 9m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 1.51 GB
Genre: eLearning | Language: English
The core objective is to provide skills in understand the regression model and interpreting it for predictions
What you'll learn
The course works across multiple software packages such as SPSS, MS Office, PDF writers, and Paint.
The core objective of this course is to provide skills in understand the regression model and interpreting it for predictions
The associated parameters of the regression model will be interpreted and tested for significance and test the goodness of fit of the given regression model
Interpretation of regression attributes such as R-Squared (correlation coefficient), t and p values
Requirements
Prior knowledge of Quantitative Methods, MS Office and Paint is desired.
Description
Predictive modelling course aims to provide and enhance predictive modelling skills across business sectors/domains. Quantitative methods and predictive modelling concepts could be extensively used in understanding the current customer behavior, financial markets movements, and studying tests and effects in medicine and in pharma sectors after drugs are administered. The course picks theoretical and practical datasets for predictive analysis. Implementations are done using SPSS software. Observations, interpretations, predictions and conclusions are explained then and there on the examples as we proceed through the training. The course also emphasizes on the higher order regression models such as quadratic and polynomial regressions which aren’t covered in other online courses
Essential skillsets – Prior knowledge of Quantitative methods and MS Office, Paint
Desired skillsets -- Understanding of Data Analysis and VBA toolpack in MS Excel will be useful
The course works across multiple software packages such as SPSS, MS Office, PDF writers, and Paint.
Regression modelling forms the core of Predictive modelling course. The core objective of this course is to provide skills in understand the regression model and interpreting it for predictions. The associated parameters of the regression model will be interpreted and tested for significance and test the goodness of fit of the given regression model.
Through this course we are going to understand
• Interpretation of regression attributes such as R-Squared (correlation coefficient), t and p values
• m (slope) and c (intercept),
• dependent (Y) and independent (X) variables
• Examining the significance of independent (X) variable to check the fitness of regression model
• Predicting Y-variable based on varying values of X-variable
• Implementation on sample datasets using SPSS and output simulation in MS Excel
Who this course is for
Students and Quantitative and Predictive Modellers and Professionals
Homepage
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