Srbija Posted November 8, 2022 Share #1 Posted November 8, 2022 Predictive Modeling And Regression Analysis Using Spss Last updated 12/2018 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 6.51 GB | Duration: 12h 20m Master Logistic Regression, Linear, Multinomial and Multiple Regression Modeling, Correlation Techniques using SPSS What you'll learn The course works across multiple software packages such as SPSS, MS Office, PDF writers, and Paint. This course is to specifically learn about Descriptive Statistics, Means, Standard Deviation and T-test Understanding Means, Standard Deviation, Skewness, Kurtosis and T-test concepts Learn Importing Dataset and Correlation Techniques Learn Linear Regression Modeling Learn Multiple Regression Modeling Learn Logistic Regression Learn Multinomial Regression Requirements Prior knowledge of Quantitative Methods, MS Office and Paint is desired Description Predictive modeling course aims to provide and enhance predictive modeling skills across business sectors/domains. Quantitative methods and predictive modeling 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, PaintDesired skillsets - Understanding of Data Analysis and VBA toolpack in MS Excel will be useful Overview Section 1: Importing Dataset Lecture 1 Importing Datasets in Text and CSV Lecture 2 Importing Datasets xlsx, xls Formats Lecture 3 Importing Datasets xlsx, xls Formats Continue Lecture 4 Understanding User Operating Concepts Lecture 5 Software Menus Lecture 6 Understanding Mean Standard Deviation Lecture 7 Other Concepts of Understanding Mean SD Lecture 8 Implementation Using SPSS Lecture 9 Implementation using SPSS Continues Section 2: Correlation Techniques Lecture 10 Basic Correlation Theory Lecture 11 Interpretation Lecture 12 Implementation Lecture 13 Data Editor Lecture 14 Simple Scatter Plot Lecture 15 Heart Pulse Lecture 16 Statistics Viewer Lecture 17 Heart Pulse (Before and After RUN) Lecture 18 Interpretation and Implementation on Datasets Example 1 Lecture 19 Interpretation and Implementation on Datasets Example 2 Lecture 20 Interpretation and Implementation on Datasets Example 3 Lecture 21 Interpretation and Implementation on Datasets Example 4 Section 3: Linear Regression Modeling Lecture 22 Introduction to Linear Regression Modeling Using SPSS Lecture 23 Linear Regression Lecture 24 Stock Return Lecture 25 T-Value Lecture 26 Scatter Plot Rril v/s Rbse Lecture 27 Create Attributes for Variables Lecture 28 Scatter Plot - Rify v/s Rbse Lecture 29 Regression Equation Lecture 30 Interpretation Lecture 31 Copper Expansion Lecture 32 Copper Expansion Example Lecture 33 Copper Expansion Example Continue Lecture 34 Energy Consumption Lecture 35 Observations Lecture 36 Energy Consumption Example Lecture 37 Debt Assessment Lecture 38 Debt Assessment Continue Lecture 39 Debt to Income Ratio Lecture 40 Credit Card Debt Lecture 41 Basic Multiple regression Theory Lecture 42 Basic Multiple regression Theory Continue Section 4: Multiple Regression Modeling Lecture 43 Multiple Regression Example Part 1 Lecture 44 Multiple Regression Example Part 2 Lecture 45 Multiple Regression Example Part 3 Lecture 46 Multiple Regression Example Part 4 Lecture 47 Multiple Regression Example Part 5 Lecture 48 Multiple Regression Example Part 6 Lecture 49 Multiple Regression Example Part 7 Lecture 50 Multiple Regression Example Part 8 Lecture 51 Multiple Regression Example Part 9 Lecture 52 Multiple Regression Example Part 10 Lecture 53 Multiple Regression Example Part 11 Lecture 54 Multiple Regression Example Part 12 Lecture 55 Multiple Regression Example Part 13 Lecture 56 Multiple Regression Example Part 14 Section 5: Logistic Regression Lecture 57 Understanding Logistic Regression Concepts Lecture 58 Working on IBM SPSS Statistics Data Editor Lecture 59 SPSS Statistics Data Editor Continues Lecture 60 IBM SPSS Viewer Lecture 61 Variable in the Equation Lecture 62 Implementation Using MS Excel Lecture 63 Smoke Preferences Lecture 64 Heart Pulse Study Lecture 65 Heart Pulse Study Continues Lecture 66 Variables in the Equation Lecture 67 Smoking Gender Equation Lecture 68 Generating Output and Observations Lecture 69 Generating Output and Observations Continues Lecture 70 Interpretation of Output Example Section 6: Multinomial Regression Lecture 71 Introduction to Multinomial-Polynomial Regression Lecture 72 Example 1 Health Study of Marathoners Lecture 73 Note Lecture 74 Case Processing Summary Lecture 75 Model Fitting Information Lecture 76 Asymptotic Correlation Matrix Lecture 77 Understanding Dataset Lecture 78 Generating Output Lecture 79 Parameters Estimates Lecture 80 Asymptotic Correlations Metrics Lecture 81 Interpretation of Output Lecture 82 Interpretation of Output Continues Lecture 83 Interpretation of Estimates Lecture 84 Understand Interpretation Students,Quantitative and Predictive Modellers and Professionals,CFA's and Equity Research professionals,Pharma and research scientists Hidden Content Give reaction to this post to see the hidden content. 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