4.2 Point Biserial Correlation & Phi Correlation.4.1.3 Calculating the Pearson/Spearman Correlation in R.3.7.3 Select 10% of total student at random and delete unselected cases.3.7.2 Select students who have id=3001 to id=4000 and filter out unselected cases.3.7.1 Select вЂgender = girlвЂ™ and вЂyear = 2000вЂ™ and create a new dataset named by GIRL_2000.3.6.3 Perform frequency analysis for вЂlearning2, learning4, confidence2_re, confidence3_re, confidence6_reвЂ™.3.6.2 Recode вЂconfidence2, confidence3, confidence6вЂ™ variables only for students who are born in 1999 (вЂyearвЂ™ variable) and save the recoded variables into вЂconfidence2_re, confidence3_re, confidence6_reвЂ™ variables.3.6.1 Recode into same variables for вЂlearning2, learning4вЂ™.3.5.1 Load the car package for reverse coding.3.4.4 Perform descriptive analysis (mean,median,mode,and S.D.) on ScienceScore, ParentSupport,and StudentsBullied.3.4.3 Create new variable,вЂStudentsBulliedвЂ™ using the sum of 6 variables (studentbullied1-studentbullied6).3.4.2 Create new variable,вЂParentSupportвЂ™ using the mean of 4 variables (parentsupport1-parentsupport4).3.4.1 Create new variable,вЂScienceTotalвЂ™,using the average of (score1-score5).

3.4 Class Activity 1: Calculate the aggregated data.3.1 Read the data from an excel/SPSS file.13.2.3 Run the Probit logistic Regression model using stats package.13.2 R-Lab: Running Probit Analysis in R.12.10 Measuring Strength of Association (Calculating the Pseudo R-Square).12.9 Compute a confusion table and misclassification error (R exclusive).12.6 Run the ordinal logistic Regression model using MASS package.12.1 Introduction to Ordinal Logistic Regression.11.7.9 Interpretation of the Predictive Equation.11.7.3 Run the Multinomial Model using вЂњnnetвЂќ package.11.7.1 Understanding the Data: Choice of Programs.11.7 R Labs: Running Multinomial Logistic Regression in R.11.6 Features of Multinomial logistic regression.11.5 Checking AssumptionL: Multicollinearity.11.1 Introduction to Multinomial Logistic Regression.10.7.3 Running a logstic regression model.10.7.1 Data Explanations ((Data set: class.sav)).10.7 R Lab: Running Binary Logistic Regression Model.10.6 Likelihood Ratio Test for Nested Models.10.2 The Purpose of Binary Logistic Regression.9.2.1 Organize Longitudinal Data: Long Format vs.В Wide Format.8.7 Question 4 - How do public and Catholic schools compare in terms of mean math achievement and in terms of the strength of the SES-math achievement relationship, after we control for MEAN SES?.8.6 Question 3 - Is the strength of association between student CSES and math achievement similar across schools? Or is CSES a better predictor of student math achievement in some schools than others?.8.5 Question 2 - Do schools with high MEAN SES also have high math achievement?.high schools vary in their mean math achievement? 7.2.6 Adding an interaction term to the model.7.2.5 Random intercepts and slopes model.7.2.3 Setting up an Unconditional Model.7.2.2 Setting up the simple linear model.7.2 R Lab: Running Multilevel models in R.6.3 Run the Curvilinear Regression Model.6.1 Introduction to Curvilinear Regression.5.5.4 Check the outliers by using CookвЂ™s Distance.5.5.3 Check the outliers by using Mahalanobis Distance.5.5.1 Check the correlation matrix & the P-value matrix.5.4.1 Run the Multiple Regression model.5.2.5 Centering Variable for better interpretation.5.1 Introduction to Multiple Regression.4.3 Partial and Semi-partial Correlation.

Curvilinear regression excel