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 Cook’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.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 ‘gender = girl’ and ‘year = 2000’ and create a new dataset named by GIRL_2000.3.6.3 Perform frequency analysis for ‘learning2, learning4, confidence2_re, confidence3_re, confidence6_re’.3.6.2 Recode ‘confidence2, confidence3, confidence6’ variables only for students who are born in 1999 (‘year’ variable) and save the recoded variables into ‘confidence2_re, confidence3_re, confidence6_re’ variables.3.6.1 Recode into same variables for ‘learning2, learning4’.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,‘StudentsBullied’ using the sum of 6 variables (studentbullied1-studentbullied6).3.4.2 Create new variable,‘ParentSupport’ using the mean of 4 variables (parentsupport1-parentsupport4).3.4.1 Create new variable,‘ScienceTotal’,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.