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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).
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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.