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Fixed effects are those that are of interest to the researcher, while random effects are those that are not of interest, but may still influence the data.",{"type":1369,"value":1394},"As an extension to simple and multiple linear regression introduced in MAS-I, linear mixed models allow us to model data that do not fit the assumptions.",{"type":1369,"value":1396},"On top of the models themselves, we will be covering model inference and diagnostics.",{"type":1376,"value":1398},"Candidates should understand the structure of linear mixed models, including how to accommodate models with correlated observations or models where the variance is either not assumed to be constant or a function of the mean.","2",{"id":1401,"name":1402,"ordinal":970,"minWeight":1403,"maxWeight":1403,"contentStats":1404,"meta":1411,"shortname":1420},5283,"Statistical Learning",0.45,{"wordCount":1405,"assignmentQuestionCount":1406,"videoDurationInMinutes":1407,"practiceQuestionCount":1408,"characterCount":1409,"learnManualCount":1410},57396,52,460,433,378752,54,[1412,1414,1416,1418],{"type":1369,"value":1413},"With big data and data analytics on the rise, it is crucial for actuaries to be well-equipped with statistical tools. In this section, we will learn modeling to help us identify patterns in datasets.",{"type":1369,"value":1415},"Remember the statistical learning methods we learned in MAS-I? In MAS-II, we will continue our discussion with a variety of non-parametric supervised learning and unsupervised learning approaches.",{"type":1369,"value":1417},"We will also cover several other performance measures to evaluate model quality.",{"type":1376,"value":1419},"Candidates should understand the mechanics of the algorithms identified in the tasks below and recognize their inherent strengths and weaknesses to be able to select the most appropriate procedure for the learning task at hand.","3",{"id":1422,"name":1423,"ordinal":1424,"minWeight":1359,"maxWeight":1359,"contentStats":1425,"meta":1430,"shortname":1435},5292,"Time Series with Constant Variance",4,{"wordCount":1426,"assignmentQuestionCount":1366,"videoDurationInMinutes":1427,"practiceQuestionCount":1428,"characterCount":1429,"learnManualCount":1389},24894,196,226,191599,[1431,1433],{"type":1369,"value":1432},"In this section, we will explore time series models and other important concepts related to time series, such as stationarity and autoregression. We will also learn about different time series models and forecast future observations.",{"type":1376,"value":1434},"Candidates should understand the basic applications of the Auto Regressive Integrated Moving Average (ARIMA) time series model.","4"]