Instructional Videos
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YouTube Channel
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1.1: Identifying and summarizing data
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1.2: Population distributions
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1.3: Selecting individuals at random – probability
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1.4a: Random sampling (part 1)
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1.4b: Random sampling (part 2)
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1.5: Interval estimation
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1.6a: Hypothesis testing (part 1)
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1.6b: Hypothesis testing (part 2)
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1.7: Prediction intervals
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2.1: Simple linear regression (SLR) model
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2.2: SLR least squares regression line
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2.3a: SLR regression standard error
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2.3b: SLR coefficient of determination, R-squared
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2.3c: Estimating and testing the SLR slope parameter
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2.4: SLR model assumptions
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2.5: SLR interpretation
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2.6: SLR confidence and prediction intervals
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2.7: Complete SLR analysis
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3.1: Multiple linear regression (MLR) model
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3.2: MLR least squares regression equation
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3.3a: MLR regression standard error
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3.3b: MLR coefficient of determination, R-squared
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3.3c: MLR global usefulness test (overall F-test)
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3.3d: MLR nested model test (subset or partial F-test)
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3.3e: MLR individual parameter t-tests
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3.4: MLR model assumptions
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3.5: MLR interpretation
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3.6: MLR confidence and prediction intervals
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4.1a: MLR predictor transformations
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4.1b: MLR response transformations
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4.2: MLR interactions
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4.3a: MLR categorical predictors (part 1)
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4.3b: MLR categorical predictors (part 2)
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4.3c: MLR categorical predictors (part 3)
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5.1a: MLR outliers
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5.1b: MLR leverage
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5.1c: MLR Cook’s distance
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5.2a: MLR pitfalls – nonconstant variance
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5.2b: MLR pitfalls – autocorrelation
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5.2c: MLR pitfalls – multicollinearity
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5.2d: MLR pitfalls – Simpson’s paradox
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5.2e: MLR pitfalls – overfitting, extrapolation, missing data
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5.3: MLR model building guidelines
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5.4: MLR predictor effect plots