Search for tag: "regression analysis"

2.6 Correlation is not causation | Basic Statistics | Correlation and Regression | UvA

This video explains why you have to be very careful interpreting the results of a regression analysis. Matthijs explains two key messages: that correlation is not the same as causation (e.g. with…

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3.7 Experimental designs | Quantitative methods | Research Designs | UvA

This video discusses four very common experimental designs: two-group design, two-group pre/post design, Solomon four-group design and within/repeated measures design.

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3.3 The regression model | Inferential Statistics | Simple regression | UvA

This video looks at the population regression equation. You see how it models the relation between predictor and response variable in the population.

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5.6 ANOVA and regression | Inferential Statistics | Analysis of variance | UvA

In this video we discuss the link between analysis of variance and multiple regression. In fact, multiple regression and analysis of variance are technically the same. To illustrate this we perform a…

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4.8 Interpreting results | Inferential Statistics | Multiple regression | UvA

In this video we will go through all the steps of performing a regression analysis, from checking assumptions to interpreting results. We will also compare the different regression models we…

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4.7 Categorical response variable | Inferential Statistics | Multiple regression | UvA

In this video you learn how you can use quantitative predictors and indicator variables to predict a binary response variable, using multiple logistic regression.

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4.6 Categorical predictors | Inferential Statistics | Multiple regression | UvA

In this video you learn how to include categorical predictors called indicators, and how to interpret them in multiple linear regression.

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4.5 Checking assumptions | Inferential Statistics | Multiple regression | UvA

In this video you learn how to check whether the assumptions of multiple linear regression hold. The assumptions discussed here are: linearity, normality, homoscedasticity, independence, sufficient…

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4.4 Individual T-tests | Inferential Statistics | Multiple regression | UvA

In this video you learn how to perform individual T-tests to see for each predictor separately whether it's significantly related to the response variable, while controlling for the other…

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4.3 Overall F-test | Inferential Statistics | Multiple regression | UvA

In this video you learn how to test whether the predictors together as a set are significantly related to the response variable. This test is referred to as the overall F-test of a multiple…

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4.2 R and R-squared | Inferential Statistics | Multiple regression | UvA

In this video you learn about the multiple correlation coefficient used to assess the strength of association between a response variable and a set of predictors. You also learn about the properties…

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4.1 Regression model | Inferential Statistics | Multiple regression | UvA

In this video you learn why multiple regression is useful and how we express a multiple regression model at the sample and population level. You also learn how to interpret the regression…

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3.9 Exponential regression | Inferential Statistics | Simple regression | UvA

In this video you learn how to recognize an exponential pattern in data and how to interpret a simple exponential regression model.

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3.8 CI and PI for predicted values | Inferential Statistics | Simple regression | UvA

In this video you learn how to interpret a confidence interval used to determine a range of plausible values for a predicted population mean. You also learn how to interpret a prediction interval…

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3.6 Testing the regression model | Inferential Statistics | Simple regression | UvA

In this video you learn how to perform a statistical test to see whether the predictor and the response variable are likely to be related in the population. You also learn how to calculate the…

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3.5 Pitfalls in regression | Inferential Statistics | Simple regression | UvA

In this video you learn about some potential pitfalls in regression. We will discuss nonlinearity, outliers, correlation and causation, inappropriate extrapolation, ecological fallacy and restriction…

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