Канал: Amelia McNamara

Extensions to logistic regression

Extensions to logistic regression

Installing tinytex

Installing tinytex

Regression summary lab (STAT 320, lab04_regression-summary)

Regression summary lab (STAT 320, lab04_regression-summary)

Chi-square pre lab (video 2 of 2)

Chi-square pre lab (video 2 of 2)

Inference for two samples (video 2 of 2)

Inference for two samples (video 2 of 2)

Inference for two samples (video 1 of 2)

Inference for two samples (video 1 of 2)

Installing Python, conda, reticulate, and Quarto

Installing Python, conda, reticulate, and Quarto

Inference for a single sample (video 2 of 2)

Inference for a single sample (video 2 of 2)

Inference for a single sample (video 1 of 2)

Inference for a single sample (video 1 of 2)

Descriptive linear regression pre-lab (video 1 of 1)

Descriptive linear regression pre-lab (video 1 of 1)

Scatterplots pre-lab (video 1 of 1)

Scatterplots pre-lab (video 1 of 1)

Categorical variables pre-lab (video 2 of 3)

Categorical variables pre-lab (video 2 of 3)

Exporting images from labs

Exporting images from labs

Categorical variables pre-lab (video 1 of 3)

Categorical variables pre-lab (video 1 of 3)

Sampling pre-lab (video 1 of 1)

Sampling pre-lab (video 1 of 1)

Describing data pre-lab (video 1 of 1)

Describing data pre-lab (video 1 of 1)

Integrating Computation in Statistics: Instructional Decisions for Teaching R

Integrating Computation in Statistics: Instructional Decisions for Teaching R

Checking conditions on real models (2 of 2)

Checking conditions on real models (2 of 2)

Assessing conditions, again (1 of 2)

Assessing conditions, again (1 of 2)

More multiple regression (2 of 2)

More multiple regression (2 of 2)

Introduction to multiple regression (1 of 2)

Introduction to multiple regression (1 of 2)

The National Atlas of the United States of America

The National Atlas of the United States of America

Visualizing time and timeseries data

Visualizing time and timeseries data

More considerations for visualizing spatial data

More considerations for visualizing spatial data

Visualizing spatial data: points, lines, and polygons

Visualizing spatial data: points, lines, and polygons

Visualizing more variables

Visualizing more variables

Multiple testing

Multiple testing

Type I and Type II errors

Type I and Type II errors

Determining statistical significance

Determining statistical significance

Introduction to randomization distributions

Introduction to randomization distributions

Measuring evidence with p-values

Measuring evidence with p-values

Introduction to hypothesis tests

Introduction to hypothesis tests

Inference for paired data (2 of 2)

Inference for paired data (2 of 2)

Paired difference in means (1 of 2)

Paired difference in means (1 of 2)

Bootstrap approach for a confidence interval of a difference in means (6.4CI video 2 of 2)

Bootstrap approach for a confidence interval of a difference in means (6.4CI video 2 of 2)

Confidence interval for a difference in means

Confidence interval for a difference in means

Approximating the sampling distribution for a difference in means

Approximating the sampling distribution for a difference in means

Confidence interval for a difference in proportions (1 of 2)

Confidence interval for a difference in proportions (1 of 2)

Distribution for difference of two proportions

Distribution for difference of two proportions

Confidence intervals for a difference in proportions (2 of 2)

Confidence intervals for a difference in proportions (2 of 2)

More graphical perception

More graphical perception

Graphical perception

Graphical perception

Introduction to perception

Introduction to perception

Gestalt concepts

Gestalt concepts

Approximating the sampling distribution of a single mean (6.2D video 1 of 1)

Approximating the sampling distribution of a single mean (6.2D video 1 of 1)

Introduction to approximating with distributions

Introduction to approximating with distributions

Approximating the distribution of a single proportion

Approximating the distribution of a single proportion

Color is cultural

Color is cultural

Colorblindness, contextual colors

Colorblindness, contextual colors

Color palettes

Color palettes

Color models

Color models

More bootstrap in StatKey (3.3 part 4 of 4)

More bootstrap in StatKey (3.3 part 4 of 4)

Bootstrap terminology (3.3 part 3 of 4)

Bootstrap terminology (3.3 part 3 of 4)

Visualizing the bootstrap (3.3 part 2 of 4)

Visualizing the bootstrap (3.3 part 2 of 4)

Introduction to the bootstrap (3.3 part 1 of 4)

Introduction to the bootstrap (3.3 part 1 of 4)

Confidence intervals and sampling distributions

Confidence intervals and sampling distributions

More confidence intervals (3.2 part 2 of 3)

More confidence intervals (3.2 part 2 of 3)

Intro to confidence intervals, margin of error

Intro to confidence intervals, margin of error

More on sampling distributions

More on sampling distributions

Intro to sampling distributions

Intro to sampling distributions

Handmade viz

Handmade viz

Data art, self tracking

Data art, self tracking

Even more viz! (2.7 video 4 of 4)

Even more viz! (2.7 video 4 of 4)

Interactive visualization (2.7 video 3 of 4)

Interactive visualization (2.7 video 3 of 4)

Simpson's Paradox (2.7 video 1 of 4)

Simpson's Paradox (2.7 video 1 of 4)

Visualizing space and time (2.7 part 2 of 4)

Visualizing space and time (2.7 part 2 of 4)

Regression in StatKey (3 of 3)

Regression in StatKey (3 of 3)

Linear regression interpretation and cautions (2 of 3)

Linear regression interpretation and cautions (2 of 3)

Intro to linear regression (1 of 3)

Intro to linear regression (1 of 3)

Two quantitative variables: correlation (2 of 2)

Two quantitative variables: correlation (2 of 2)

Two quantitative variables: scatterplot (1 of 2)

Two quantitative variables: scatterplot (1 of 2)

More data viz history (1960s-2010s)

More data viz history (1960s-2010s)

Some data viz history (15,000 BC - 1960s)

Some data viz history (15,000 BC - 1960s)

A few of my favorite data visualizations

A few of my favorite data visualizations

Downloading natality data from ICPSR

Downloading natality data from ICPSR

The Standard Normal (video 3 of 3)

The Standard Normal (video 3 of 3)

Learning things from the Normal distribution (video 2 of 3)

Learning things from the Normal distribution (video 2 of 3)

Introduction to the Normal distribution (video 1 of 3)

Introduction to the Normal distribution (video 1 of 3)

One quantitative, one categorical (2.4 video 1 of 1)

One quantitative, one categorical (2.4 video 1 of 1)

One quantitative variable: spread

One quantitative variable: spread

One quantitative variable: center and shape

One quantitative variable: center and shape

What is data journalism?

What is data journalism?

What is journalism?

What is journalism?

Introduction to data communication

Introduction to data communication

More conditional probability (P.1 video 3 of 4)

More conditional probability (P.1 video 3 of 4)

Special cases (P.1 video 4 of 4)

Special cases (P.1 video 4 of 4)

More probability rules (P.1 video 2 of 4)

More probability rules (P.1 video 2 of 4)

Intro to probability rules (P.1, video 1 of 4)

Intro to probability rules (P.1, video 1 of 4)

Exploratory data analysis for two categorical variables

Exploratory data analysis for two categorical variables

Exploratory data analysis for one categorical variable

Exploratory data analysis for one categorical variable

Experiments and observational studies

Experiments and observational studies

The structure of data

The structure of data

Sampling from a population

Sampling from a population

Inference for regression pre-lab, part 3 of 3

Inference for regression pre-lab, part 3 of 3

Chi-square pre-lab (tidy) video 1 of 1

Chi-square pre-lab (tidy) video 1 of 1

ARIMA models (STAT 320 chapter 12 video 6 of 6)

ARIMA models (STAT 320 chapter 12 video 6 of 6)

AR and MA models (STAT 320 chapter 12 video 5 of 6)

AR and MA models (STAT 320 chapter 12 video 5 of 6)

Lags and temporal autocorrelation (STAT 320 chapter 12 video 4 of 6)

Lags and temporal autocorrelation (STAT 320 chapter 12 video 4 of 6)

Chi-square pre-lab (formula) video 1 of 2

Chi-square pre-lab (formula) video 1 of 2

Assessing the logistic model in R (STAT 320 lab_logistic_cont video 1 of 1)

Assessing the logistic model in R (STAT 320 lab_logistic_cont video 1 of 1)

Checking the conditions for logistic regression (STAT 320 ch 9 video 6 of 6)

Checking the conditions for logistic regression (STAT 320 ch 9 video 6 of 6)

Hypothesis tests in logistic regression (STAT 320 ch 9 video 5 of 6)

Hypothesis tests in logistic regression (STAT 320 ch 9 video 5 of 6)

Logistic regression in R (STAT 320 lab_logistic video 1 of 1)

Logistic regression in R (STAT 320 lab_logistic video 1 of 1)

Logistic regression algebra (STAT 320 ch 9, video 4 of 6)

Logistic regression algebra (STAT 320 ch 9, video 4 of 6)

The three "spaces" of logistic regression (STAT 320 ch 9 video 3 of 6)

The three "spaces" of logistic regression (STAT 320 ch 9 video 3 of 6)

Probability and odds (STAT 320 ch 9 video 2 of 6)

Probability and odds (STAT 320 ch 9 video 2 of 6)

Setting up logistic regression (STAT 320 ch 9 video 1 of 6)

Setting up logistic regression (STAT 320 ch 9 video 1 of 6)

Inference for two samples pre-lab (tidy) part 1 of 2

Inference for two samples pre-lab (tidy) part 1 of 2

Inference for two samples pre-lab (tidy) part 2 of 2

Inference for two samples pre-lab (tidy) part 2 of 2

Introduction to two-way ANOVA (STAT 320 ch 6 video 1 of 2)

Introduction to two-way ANOVA (STAT 320 ch 6 video 1 of 2)

Correcting for multiple comparisons in R (STAT 320 lab_multiple_testing video 1 or 1)

Correcting for multiple comparisons in R (STAT 320 lab_multiple_testing video 1 or 1)

Completing the ANOVA table for two-way ANOVA (STAT 320 ch 6 video 2 of 2)

Completing the ANOVA table for two-way ANOVA (STAT 320 ch 6 video 2 of 2)

More one-way ANOVA (STAT 320 ch 5, video 2 of 2)

More one-way ANOVA (STAT 320 ch 5, video 2 of 2)

Ways to write the ANOVA model (STAT 320 ch 5, video 1 of 2)

Ways to write the ANOVA model (STAT 320 ch 5, video 1 of 2)

Inference for a single mean pre-lab (tidy) video 1 of 1

Inference for a single mean pre-lab (tidy) video 1 of 1

Inference for a single proportion pre-lab (tidy) video 1 of 1

Inference for a single proportion pre-lab (tidy) video 1 of 1

Bootstrap in R (STAT 320 lab_CV_and_simulation video 3 of 3)

Bootstrap in R (STAT 320 lab_CV_and_simulation video 3 of 3)

Randomization in R (STAT 320 lab_CV_and_simulation video 2 of 3)

Randomization in R (STAT 320 lab_CV_and_simulation video 2 of 3)

Testing and training data in R (STAT 320 lab_CV_and_simulation video 1 of 3)

Testing and training data in R (STAT 320 lab_CV_and_simulation video 1 of 3)

Variable selection in R (STAT 320 lab_stepwise, video 1 of 1)

Variable selection in R (STAT 320 lab_stepwise, video 1 of 1)

Randomization and the bootstrap (STAT 320 chapter 4 video 4 of 4)

Randomization and the bootstrap (STAT 320 chapter 4 video 4 of 4)

Testing/training data (STAT 320 chapter 4 video 3 of 4)

Testing/training data (STAT 320 chapter 4 video 3 of 4)

Statistics for model comparison (STAT 320 chapter 4, video 2 of 4)

Statistics for model comparison (STAT 320 chapter 4, video 2 of 4)

Variable selection methods (STAT 320, chapter 4 video 1 of 4 )

Variable selection methods (STAT 320, chapter 4 video 1 of 4 )

Nested F-tests in R (STAT 320 chapter 3 lab_multicolinearity video 3 of 3)

Nested F-tests in R (STAT 320 chapter 3 lab_multicolinearity video 3 of 3)

Iteratively using CFAU in mlr (STAT 320 chapter 3 lab_multicolinearity video 2 of 3)

Iteratively using CFAU in mlr (STAT 320 chapter 3 lab_multicolinearity video 2 of 3)

Pairs plots in R (STAT 320 chapter 3 lab_multicolinearity video 1 of 3)

Pairs plots in R (STAT 320 chapter 3 lab_multicolinearity video 1 of 3)

Polynomial regression, VIF, nested F-tests (STAT 320 chapter 3 video 3 of 3)

Polynomial regression, VIF, nested F-tests (STAT 320 chapter 3 video 3 of 3)

Even more interaction terms (STAT 320 chapter 3 lab_mlr video 3 of 3)

Even more interaction terms (STAT 320 chapter 3 lab_mlr video 3 of 3)

Regression pre-lab (tidy) video 1 of 1

Regression pre-lab (tidy) video 1 of 1

Geometric visualizations of multiple regression models (STAT 320 chapter 3 lab_mlr video 2 of 3)

Geometric visualizations of multiple regression models (STAT 320 chapter 3 lab_mlr video 2 of 3)

Regression pre-lab (formula) video 1 of 1

Regression pre-lab (formula) video 1 of 1

Multiple regression in R (STAT 320, chapter 3 lab_mlr video 1 of 3)

Multiple regression in R (STAT 320, chapter 3 lab_mlr video 1 of 3)

Notation and geometry for multiple regression (STAT 320, chapter 3 video 2 of 3)

Notation and geometry for multiple regression (STAT 320, chapter 3 video 2 of 3)

Review of confidence intervals (STAT 320)

Review of confidence intervals (STAT 320)

Review of hypothesis testing (STAT 320)

Review of hypothesis testing (STAT 320)

Quantitative variables pre-lab (tidy) part 2/2

Quantitative variables pre-lab (tidy) part 2/2

Quantitative variables pre-lab (tidy) part 1/2

Quantitative variables pre-lab (tidy) part 1/2

Quantitative variables pre-lab (formula) part 2/2

Quantitative variables pre-lab (formula) part 2/2

Quantitative variables pre-lab (formula) part 1/2

Quantitative variables pre-lab (formula) part 1/2

Introduction to multiple regression (STAT 320, chapter 3 video 1 of 3)

Introduction to multiple regression (STAT 320, chapter 3 video 1 of 3)

Prediction and confidence intervals for fitted values (STAT 320, lab_regression_summary video 2/2)

Prediction and confidence intervals for fitted values (STAT 320, lab_regression_summary video 2/2)

Building the regression summary table in R (STAT 320, lab_regression_summary video 1 of 2)

Building the regression summary table in R (STAT 320, lab_regression_summary video 1 of 2)

Sums of squares, intervals (STAT 320, ch 2 video 4 of 4)

Sums of squares, intervals (STAT 320, ch 2 video 4 of 4)

Uses for partitioned variability (STAT 320 ch 2 video 3 of 4)

Uses for partitioned variability (STAT 320 ch 2 video 3 of 4)

Intro to ANOVA and partitioning variability (STAT 320 ch 2 video 2 of 4)

Intro to ANOVA and partitioning variability (STAT 320 ch 2 video 2 of 4)

Categorical variables pre-lab (tidy) part 2/2

Categorical variables pre-lab (tidy) part 2/2

Categorical variables pre-lab (tidy) part 1/2

Categorical variables pre-lab (tidy) part 1/2

Categorical variables pre-lab (formula) part 2/2

Categorical variables pre-lab (formula) part 2/2

Categorical variables pre-lab (formula) part 1/2

Categorical variables pre-lab (formula) part 1/2

Intro to regression inference (STAT 320, ch 2 video 1 of 4)

Intro to regression inference (STAT 320, ch 2 video 1 of 4)

Checking for outliers in R (STAT 320, lab_residuals video 2 of 2)

Checking for outliers in R (STAT 320, lab_residuals video 2 of 2)

Transformations in R (STAT 320, lab_residuals video 1 of 2)

Transformations in R (STAT 320, lab_residuals video 1 of 2)

Outliers: leverage and influence (STAT 320, chapter 1 video 6 of 6)

Outliers: leverage and influence (STAT 320, chapter 1 video 6 of 6)

Transformations (STAT 320, chapter 1 video 5 of 6)

Transformations (STAT 320, chapter 1 video 5 of 6)

Describing data pre-lab (tidy) video 1 of 1

Describing data pre-lab (tidy) video 1 of 1

Describing data pre-lab (formula) video 1 of 1

Describing data pre-lab (formula) video 1 of 1

Submitting your finished lab to Canvas

Submitting your finished lab to Canvas

LINE conditions for linear regression (STAT 320, chapter 1 video 4 of 6)

LINE conditions for linear regression (STAT 320, chapter 1 video 4 of 6)

Exploratory Data Analysis and simple modeling in R and RStudio (STAT 320, lab-EDA video 2 of 2)

Exploratory Data Analysis and simple modeling in R and RStudio (STAT 320, lab-EDA video 2 of 2)

Introduction to R and RStudio (STAT 320, lab-EDA video 1 of 2)

Introduction to R and RStudio (STAT 320, lab-EDA video 1 of 2)

Simple linear regression example (STAT 320, chapter 1 video 3 of 6)

Simple linear regression example (STAT 320, chapter 1 video 3 of 6)

Simple linear regression notation (STAT 320, chapter 1 video 2 of 6)

Simple linear regression notation (STAT 320, chapter 1 video 2 of 6)

Model notation (STAT 320, chapter 1 video 1 of 6)

Model notation (STAT 320, chapter 1 video 1 of 6)

Installing R and RStudio

Installing R and RStudio

Using RSelenium to scrape photos from Facebook

Using RSelenium to scrape photos from Facebook

Weird "visualizations" (sonification, physicalization, tastification, smellification)

Weird "visualizations" (sonification, physicalization, tastification, smellification)

Overview of randomization

Overview of randomization

Inference review

Inference review

Inference for many proportions-- chi-square tests for association

Inference for many proportions-- chi-square tests for association

Inference for many proportions-- chi-square tests for goodness of fit (video 2 of 2)

Inference for many proportions-- chi-square tests for goodness of fit (video 2 of 2)

Inference for many proportions-- chi-square tests for goodness of fit (video 1 of 2)

Inference for many proportions-- chi-square tests for goodness of fit (video 1 of 2)

Inference after ANOVA (8.2, video 2 of 2)

Inference after ANOVA (8.2, video 2 of 2)

Inference after ANOVA (8.2, video 1 of 2)

Inference after ANOVA (8.2, video 1 of 2)

Interactivity, how do they do it?

Interactivity, how do they do it?

ANOVA pre-lab (formula) part 2/2

ANOVA pre-lab (formula) part 2/2

ANOVA pre-lab (formula) part 1/2

ANOVA pre-lab (formula) part 1/2

ANOVA pre-lab (tidy) part 2/2

ANOVA pre-lab (tidy) part 2/2

ANOVA pre-lab (tidy) part 1/2

ANOVA pre-lab (tidy) part 1/2

Finding p-values for ANOVA (3 of 3)

Finding p-values for ANOVA (3 of 3)

Building the ANOVA table (2 of 3)

Building the ANOVA table (2 of 3)

Introduction to ANOVA (1 of 3)

Introduction to ANOVA (1 of 3)

Inference for two samples pre-lab (formula) part 2 of 2

Inference for two samples pre-lab (formula) part 2 of 2

Inference for two samples pre-lab (formula) part 1 of 2

Inference for two samples pre-lab (formula) part 1 of 2

Inference for regression pre-lab (formula), part 1 of 3

Inference for regression pre-lab (formula), part 1 of 3

Inference for regression pre-lab (tidy), part 1 of 3

Inference for regression pre-lab (tidy), part 1 of 3

Inference for regression pre-lab, part 2 of 3

Inference for regression pre-lab, part 2 of 3

Confidence and prediction intervals (1 of 1)

Confidence and prediction intervals (1 of 1)

Inference for regression (9.1, video 6 of 6)

Inference for regression (9.1, video 6 of 6)

Inference for regression (9.1, video 5 of 6)

Inference for regression (9.1, video 5 of 6)

Inference for regression (9.1, video 4 of 6)

Inference for regression (9.1, video 4 of 6)

Inference for regression (3 of 6)

Inference for regression (3 of 6)

Inference for regression (2 of 6)

Inference for regression (2 of 6)

Inference for regression (9.1, video 1 of 6)

Inference for regression (9.1, video 1 of 6)

Behind the scenes-- how I screen-record slides and video

Behind the scenes-- how I screen-record slides and video

Installing R and RStudio locally

Installing R and RStudio locally

Hypothesis tests for a single mean using simulation methods (6.2-HT)

Hypothesis tests for a single mean using simulation methods (6.2-HT)

Confidence intervals for a single mean using simulation methods (6.2-CI video 2 of 2)

Confidence intervals for a single mean using simulation methods (6.2-CI video 2 of 2)

Inference for a single sample pre-lab (formula) part 3/3

Inference for a single sample pre-lab (formula) part 3/3

Inference for a single sample pre-lab (formula) part 2/3

Inference for a single sample pre-lab (formula) part 2/3