| 01 - first steps.R | 1.4 KB | ||
| 02a - compare two groups with ttest.R | 2.3 KB | ||
| 02b - compare two groups with ttest on data from dataframe.R | 3.8 KB | ||
| 02c - compare two groups with lm.R | 5.5 KB | ||
| 03 - compare more than two groups.R | 6.4 KB | ||
| 04 - linear regression.R | 3.3 KB | ||
| 05 - Intro to ggplot.R | 3.2 KB | ||
| 06 - testing the effects of factorial + continuous predictors.R | 7 KB | ||
| 07 - testing the effect of two factorial predictors.R | 5.7 KB | ||
| 08 - testing the effect of two continuous predictors.R | 4.9 KB | ||
| 09 - polynomial regression.R | 2.6 KB | ||
| 1. A panoramic of some data-wrangling tools.mp4 | 55.7 MB | ||
| 1. A panoramic of some data-wrangling tools.srt | 8.4 KB | ||
| 1. An example using function nls().mp4 | 91.7 MB | ||
| 1. An example using function nls().srt | 9.5 KB | ||
| 1. An example with count data.mp4 | 20.3 MB | ||
| 1. An example with count data.srt | 8.2 KB | ||
| 1. Comparing more than two groups with lm().mp4 | 133.6 MB | ||
| 1. Comparing more than two groups with lm().srt | 17.4 KB | ||
| 1. Concepts, scenarios, and worked example.mp4 | 97.1 MB | ||
| 1. Concepts, scenarios, and worked example.srt | 15.2 KB | ||
| 1. Example 1.mp4 | 52.3 MB | ||
| 1. Example 1.srt | 6.8 KB | ||
| 1. Final remarks.mp4 | 10.6 MB | ||
| 1. Final remarks.srt | 5.2 KB | ||
| 1. Housework on a Mac.mp4 | 16.2 MB | ||
| 1. Housework on a Mac.srt | 2.8 KB | ||
| 1. How to produce and interpret diagnostic plots.mp4 | 33.3 MB | ||
| 1. How to produce and interpret diagnostic plots.srt | 9.3 KB | ||
| 1. Introducing lm() - example comparing two groups.mp4 | 68.6 MB | ||
| 1. Introducing lm() - example comparing two groups.srt | 9.8 KB | ||
| 1. Introduction to ggplot2.mp4 | 99.3 MB | ||
| 1. Introduction to ggplot2.srt | 11.1 KB | ||
| 1. Introduction.mp4 | 19.7 MB | ||
| 1. Introduction.srt | 1.8 KB | ||
| 1. Performing linear regression in R.mp4 | 47.9 MB | ||
| 1. Performing linear regression in R.srt | 6.8 KB | ||
| 1. Scenarios and worked example.mp4 | 128.6 MB | ||
| 1. Scenarios and worked example.srt | 16.3 KB | ||
| 1. t.test() - part 1.mp4 | 20.1 MB | ||
| 1. t.test() - part 1.srt | 9.1 KB | ||
| 1.1 02a - compare two groups with ttest.R | 2.3 KB | ||
| 1.1 02c - compare two groups with lm.R | 5.5 KB | ||
| 1.1 03 - compare more than two groups.R | 6.4 KB | ||
| 1.1 04 - linear regression.R | 3.3 KB | ||
| 1.1 05 - Intro to ggplot.R | 3.2 KB | ||
| 1.1 06 - testing the effects of factorial + continuous predictors.R | 7 KB | ||
| 1.1 07 - testing the effect of two factorial predictors.R | 5.7 KB | ||
| 1.1 08 - testing the effect of two continuous predictors.R | 4.9 KB | ||
| 1.1 09 - polynomial regression.R | 2.6 KB | ||
| 1.1 10 - non-linear modelling.R | 4.2 KB | ||
| 1.1 11 - glm Poisson.R | 3.8 KB | ||
| 1.1 12 - data wrangling.R | 3.9 KB | ||
| 1.1 Manuals and other resources.pdf | 56.3 KB | ||
| 1.1 ancova.pdf | 117.9 KB | ||
| 1.1 housework mac.R | 204.8 B | ||
| 1.2 ANCOVA_data.csv | 3.7 KB | ||
| 1.2 anova1.pdf | 453.9 KB | ||
| 1.2 anova_type2_data.csv | 13.7 KB | ||
| 1.2 compare_2_species.csv | 2.9 KB | ||
| 1.2 compare_6_species.txt | 46.5 KB | ||
| 1.2 count_data.csv | 1.8 KB | ||
| 1.2 multi_reg_data.csv | 7 KB | ||
| 1.2 non_linear_datasets.xlsx | 54.1 KB | ||
| 1.2 regression_data.csv | 716.8 B | ||
| 1.3 GLM resources.pdf | 43.5 KB | ||
| 1.3 anova2.pdf | 152.1 KB | ||
| 1.3 regression_data.csv | 716.8 B | ||
| 1.4 multi_reg.pdf | 138.8 KB | ||
| 1.5 non_linear.pdf | 161.5 KB | ||
| 1.6 poisson.pdf | 242.6 KB | ||
| 1.7 poly2.pdf | 72.7 KB | ||
| 1.8 sim_heterosc.pdf | 87.3 KB | ||
| 10 - non-linear modelling.R | 4.2 KB | ||
| 11 - glm Poisson.R | 3.8 KB | ||
| 12 - data wrangling.R | 3.9 KB | ||
| 2. Evaluating diagnostic plots for a linear regression.mp4 | 5.8 MB | ||
| 2. Evaluating diagnostic plots for a linear regression.srt | 4.3 KB | ||
| 2. Example 2.mp4 | 26.1 MB | ||
| 2. Example 2.srt | 3.3 KB | ||
| 2. Housework on a Windows machine.mp4 | 20.9 MB | ||
| 2. Housework on a Windows machine.srt | 2.4 KB | ||
| 2. Installing R.mp4 | 34.6 MB | ||
| 2. Installing R.srt | 5.4 KB | ||
| 2. t.test() - part 2.mp4 | 72.2 MB | ||
| 2. t.test() - part 2.srt | 8.8 KB | ||
| 2.1 02b - compare two groups with ttest on data from dataframe.R | 3.8 KB | ||
| 2.1 housework windows.R | 512 B | ||
| 2.2 compare_2_species.csv | 2.9 KB | ||
| 3. Housework in RStudio.mp4 | 23.4 MB | ||
| 3. Housework in RStudio.srt | 3.3 KB | ||
| 3. Writing code and creating object in the R console.mp4 | 4.7 MB | ||
| 3. Writing code and creating object in the R console.srt | 1 KB | ||
| 4. Housework - final remarks.mp4 | 5.1 MB | ||
| 4. Housework - final remarks.srt | 1.1 KB | ||
| 4. Your first R script.mp4 | 24.2 MB | ||
| 4. Your first R script.srt | 5.3 KB | ||
| 4.1 01 - first steps.R | 1.4 KB | ||
| 5. Your first graph.mp4 | 9.5 MB | ||
| 5. Your first graph.srt | 2 KB | ||
| 6. Your second graph.mp4 | 40.5 MB | ||
| 6. Your second graph.srt | 6.4 KB | ||
| 7. Saving figures on a Windows machine.mp4 | 15.4 MB | ||
| 7. Saving figures on a Windows machine.srt | 2.1 KB | ||
| ANCOVA_data.csv | 3.7 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| DS_Store | 6 KB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| Rapp.history | 6.3 KB | ||
| _.DS_Store | 102.4 B | ||
| _01 - first steps.R | 204.8 B | ||
| _02a - compare two groups with ttest.R | 307.2 B | ||
| _02b - compare two groups with ttest on data from dataframe.R | 307.2 B | ||
| _02c - compare two groups with lm.R | 307.2 B | ||
| _03 - compare more than two groups.R | 204.8 B | ||
| _04 - linear regression.R | 204.8 B | ||
| _05 - Intro to ggplot.R | 307.2 B | ||
| _06 - testing the effects of factorial + continuous predictors.R | 204.8 B | ||
| _07 - testing the effect of two factorial predictors.R | 204.8 B | ||
| _08 - testing the effect of two continuous predictors.R | 307.2 B | ||
| _09 - polynomial regression.R | 307.2 B | ||
| _10 - non-linear modelling.R | 307.2 B | ||
| _11 - glm Poisson.R | 307.2 B | ||
| _12 - data wrangling.R | 204.8 B | ||
| anova_type2_data.csv | 13.7 KB | ||
| compare_2_species.csv | 2.9 KB | ||
| compare_6_species.txt | 46.5 KB | ||
| count_data.csv | 1.8 KB | ||
| diel_activity_data.csv | 7.3 KB | ||
| multi_reg_data.csv | 7 KB | ||
| non_linear_datasets.xlsx | 54.1 KB | ||
| pairwise_tests.csv | 1.5 KB | ||
| poly_data1.csv | 1.4 KB | ||
| poly_data2.csv | 2.3 KB | ||
| regression_data.csv | 716.8 B | ||
| ▲ 141 total files | |||
First steps in data analysis with R
https://FreeCourseWeb.com
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 28 lectures (2h 50m) | Size: 1.17 GB
Data analysis from zero to hero
What you'll learn:
Develop a reliable data analysis framework
Refresh your statistical knowledge in a visual, intuitive way
Visualise your data with publication-ready figures
Become confident with testing general linear models: regression, ANOVA, etc.
Learn and apply the principles of hypothesis testing and model selection
Introduction to generalised linear models and to non-linear modelling
Requirements
No programming experience needed. You'll learn how to use R from absolute 0.
You should be familiar with statistical concepts covered in any introductory statistics course, such as: Normal distribution, model parameters, variance, standard deviation, standard error, F-test, p-value.
Description
This course is aimed at those that already have a theoretical understanding of statistical concepts and want to learn the practical side of data analysis.
Learning how to analyse data can be a daunting test. Applying the statistical knowledge learned from books to real-world scenarios can be challenging, and it's often made harder by seemingly complicated data analysis softwares.
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Udemy - Conversations That Drive Team Outcomes - First-Time Managers Posted by
freecoursewb in Other
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608.7 MB | freecoursewb | 1 day | 0 | 0 |
| 2.6 GB | freecoursewb | 2 weeks | 0 | 0 | |
| 872.3 MB | freecoursewb | 2 weeks | 4 | 5 | |
| 1.9 GB | freecoursewb | 2 weeks | 50 | 11 | |
| 1.1 GB | freecoursewb | 2 weeks | 18 | 13 |
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