Udemy - First steps in data analysis with R

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[ FreeCourseWeb.com ] Udemy - First steps in data analysis with R
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Introduction
    • 1. Introduction.mp4 (19.7 MB)
    • 1. Introduction.srt (1.8 KB)
    • 1.1 ancova.pdf (117.9 KB)
    • 1.2 anova1.pdf (453.9 KB)
    • 1.3 anova2.pdf (152.1 KB)
    • 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)
    • 2. Installing R.mp4 (34.6 MB)
    • 2. Installing R.srt (5.4 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 (1.0 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 (2.0 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)
      • First steps in data analysis with R byMarco Plebani - all scripts and data
      • DS_Store (8.0 KB)
        • my_data
        • ANCOVA_data.csv (3.7 KB)
        • DS_Store (6.0 KB)
        • Rapp.history (6.3 KB)
        • 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 (7.0 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 (0.7 KB)
        my_scripts ANNOTATED
        • 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 (7.0 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)
        • 10 - non-linear modelling.R (4.2 KB)
        • 11 - glm Poisson.R (3.8 KB)
        • 12 - data wrangling.R (3.9 KB)
        __MACOSX First steps in data analysis with R byMarco Plebani - all scripts and data
        • _.DS_Store (0.1 KB)
          • my_data
          • _.DS_Store (0.1 KB)
          my_scripts ANNOTATED
          • _01 - first steps.R (0.2 KB)
          • _02a - compare two groups with ttest.R (0.3 KB)
          • _02b - compare two groups with ttest on data from dataframe.R (0.3 KB)
          • _02c - compare two groups with lm.R (0.3 KB)
          • _03 - compare more than two groups.R (0.2 KB)
          • _04 - linear regression.R (0.2 KB)
          • _05 - Intro to ggplot.R (0.3 KB)
          • _06 - testing the effects of factorial + continuous predictors.R (0.2 KB)
          • _07 - testing the effect of two factorial predictors.R (0.2 KB)
          • _08 - testing the effect of two continuous predictors.R (0.3 KB)
          • _09 - polynomial regression.R (0.3 KB)
          • _10 - non-linear modelling.R (0.3 KB)
          • _11 - glm Poisson.R (0.3 KB)
          • _12 - data wrangling.R (0.2 KB)
          10. Testing the effect of two factorial predictors on a response variable
          • 1. Concepts, scenarios, and worked example.mp4 (79.4 MB)
          • 1. Concepts, scenarios, and worked example.srt (10.0 KB)
          • 1.1 07 - testing the effect of two factorial predictors.R (5.7 KB)
          • 1.2 anova_type2_data.csv (13.7 KB)
          11. Multiple regression
          • 1. Scenarios and worked example.mp4 (128.6 MB)
          • 1. Scenarios and worked example.srt (16.3 KB)
          • 1.1 08 - testing the effect of two continuous predictors.R (4.9 KB)
          • 1.2 multi_reg_data.csv (7.0 KB)
          12. Polynomial regression
          • 1. Example 1.mp4 (52.3 MB)
          • 1. Example 1.srt (6.8 KB)
          • 1.1 09 - polynomial regression.R (2.6 KB)
          • 1.2 non_linear_datasets.xlsx (54.1 KB)
          • 2. Example 2.mp4 (26.1 MB)
          • 2. Example 2.srt (3.3 KB)
          13. Fitting user-defined non-linear models
          • 1. An example using function nls().mp4 (91.7 MB)
          • 1. An example using function nls().srt (9.5 KB)
          • 1.1 10 - non-linear modelling.R (4.2 KB)
          • 1.2 non_linear_datasets.xlsx (54.1 KB)
          14. Dealing with non-normality of residuals
          • 1. An example with count data.mp4 (20.3 MB)
          • 1. An example with count data.srt (8.2 KB)
          • 1.1 11 - glm Poisson.R (3.8 KB)
          • 1.2 count_data.csv (1.8 KB)
          • 1.3 GLM resources.pdf (43.5 KB)
          15. Data wrangling
          • 1. A panoramic of some data-wrangling tools.mp4 (55.7 MB)
          • 1. A panoramic of some data-wrangling tools.srt (8.4 KB)
          • 1.1 12 - data wrangling.R (3.9 KB)
          16. Final remarks
          • 1. Final remarks.mp4 (10.6 MB)
          • 1. Final remarks.srt (5.2 KB)
          • 1.1 Manuals and other resources.pdf (56.3 KB)
          2. Housework
          • 1. Housework on a Mac.mp4 (16.2 MB)
          • 1. Housework on a Mac.srt (2.8 KB)
          • 1.1 housework mac.R (0.2 KB)

Description

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 - First steps in data analysis with R

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Download torrent
1.2 GB
seeders:5
leechers:5
Udemy - First steps in data analysis with R

Torrent hash: 5F6F697D32F3929E848695415239AA58050228CE