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Data Analysis and Statistical Inference

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9 Horas233 Ejercicios
53.572 AprendicesTrophyDeclaración de cumplimiento

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Descripción del curso

This interactive DataCamp course complements the Coursera course Data Analysis and Statistical Inference by Mine Çetinkaya-Rundel. For every lesson given at Coursera, you can follow interactive exercises in the comfort of your browser to master the different topics.
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  1. 1

    Introduction to R

    Gratuito

    In this first lab, you'll learn the basics of how to analyze data with R. You are suggested to take this introductory lab if you are not yet familiar with this powerful open-source language.

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    Welcome!
    100 xp
    Load a data set into R
    100 xp
    Examining the data set
    100 xp
    Question 1
    50 xp
    Some more exploration
    100 xp
    Question 2
    50 xp
    Putting it in a graph
    100 xp
    Connecting the dots
    100 xp
    Question 3
    50 xp
    Using the help function
    100 xp
    R - The big calculator
    100 xp
    Question 4
    50 xp
    Question 5
    50 xp
    Comparing the data set
    100 xp
    Question 6
    50 xp
    Challenge
    100 xp
    Question 7
    50 xp
    Question 8
    50 xp
    End of Lab Survey - Question 1
    0 xp
    End of Lab Survey - Question 2
    0 xp
    End of Lab Survey - Question 3
    0 xp
    End of Lab Survey - Question 4
    0 xp
    End of Lab Survey - Question 5
    0 xp
  2. 5

    Foundations for inference: Confidence intervals

    Gratuito

    In this two part lab we will investigate sampling distributions and the Central Limit Theorem as well as confidence intervals. We will use housing data from Ames, Iowa (a small town in the US) in our exploration.

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  3. 8

    Introduction to linear regression

    Gratuito

    The movie Moneyball focuses on the "quest for the secret of success in baseball". It follows a low-budget team, the Oakland Athletics, who believed that underused statistics, such as a player's ability to get on base, better predict the ability to score runs than typical statistics like home runs, RBIs (runs batted in), and batting average. In this lab we'll be looking at data from all 30 Major League Baseball teams and examining the linear relationship between runs scored in a season and a number of other player statistics. Our aim will be to summarize these relationships both graphically and numerically in order to find which variable, if any, helps us best predict a team's runs scored in a season.

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  4. 9

    Multiple linear regression

    Gratuito

    Many college courses conclude by giving students the opportunity to evaluate the course and the instructor anonymously. However, the use of these student evaluations as an indicator of course quality and teaching effectiveness is often criticized because these measures may reflect the influence of non-teaching related characteristics, such as the physical appearance of the instructor. The article titled, "Beauty in the classroom: instructors' pulchritude and putative pedagogical productivity" (Hamermesh and Parker, 2005) found that instructors who are viewed to be better looking receive higher instructional ratings. In this lab we will analyze the data from this study in order to learn what goes into a positive professor evaluation.

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