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## Course Description

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.- 1
### Introduction to R

**Free**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.

Welcome!100 xpLoad a data set into R100 xpExamining the data set100 xpQuestion 150 xpSome more exploration100 xpQuestion 250 xpPutting it in a graph100 xpConnecting the dots100 xpQuestion 350 xpUsing the help function100 xpR - The big calculator100 xpQuestion 450 xpQuestion 550 xpComparing the data set100 xpQuestion 650 xpChallenge100 xpQuestion 750 xpQuestion 850 xpEnd of Lab Survey - Question 10 xpEnd of Lab Survey - Question 20 xpEnd of Lab Survey - Question 30 xpEnd of Lab Survey - Question 40 xpEnd of Lab Survey - Question 50 xp - 2
### Introduction to data

**Free**Some define Statistics as the field that focuses on turning information into knowledge. The first step in that process is to summarize and describe the raw information - the data. In this lab, we will gain insight into public health by generating simple graphical and numerical summaries of a data set collected by the Centers for Disease Control and Prevention (CDC). As this is a large data set, along the way we'll also learn the indispensable skills of data processing and subsetting.

Welcome!100 xpWhich variables are you working with?100 xpTaking a peek at your data100 xpQuestion 150 xpQuestion 250 xpQuestion 350 xpQuestion 450 xpLet's refresh100 xpTurning info into knowledge - Numerical data100 xpTurning info into knowledge - Categorical data100 xpCreating your first barplot100 xpQuestion 550 xpQuestion 650 xpEven prettier: the Mosaic Plot100 xpQuestion 750 xpInterlude: How R thinks about data (1)100 xpInterlude (2)100 xpInterlude (3)100 xpInterlude (4)100 xpA little more on subsetting100 xpSubset - one last time100 xpQuestion 850 xpVisualizing with box plots100 xpMore on box plots100 xpOne last box plot100 xpQuestion 950 xpHistograms100 xpWeight vs. Desired Weight100 xpQuestion 1050 xpEnd of Lab Survey - Question 10 xpEnd of Lab Survey - Question 20 xpEnd of Lab Survey - Question 30 xpEnd of Lab Survey - Question 40 xpEnd of Lab Survey - Question 50 xp - 3
### Probability

**Free**In this lab, we will investigate the phenomenon of hot hands in basketball, or specifically, whether Kobe Bryant has hot hands. We will make use of simulations in our investigation.

Let's shoot some hoops!100 xpGetting started100 xpKobe's track record100 xpQuestion 150 xpQuestion 250 xpA first analysis100 xpQuestion 350 xpQuestion 450 xpQuestion 550 xpSimulations in R100 xpFlipping 100 coins100 xpFlipping an unfair coin100 xpQuestion 650 xpSimulating the Independent Shooter100 xpKobe vs. the Independent Shooter100 xpQuestion 750 xpQuestion 850 xpQuestion 950 xpEnd of Lab Survey - Question 10 xpEnd of Lab Survey - Question 20 xpEnd of Lab Survey - Question 30 xpEnd of Lab Survey - Question 40 xpEnd of Lab Survey - Question 50 xp - 4
### Foundations for inference: Sampling distributions

**Free**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.

Sampling distributions100 xpA first distribution analysis100 xpQuestion 150 xpSampling from the population100 xpQuestion 250 xpThe sampling distribution100 xpInterlude: The for loop100 xpInterlude: Breaking it down100 xpYour first for loop100 xpQuestion 350 xpQuestion 450 xpMore on sampling100 xpEven more on sampling100 xpInfluence of sample size100 xpQuestion 550 xpNow: prices!100 xpSampling distribution of prices100 xpMore on sampling distribution of prices100 xpQuestion 650 xpEnd of Lab Survey - Question 10 xpEnd of Lab Survey - Question 20 xpEnd of Lab Survey - Question 30 xpEnd of Lab Survey - Question 40 xpEnd of Lab Survey - Question 50 xp - 5
### Foundations for inference: Confidence intervals

**Free**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.

One sample from Ames, Iowa100 xpQuestion 750 xpConfidence intervals100 xpQuestion 850 xpQuestion 950 xpQuestion 1050 xpChallenge (1)100 xpChallenge (2)100 xpChallenge (3)100 xpQuestion 1150 xpThe 99%100 xpQuestion 1250 xpEnd of Lab Survey - Question 10 xpEnd of Lab Survey - Question 20 xpEnd of Lab Survey - Question 30 xpEnd of Lab Survey - Question 40 xpEnd of Lab Survey - Question 50 xp - 6
### Inference for numerical data

**Free**In this two part lab we will work on inference for numerical data. We will use a dataset on births from North Carolina as well as data from the General Social Survey.

North Carolina births100 xpQuestion 150 xpA first analysis100 xpQuestion 250 xpCleaning your data100 xpThe bootstrap100 xpQuestion 350 xpQuestion 450 xpThe inference function100 xpSetting the confidence level100 xpChoosing a bootstrap method100 xpSetting the parameter of interest100 xpQuestion 550 xpFather's age100 xpRelationships between two variables100 xpQuestion 650 xpThe by function100 xpConditions for inference100 xpMore inference100 xpChanging the order100 xpQuestion 750 xpQuestion 850 xpThe General Social Survey100 xpAnalyze the variables100 xpQuestion 950 xpANOVA test100 xpQuestion 1050 xpQuestion 1150 xpEnd of Lab Survey - Question 10 xpEnd of Lab Survey - Question 20 xpEnd of Lab Survey - Question 30 xpEnd of Lab Survey - Question 40 xpEnd of Lab Survey - Question 50 xp - 7
### Inference for categorical data

**Free**In this lab we will work on inference for categorical data using data from a world-wide survey on religiosity and atheism.

The rise of atheism in America100 xpQuestion 150 xpQuestion 250 xpQuestion 350 xpQuestion 450 xpThe data100 xpQuestion 550 xpQuestion 650 xpAtheists in the US100 xpQuestion 750 xpInference conditions100 xpQuestion 850 xpWhat about India?100 xpAnd China?100 xpThe margin of error100 xpQuestion 950 xpAtheism in Spain100 xpQuestion 1050 xpRising in the US?100 xpQuestion 1150 xpQuestion 1250 xpQuestion 1350 xpEnd of Lab Survey - Question 10 xpEnd of Lab Survey - Question 20 xpEnd of Lab Survey - Question 30 xpEnd of Lab Survey - Question 40 xpEnd of Lab Survey - Question 50 xp - 8
### Introduction to linear regression

**Free**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.

Moneyball100 xpQuestion 150 xpQuestion 250 xpQuantifying the strength of a linear relationship100 xpQuestion 350 xpEstimating the best fit100 xpThe best fit100 xpThe linear model100 xpUnderstanding the output of lm100 xpBreaking it down100 xpQuestion 450 xpPrediction and prediction errors100 xpQuestion 550 xpQuestion 650 xpQuestion 750 xpQuestion 850 xpQuestion 950 xpQuestion 1050 xpEnd of Lab Survey - Question 10 xpEnd of Lab Survey - Question 20 xpEnd of Lab Survey - Question 30 xpEnd of Lab Survey - Question 40 xpEnd of Lab Survey - Question 50 xp - 9
### Multiple linear regression

**Free**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.

Grading the professor100 xpThe data100 xpQuestion 150 xpQuestion 250 xpQuestion 350 xpVisualizing relationships100 xpSimple Linear Regression100 xpThe jitter function100 xpMore than natural variation?100 xpQuestion 450 xpQuestion 550 xpMultiple linear regression100 xpThe relationship between all beauty variables.100 xpTaking into account gender100 xpQuestion 650 xpGendermale100 xpQuestion 750 xpSwitching rank and gender100 xpQuestion 850 xpThe search for the best model100 xpQuestion 950 xpEliminating variables from the model - p-value selection100 xpEliminating variables from the model - adjusted R-squared selection100 xpQuestion 1050 xpEnd of Lab Survey - Question 10 xpEnd of Lab Survey - Question 20 xpEnd of Lab Survey - Question 30 xpEnd of Lab Survey - Question 40 xpEnd of Lab Survey - Question 50 xp

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