Statistical Inference - Introduction

R version 4.0.0 (2020-04-24) -- "Arbor Day"
Copyright (C) 2020 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.


| Hi! Type swirl() when you are ready to begin.


| Welcome to swirl! Please sign in. If you've been here before, use the same name as
| you did then. If you are new, call yourself something unique.

What shall I call you? Krishnakanth Allika

| Please choose a course, or type 0 to exit swirl.

1: Exploratory Data Analysis
2: Statistical Inference
3: Take me to the swirl course repository!

Selection: 2

| Please choose a lesson, or type 0 to return to course menu.

 1: Introduction             2: Probability1             3: Probability2            
 4: ConditionalProbability   5: Expectations             6: Variance                
 7: CommonDistros            8: Asymptotics              9: T Confidence Intervals  
10: Hypothesis Testing      11: P Values                12: Power                   
13: Multiple Testing        14: Resampling

Selection: 1

| | 0%

| Introduction to Statistical_Inference. (Slides for this and other Data Science
| courses may be found at github
| If you care to use them, they must be downloaded as a zip file and viewed locally.
| This lesson corresponds to Statistical_Inference/Introduction.)


|======== | 10%
| In this lesson, we'll briefly introduce basics of statistical inference, the process
| of drawing conclusions "about a population using noisy statistical data where
| uncertainty must be accounted for". In other words, statistical inference lets
| scientists formulate conclusions from data and quantify the uncertainty arising from
| using incomplete data.


|=============== | 20%
| Which of the following is NOT an example of statistical inference?

1: Polling before an election to predict its outcome
2: Recording the results of a statistics exam
3: Testing the efficacy of a new drug
4: Constructing a medical image from fMRI data

Selection: 2

| All that hard work is paying off!

|======================= | 30%
| So statistical inference involves formulating conclusions using data AND quantifying
| the uncertainty associated with those conclusions. The uncertainty could arise from
| incomplete or bad data.


|=============================== | 40%
| Which of the following would NOT be a source of bad data?

1: Small sample size
2: Selection bias
3: A randomly selected sample of population
4: A poorly designed study

Selection: 3

| Your dedication is inspiring!

|====================================== | 50%
| So with statistical inference we use data to draw general conclusions about a
| population. Which of the following would a scientist using statistical inference
| techniques consider a problem?

1: Our study has no bias and is well-designed
2: Our data sample is representative of the population
3: Contaminated data

Selection: 3

| That's a job well done!

|============================================== | 60%
| Which of the following is NOT an example of statistical inference in action?

1: Testing the effectiveness of a medical treatment
2: Estimating the proportion of people who will vote for a candidate
3: Determining a causative mechanism underlying a disease
4: Counting sheep

Selection: 4

| You got it right!

|====================================================== | 70%
| We want to emphasize a couple of important points here. First, a statistic
| (singular) is a number computed from a sample of data. We use statistics to infer
| information about a population. Second, a random variable is an outcome from an
| experiment. Deterministic processes, such as computing means or variances, applied
| to random variables, produce additional random variables which have their own
| distributions. It's important to keep straight which distributions you're talking
| about.


|============================================================== | 80%
| Finally, there are two broad flavors of inference. The first is frequency, which
| uses "long run proportion of times an event occurs in independent, identically
| distributed repetitions." The second is Bayesian in which the probability estimate
| for a hypothesis is updated as additional evidence is acquired. Both flavors require
| an understanding of probability so that's what the next lessons will cover.


|===================================================================== | 90%
| Congrats! You've concluded this brief introduction to statistical inference.


|=============================================================================| 100%
| Would you like to receive credit for completing this course on

1: No
2: Yes

Selection: 2
What is your email address? [email protected]
What is your assignment token? xXxXxxXXxXxxXXXx
Grade submission succeeded!

| Great job!

| You've reached the end of this lesson! Returning to the main menu...

| Please choose a course, or type 0 to exit swirl.

1: Exploratory Data Analysis
2: Statistical Inference
3: Take me to the swirl course repository!

Selection: 0

| Leaving swirl now. Type swirl() to resume.

Last updated 2020-05-26 15:45:29.101530 IST