# Statistical Inference - Introduction

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| Welcome to swirl! Please sign in. If you've been here before, use the same name as
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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

 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 https://github.com/DataScienceSpecialization/courses.
| 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

...

|=============================== | 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

|====================================== | 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

...

|============================================================== | 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 Coursera.org?

1: No
2: Yes

Selection: 2
What is your assignment token? xXxXxxXXxXxxXXXx

| 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