# Statistical Inference - Introduction

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swirl()

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What shall I call you? Krishnakanth Allika

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1: Exploratory Data Analysis

2: Statistical Inference

3: Take me to the swirl course repository!

Selection: 2

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```
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

| 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 Coursera.org?

1: No

2: Yes

Selection: 2

What is your email address? [email protected]

What is your assignment token? xXxXxxXXxXxxXXXx

Grade submission succeeded!

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| 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*

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