About this Course
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다음 전문 분야의 1개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

High school algebra, successful completion of Course 1 in this specialization or equivalent background

완료하는 데 약 19시간 필요

권장: 4 weeks of study, 4-6 hours/week...

영어

자막: 영어, 한국어

배울 내용

  • Check

    Determine assumptions needed to calculate confidence intervals for their respective population parameters.

  • Check

    Create confidence intervals in Python and interpret the results.

  • Check

    Review how inferential procedures are applied and interpreted step by step when analyzing real data.

  • Check

    Run hypothesis tests in Python and interpret the results.

귀하가 습득할 기술

Confidence IntervalPython ProgrammingStatistical InferenceStatistical Hypothesis Testing

다음 전문 분야의 1개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

High school algebra, successful completion of Course 1 in this specialization or equivalent background

완료하는 데 약 19시간 필요

권장: 4 weeks of study, 4-6 hours/week...

영어

자막: 영어, 한국어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 3시간 필요

WEEK 1 - OVERVIEW & INFERENCE PROCEDURES

In this first week, we’ll review the course syllabus and discover the various concepts and objectives to be mastered in weeks to come. You’ll be introduced to inference methods and some of the research questions we’ll discuss in the course, as well as an overall framework for making decisions using data, considerations for how you make those decisions, and evaluating errors that you may have made. On the Python side, we’ll review some high level concepts from the first course in this series, Python’s statistics landscape, and walk through intermediate level Python concepts. All of the course information on grading, prerequisites, and expectations are on the course syllabus and you can find more information on our Course Resources page.

...
8 videos (Total 62 min), 5 readings, 1 quiz
8개의 동영상
Introduction to Inference Methods: Oh the Things You Will See!3m
Bag A or Bag B?13m
Introduction to Bayesian4m
This or That? Language and Notation13m
The Python Statistics Landscape2m
Intermediate Python Concepts: Lists vs Numpy Arrays10m
Functions and Lambda Functions, Reading Help Files11m
5개의 읽기 자료
Course Syllabus5m
Meet the Course Team!10m
Help Us Learn More About You!10m
About Our Datasets2m
This or That Reference10m
1개 연습문제
Python Basics Assessment15m
2
완료하는 데 6시간 필요

WEEK 2 - CONFIDENCE INTERVALS

In this second week, we will learn about estimating population parameters via confidence intervals. You will be introduced to five different types of population parameters, assumptions needed to calculate a confidence interval for each of these five parameters, and how to calculate confidence intervals. Quizzes and a peer assessment will appear throughout the week to test your understanding. In addition, you’ll learn how to create confidence intervals in Python.

...
13 videos (Total 121 min), 4 readings, 4 quizzes
13개의 동영상
Understanding Confidence Intervals10m
Demo: Seeing Theory5m
Assumptions for a Single Population Proportion Confidence Interval3m
Conservative Approach & Sample Size Consideration8m
Estimating a Difference in Population Proportions with Confidence6m
Interpretations & Assumptions for Two Population Proportion Intervals4m
Estimating a Population Mean with Confidence14m
Estimating a Mean Difference for Paired Data10m
Estimating a Difference in Population Means with Confidence (for Independent Groups)14m
Chocolate & Cycling Assignment2m
Introduction to Confidence Intervals in Python12m
Confidence Intervals for Differences between Population Parameters21m
4개의 읽기 자료
Confidence Intervals: Other Considerations15m
What Affects the Standard Error of an Estimate?10m
Chocolate & Cycling Assignment Instructions5m
Additional Practice: Confidence Intervals1m
3개 연습문제
Practice Quiz: All About Confidence Intervals14m
Sample Size & Assumptions
Confidence Intervals Assessment1h
3
완료하는 데 5시간 필요

WEEK 3 - HYPOTHESIS TESTING

In week three, we’ll learn how to test various hypotheses - using the five different analysis methods covered in the previous week. We’ll discuss the importance of various factors and assumptions with hypothesis testing and learn to interpret our results. We will also review how to distinguish which procedure is appropriate for the research question at hand.

...
11 videos (Total 136 min), 3 readings, 2 quizzes
11개의 동영상
Testing a One Population Proportion8m
Setting Up a Test of Difference in Population Proportions7m
Testing a Difference in Population Proportions8m
Interview: P-Values, P-Hacking and More24m
One Mean: Testing about a Population Mean with Confidence17m
Testing a Population Mean Difference13m
Testing for a Difference in Population Means (for Independent Groups)12m
Demo: Name That Scenario2m
Introduction to Hypothesis Testing in Python20m
Walk-Through: Hypothesis Testing with NHANES13m
3개의 읽기 자료
Hypothesis Testing: Oher Considerations10m
The Relationship between Confidence Intervals & Hypothesis Testing5m
Additional Practice: Hypothesis Testing1m
2개 연습문제
Name That Scenario15m
Hypothesis Testing in Python Assessment1h
4
완료하는 데 4시간 필요

WEEK 4 - LEARNER APPLICATION

In the final week of this course, we will walk through several examples and case studies that illustrate applications of the inferential procedures discussed in prior weeks. Learners will see examples of well-formulated research questions related to the study designs and data sets that we have discussed thus far, and via both confidence interval estimation and formal hypothesis testing, we will formulate inferential responses to those questions.

...
6 videos (Total 77 min), 3 readings, 1 quiz
6개의 동영상
Descriptive Inference Examples for Single Variables Using Confidence Intervals12m
Descriptive Inference Examples for Single Variables Using Hypothesis Testing12m
Comparing Means for Two Independent Samples: An Example14m
Comparing Means for Two Paired Samples: An Example12m
Comparing Proportions for Two Independent Samples: An Example13m
3개의 읽기 자료
Assumptions Consistency5m
Revisiting Examples: Accounting for Complex Samples10m
Course Feedback10m
1개 연습문제
Assessment10m
4.1
23개의 리뷰Chevron Right

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Inferential Statistical Analysis with Python의 최상위 리뷰

대학: RRMar 7th 2019

If you are interested in statistics and statistical analysis, this course gets you grounded in the essential aspects of statistics. Excellent instructors.

대학: JXJun 22nd 2019

A very in-depth learning material for inferential statistics. Very good explanation of p-value which clarifies some of the prevailing misunderstandings.

강사

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Brenda Gunderson

Lecturer IV and Research Fellow
Department of Statistics
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Brady T. West

Research Associate Professor
Institute for Social Research
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Kerby Shedden

Professor
Department of Statistics

미시건 대학교 정보

The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future....

Statistics with Python 전문 분야 정보

This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research questions to the statistical and data analysis methods taught to them....
Statistics with Python

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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