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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
An Introduction to Statistical Inference and Its Applications with R
If you only want to read and view the course content, you can audit the course for free. More questions? Visit the Learner Help Center. Browse Chevron Right. Data Science Chevron Right.
STAT-S 350 INTRODUCTION TO STATISTICAL INFERENCE (3 CR.)
Probability and Statistics. Statistical Inference.
Offered By. About this Course 82, recent views. Course 6 of 10 in the Data Science Specialization. Flexible deadlines. Flexible deadlines Reset deadlines in accordance to your schedule. Hours to complete. Available languages. English Subtitles: English, Vietnamese. What you will learn Check Describe variability, distributions, limits, and confidence intervals. Check Make informed data analysis decisions. Check Understand the process of drawing conclusions about populations or scientific truths from data. Check Use p-values, confidence intervals, and permutation tests. Chevron Left.
Kiefer's Introduction to statistical inference
Syllabus - What you will learn from this course. Video 10 videos. Introductory video 7m. Reading 11 readings. Welcome to Statistical Inference 10m. Some introductory comments 10m. Pre-Course Survey 10m. Syllabus 10m.
Data Science Specialization Community Site 10m. Homework Problems 10m. Probability 10m.
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Conditional probability 10m. Expected values 10m.
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Practical R Exercises in swirl 1 10m. Quiz 1 practice exercise. Quiz 1 12m. Reading 4 readings.
Introduction to Statistical Inference | The Economic Journal | Oxford Academic
Variability 10m. Distributions 10m. Asymptotics 10m.
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The class consists of two weekly lectures and a lab session. The lectures introduce statistical concepts and theory and the lab session applies those lessons using the statistical software R.
Algebra is the only prerequisite for the course. Data: It's Everywhere! Quantitative Resources Resources to help you get going. By the end of the course, students should be able to: Understand the effect of study design on interpretation of results Identify appropriate statistical methods when presented with new data Read and interpret basic statistical literature of various sources, from newspaper articles to academic journals Use R as a tool to perform statistical analysis The class consists of two weekly lectures and a lab session.
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