Udemy - Learn Hypothesis Testing With Python

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Free Download Udemy - Learn Hypothesis Testing With Python
Published: 3/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.93 GB | Duration: 7h 57m
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What you'll learn
Learn avout visualisations, how to code them in Python, and how they enhance the presentation of your work.
Learn anout the measure of central tendency, which is composed of the mean, median, and mode of a dataset.
Learn about the measure of dispersion, which calculates the spread and standard deviation of a dataset.
Learn about the various measures of association and some of the statistical tests that it is comprised of.
Learn about probability theory and how it is an integral part of hypothesis testing.
Learn about probability distributions and four popular distributions that are used in hypothesis testing.
Learn about the central limit theorem, whict is integral to the study of statistics.
Learn about confidence intervals, which are an important concept in hypothesis testing.
Learn about hypothesis testing and how to perform them.
Learn about difference of means tests and how to perform them.
Requirements
The learner should have a basic understanding of statistics.
The learner should have a basic understanding on the Python programming language.
Description
In today's data-driven world, learning hypothesis testing is essential for several reasons, to include:1. Informed Decision-Making: Hypothesis testing helps individuals make decisions based on data rather than intuition or guesswork. Whether it's in business, healthcare, education, or everyday life, making decisions backed by statistical evidence ensures more accurate and reliable outcomes.2. Critical Thinking: Understanding hypothesis testing fosters critical thinking skills. It encourages individuals to question assumptions, analyze data rigorously, and draw conclusions based on empirical evidence. This skill is valuable in evaluating the credibility of information and avoiding biases.3. Professional Advantage: Many professions, such as data analysis, scientific research, marketing, and finance, require a solid understanding of hypothesis testing. Mastering this skill can enhance career prospects and open doors to opportunities in fields that rely on data analysis and evidence-based decision-making.4. Enhanced Research Skills: Hypothesis testing is a fundamental aspect of scientific research. By learning how to formulate and test hypotheses, individuals can contribute to advancing knowledge in various domains, from medicine to social sciences. It also enables them to critically assess research studies and their findings.5. Policy and Program Evaluation: Hypothesis testing is crucial for evaluating the effectiveness of policies, programs, and interventions. Governments and organizations use it to determine whether initiatives are producing the desired outcomes and to make data-informed decisions for improvements.6. Empowerment in Daily Life: Understanding hypothesis testing empowers individuals to interpret data presented in news, reports, and studies. It helps them make informed choices about personal health, finances, and other aspects of life by discerning valid conclusions from misleading claims.7. Technological Integration: With the rise of big data and artificial intelligence, hypothesis testing has become even more relevant. It forms the backbone of machine learning models and algorithms, enabling the extraction of meaningful insights from vast datasets.8. Reduction of Misinformation: In an era of information overload, knowing hypothesis testing helps combat misinformation. It equips individuals with the tools to critically evaluate the validity of claims and distinguish between scientifically sound information and pseudoscience.In summary, learning hypothesis testing equips individuals with the skills needed to navigate a complex and data-rich world. It promotes informed decision-making, critical thinking, professional development, and a deeper understanding of the world around us.in this course the student will learn how to conduct several hypothesis testing scenerious using the general purpose language, Python. the student will learn about:-1. Visualisation techniques that are important in statistical research, with a special emphasis on hypothesis testing.2. Specific staistical measurements that are important when carrying out a hypothesis test.3. the theory of probability and distribution, with a special emphasis ob the distributions that are used in hypothesis testing.4. the student will learn the Python code of a multitude of practice problems in probability, confidence intervals,hypothesis testing, and difference in means testing.
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: Charts
Lecture 2 charts
Lecture 3 line chart
Lecture 4 bar chart
Lecture 5 pie chart
Lecture 6 scatter plot
Lecture 7 box plot
Lecture 8 histogram
Lecture 9 QQ plot
Section 3: Measure of central tendency
Lecture 10 measure of central tendency
Section 4: Measure of dispersion
Lecture 11 measure of dispersion
Section 5: Measure of association
Lecture 12 measure of association
Lecture 13 pearson correlation coefficient
Lecture 14 spearman rank coefficient
Lecture 15 chi2 test of independence
Lecture 16 cramers v
Lecture 17 odds ratio
Lecture 18 linear regression
Lecture 19 contingency coefficient
Lecture 20 special considerations
Lecture 21 logistic regression
Section 6: Probability theory
Lecture 22 probability theory
Section 7: distributions theory
Lecture 23 distribution theory
Lecture 24 symmetrical distribution
Lecture 25 left skewed distribution
Lecture 26 right skewed distribution
Section 8: Probability distributions
Lecture 27 probability distributions
Lecture 28 normal distribution
Lecture 29 binomial distribution
Lecture 30 poisson distribution
Lecture 31 t distribution
Lecture 32 summary of distributions
Section 9: Central limit theorem
Lecture 33 central limit theorem
Section 10: Practice problems using the normal distribution
Lecture 34 loaves of bread
Lecture 35 test scores
Lecture 36 heights
Lecture 37 male heights
Lecture 38 manufacturing
Lecture 39 sandwiches
Section 11: Practice problems using the binomial distribution
Lecture 40 dice rolls
Lecture 41 tax returns
Lecture 42 light bulbs
Lecture 43 sports
Lecture 44 customer service
Lecture 45 pass or fail
Section 12: Practice problems using the Poisson distribution
Lecture 46 convenience store
Lecture 47 coffee shop
Lecture 48 defective parts
Lecture 49 traffic accidents
Lecture 50 help desk
Lecture 51 hotel bookings
Section 13: Practice poblems with the t distribution
Lecture 52 test scores
Lecture 53 researcher
Lecture 54 drug trials
Lecture 55 diet
Lecture 56 machines
Section 14: Confidence intervals
Lecture 57 confidence intervals
Lecture 58 house prices and sales
Lecture 59 ceo management succession plan
Lecture 60 defective batteries
Lecture 61 political pollster
Lecture 62 teaching methods
Section 15: Hypothesis tests
Lecture 63 hypothesis testing
Lecture 64 bottles
Lecture 65 miles per gallon
Lecture 66 batteries
Lecture 67 software
Lecture 68 drugs
Lecture 69 men's mba ages
Lecture 70 tea or coffee?
Lecture 71 masks
Lecture 72 lunch
Section 16: Difference in means tests
Lecture 73 difference in means tests
Lecture 74 olympian heights
Lecture 75 male and female heights
Lecture 76 company salaries
Lecture 77 blood pressure
Lecture 78 ages of men and women mba students
Section 17: End of course
Lecture 79 Congratulations for completing the course
This course is intended for researchers who would like to know how to perform hypothesis tests.,This course is intended for students would would like to learn more about statistics.,This course is intended for Python programmers wou would like to know more about the statistical and scientific libraries that can be used with the language.


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