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    Learn Hypothesis Testing With Python

    Posted By: ELK1nG
    Learn Hypothesis Testing With Python

    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

    to succeed in your career

    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.