Learn Hypothesis Testing With Python

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.