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Free Certificate
Basics of Inferential Statistics
Master data-driven decision-making with this inferential statistics free course. Learn probability, distributions, and sampling techniques to draw accurate conclusions from random data samples.
15 hours of learning
Probability
Ad Copywriting
Stat Inference
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Key Highlights Of This Free Inferential Statistics Course
What You Will Learn
Basics of Probability
In this session, you will be introduced to fundamental probability concepts that are essential for understanding data analysis. You’ll learn how to quantify event outcomes using random variables, find their probability distributions, and calculate expected values. This foundation will set you up for more advanced concepts in inferential statistics.
Topics Covered
- Random Variables: Random variables represent outcomes of random processes, and they can be discrete (taking on specific values) or continuous (able to take any value within a range). You’ll learn how to model and manipulate these variables to calculate probabilities.
- Probability Distributions: A probability distribution describes how probabilities are assigned to different outcomes. You'll explore various distributions such as uniform, binomial, and normal, learning how they define the likelihood of different events.
- Expected Value: The expected value of a random variable is its long-term average, or mean, calculated over many trials. You'll learn how to calculate this using the distribution of the random variable, providing a crucial insight into the average outcome of a random process.
Discrete Probability Distributions
This session focuses on discrete probability distributions, specifically exploring the binomial distribution and uniform distribution. You’ll also be introduced to cumulative probability, a key concept that leads to understanding continuous distributions in later sessions.
Topics Covered
- Probability without Experiment: Learn how to calculate probabilities based on theoretical foundations, without the need for conducting actual experiments. This approach is essential for understanding how probabilities are derived in a controlled setup.
- Binomial Distribution: This distribution models situations where there are two possible outcomes (success or failure) over a fixed number of trials. You’ll learn how to compute probabilities for events like the number of successes in a set of trials.
- Cumulative Probability: This concept helps calculate the probability that a random variable will take a value less than or equal to a particular point. You’ll learn how cumulative probability functions allow for understanding broader outcomes beyond single-point probabilities.
- Comprehension: Expected Value: Building on the previous topic, you’ll dive deeper into how expected values are calculated for discrete distributions and what they reveal about the central tendency of the data.
Continuous Probability Distributions
This session introduces continuous probability distributions and contrasts them with discrete distributions. You’ll learn about probability density functions (PDFs) and the normal distribution, an essential concept for continuous data analysis.
Topics Covered
- Probability Density Functions (PDFs): PDFs describe the likelihood of continuous outcomes within a given range. Unlike discrete distributions, PDFs deal with intervals rather than specific points. You’ll understand how areas under the PDF curve represent probabilities.
- Normal Distribution: The normal distribution is a bell-shaped curve that represents the distribution of many types of data in nature and human activity. This session explains the key characteristics of the normal distribution, such as symmetry and the empirical rule (68-95-99.7).
- Standard Normal Distribution: This is a special case of the normal distribution with a mean of 0 and a standard deviation of 1. You’ll learn how the standard normal distribution is used in hypothesis testing and the Z-score, a standardization tool used to compare data from different normal distributions.
Central Limit Theorem
This critical session explores the central limit theorem (CLT), which forms the foundation of many statistical methods. You'll learn how the distribution of sample means approaches a normal distribution as sample sizes increase, regardless of the original data distribution.
Topics Covered
- Samples: A sample is a subset of a population that is used for statistical analysis. You’ll learn about the importance of sampling in statistics and how it enables you to draw inferences about large populations.
- Sampling Distributions: The sampling distribution is the distribution of sample statistics (such as the sample mean) across many samples from the population. You’ll explore how sampling distributions behave and their key properties, such as their mean and variance.
- Properties of Sampling Distributions: The sampling distribution has specific properties, including the fact that the mean of the sample means equals the population mean. You'll learn how to calculate the standard error and understand its role in statistical inference.
- Estimating Mean using CLT: The central limit theorem is used to estimate the population mean by analyzing sample means. You’ll learn how increasing sample size leads to more accurate estimates of the population mean.
- Confidence Interval: A confidence interval is a range of values used to estimate an unknown population parameter. You’ll explore how to calculate and interpret confidence intervals for sample means using the standard error.
Applications of Sampling Methods
In this session, you’ll learn about the practical applications of different sampling methods. Understanding sampling is crucial because incorrect sampling techniques can lead to misleading conclusions, even with correct statistical analysis.
Topics Covered
- Types of Sampling Methods: Study the various types of sampling methods, including random sampling, stratified sampling, and systematic sampling. You’ll learn how each method ensures that the sample is representative of the population and how to choose the best method for different situations.
- Uses in Market Research: Sampling is widely used in market research to gather insights about consumer behavior, preferences, and trends. You’ll see how to apply sampling techniques to survey populations effectively.
- Uses in Marketing Campaigns: Companies use sampling methods to test and refine marketing strategies before large-scale implementation. You’ll learn how sampling helps businesses make data-driven decisions in their marketing efforts.
- Uses in Pilot Testing: In product development, pilot testing involves using samples to test a product, service, or process before full-scale launch. You’ll learn how sampling methods help mitigate risks and improve outcomes during testing phases.
- Uses in Quality Control: In manufacturing and production, sampling is used to assess the quality of products. You’ll explore how quality control processes rely on sampling methods to identify defects and ensure product consistency.
Inferential Statistics Course Certification
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Receive a signed and verifiable e-certificate from upGrad upon successfully completing the course.
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Master Skills in Inferential Statistics to Boost Your Career and Analytical Skills
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