Why are we talking about Stats?
(I’ll be leaving out many concepts since the purpose of this post is simply to highlight the importance of statistics.
We can’t fit everything into one page, this site is just an overview)
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Statistics is essential for making data-driven decisions, understanding concepts like probability, regression, identifying outliers, handling missing data, and recognizing biases helps us to apply insights to real-world problems and improve decision-making in any field.​
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Descriptive Statistics:
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To make decisions, you first need to explore and summarize your data.
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Measures of Central Tendency:
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Mean: Sum of all values divided by the number of values.
Median: Middle value in an ordered dataset.
Mode: Most frequent value.
Measures of Dispersion:
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Range: Difference between highest and lowest values.
Variance & Standard Deviation: How much data deviates from the mean.
Data Visualization:
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Histograms, Box Plots, Scatter Plots to show distributions, outliers, and relationships.
Identifying Outliers:
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Outliers are extreme values that differ significantly from other data points. Identifying them helps in decision-making, as they might indicate errors or rare but valuable occurrences. Methods for detecting outliers include visual tools like box plots and statistical methods like the IQR (Interquartile Range).
Inferential Statistics:
Making predictions & testing assumptions, once we understand your data, the next step is making predictions and testing assumptions.
Sampling: Random and stratified sampling ensure unbiased and accurate representataion.
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Confidence Intervals: Estimating the range of a value. Example: "We are 95% confident that customer satisfaction is between 4.2 and 4.8."
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Hypothesis Testing: Helps test assumptions.
Probability Distributions:
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Predicting outcomes, distributions help model real-world scenarios.
Normal Distribution:
Common in natural data. Example: Heights, test scores.
Binomial Distribution:
Models yes/no outcomes. Example: The probability of a customer buying a product.
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Poisson Distribution: Counts rare or specific events.
Correlation & Regression:
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Essential for predicting trends and understanding data relationships.
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​​​Correlation:
Strength of relationship between two variables.
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Linear Regression:
Predicts one variable from another.
Business Applications of Statistics:
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Quality Control
A/B Testing
Risk Assessment
Customer Insights
Important to know​
Logic in Data Analysis:
Understanding logical operations
Ensuring that conclusions drawn are logically consistent with the data
Bias in Data
Selection Bias: Occurs when the sample isn't representative of the population.
Confirmation Bias: The tendency to focus on data that supports pre-existing beliefs.
Identifying and correcting for bias is critical to avoid skewed results and flawed conclusions.
Handling Missing Data:
Imputation: Filling in missing data points based on the existing data
Dropping data points or variables with too much missing data.