what is difference between a data scientist and statistician? A statistician can make an inference with 5 data points to prove Jessen's point, the more you buy the more you save. Very simple DD for NVDA. now they only worry is the the linear project will be capped.
https://preview.redd.it/oiluumyeu6gg1.png?width=694&format=png&auto=webp&s=5fb4ad52b9a2085a98428caa24968e0ba19202d6
This image compares key performance and investment metrics for the "Big Five" tech companies. Below is the data converted into a table, followed by a linear regression analysis.
# Tech Performance Data Table
|**Rank**|**Company**|**Capex/Rev % (x)**|**Model Rank**|**Adoption Rank**|**Rev Growth (y)**|
|:-|:-|:-|:-|:-|:-|
|1|Alphabet|23%|1|3|\+16.2%|
|2|Meta|36%|7|1|\+26.2%|
|3|Microsoft|27%|6|5|\+18.4%|
|4|Amazon|17%|5|4|\+13.4%|
|5|Apple|3%|8|6|\+7.9%|
# Linear Regression Analysis
To find the relationship where **Revenue Growth ($y$)** is a function of **Capex/Revenue % ($x$)**, we use the linear model:
$$y = \\beta\_0 + \\beta\_1x$$
# 1. Calculation Parameters
Using the data points provided:
* **Mean $x$ ($\\bar{x}$):** 21.2%
* **Mean $y$ ($\\bar{y}$):** 16.42%
# 2. The Model
After calculating the least squares fit, the resulting equation is approximately:
$$y = 0.548x + 4.80$$
# 3. Interpretation
* **Slope ($\\beta\_1 \\approx 0.55$):** For every **1% increase** in Capex as a percentage of revenue, the model predicts an approximate **0.55% increase** in Revenue Growth.
* **Intercept ($\\beta\_0 \\approx 4.80$):** This suggests a theoretical "baseline" growth of 4.8% if Capex were zero (though this is purely mathematical, as Apple demonstrates a low Capex still yields significant growth).
* **Correlation:** There is a **strong positive correlation** between these two variables in this specific dataset. Meta's high Capex/High Growth and Apple's low Capex/Low Growth anchor the ends of this trend line.
Would you like me to calculate the **R-squared** value to see how well this line actually fits the data points, or perhaps run a regression using the **Adoption Rank** instead?