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Grokking Modern System Design Interview for Engineers & Managers

Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.

In a linear function, **simple linear regression** defines the connection between an independent variable x and a dependent variable y.

```
y = slope * x + intercept
```

Note: Refer to A deep dive into linear regression to learn more about linear regression.

The ** linear_regression() method** is used to get the

`slope`

and `intercept`

of a simple linear regression given the values `x`

and `y`

using the ordinary least squares estimation method. This method was introduced in Python version 3.10.The inputs `x`

and `y`

should be the same length and should at least have two data points.

```
linear_regression(x, y)
```

: This is the independent variable.`x`

: This is the dependent variable.`y`

The method returns the `slope`

and `intercept`

.

Let’s look at the code below:

import statisticsx = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]y = [11 ,13, 12, 15, 17, 18, 18, 19, 20, 22]slope, intercept = statistics.linear_regression(x, y)print("Slope - ", slope)print("Intercept - ", intercept)

**Line 1**: We import the`statistics`

module.**Line 3**: We define the independent variable`x`

.**Line 4**: We define the dependent variable`y`

.**Line 5**: We obtain the`slope`

and`intercept`

of`x`

and`y`

using the`linear_regression()`

method.**Lines 8 and 9**: We print the`slope`

and`intercept`

value.

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abhilash

Grokking Modern System Design Interview for Engineers & Managers

Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.

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