# Simple Linear Regression

This simple linear regression is a nice calculator and tool that will solve your problem really fast. It will also help you solve your problem really quickly. In this tool, you don’t have to use any formula or anything.

x

y

#### For Example

Sam found how many hours of sunshine vs how many ice creams were sold at the shop from Monday to Friday:
"x"
Hours of Sunshine
"y"
Ice Creams Sold
2 4
3 5
5 7
7 10
9 15
Let us find the best m (slope) and b (y-intercept) that suits that data
$$y = mx + b$$
Step 1: For each $$x,y$$ calculate $$x^2$$ and $$xy$$:
x y x2 xy
2 4 4 8
3 5 9 15
5 7 25 35
7 10 49 70
9 15 81 135
Step 2: Sum $$x, y, x^2\ and\ xy\ (gives\ us\ Σx,\ Σy,\ Σx^2\ and\ Σxy):$$
x y x2 xy
2 4 4 8
3 5 9 15
5 7 25 35
7 10 49 70
9 15 81 135
Σx: 26 Σy: 41 Σx2: 168 Σxy: 263
Step 3: Calculate Slope m:
m = N Σ(xy) − Σx ΣyN Σ(x2) − (Σx)2
= 5 x 263 − 26 x 415 x 168 − 262
= 1315 − 1066840 − 676
= 249164 = 1.5183...
Step 4: Calculate Intercept b:
$$b = Σy − m ΣxN$$
= 41 − 1.5183 x 265
= 0.3049...
Step 5: Assemble the equation of a line:
y = mx + b
y = 1.518x + 0.305
Let's see how it works out:
x y y = 1.518x + 0.305 error
2 4 3.34 −0.66
3 5 4.86 −0.14
5 7 7.89 0.89
7 10 10.93 0.93
9 15 13.97 −1.03
Sam hears the weather forecast which says "we expect 8 hours of sun tomorrow", so he uses the above equation to estimate that he will sell
y = 1.518 x 8 + 0.305 = 12.45 Ice Creams

## How to use this tool simple linear regression?

This simple linear regression is a nice calculator and tool that will solve your problem really fast. It will also help you solve your problem really quickly. In this tool, you don’t have to use any formula or anything. You just have to type your simple linear regression problem value and this tool will do all the things for you.

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## what is simple linear Regression?

Simple linear regression is a method to statistical obtaining formulas that can predict the value of one variable to another value. And it happens when the relationship between two value are casual.

The variable we are anticipating is known as the rule variable and is alluded to as Y. The variable we are putting together our forecasts with respect to is known as the indicator variable and is alluded to as X. When there is just a single indicator variable, the forecasting strategy is called straightforward relapse. In straightforward direct relapse, the subject of this segment, the forecasts of Y when plotted as a component of X structure a straight line.

On the off chance that we had different information credits (for example x1, x2, x3, and so on) This would be called various direct relapses. The technique for direct relapse is extraordinary and less difficult than that for various straight relapses, so it is a decent spot to begin.

In this segment, we will make a basic straight relapse model from our preparation information, at that point make forecasts for our preparation information to get a thought of how well the model took in the relationship in the information.

With straightforward direct relapse we need to show our information as follows:

y = B0 + B1 * x

This is where y is the yield variable we need to foresee, x is the information variable we know and B0 and B1 are coefficients that we need to assess that move the line around. In fact, B0 is known as the capture since it figures out where the line blocks the y-pivot. In AI we can call this the inclination since it is added to balance all forecasts that we make. The B1 expression is known as the incline since it characterizes the slant of the line or how x converts into a y esteem before we add our predisposition.

The objective is to locate the best gauges for the coefficients to limit the blunders in foreseeing y from x.

Straightforward relapse is extraordinary, in light of the fact that instead of looking for values by experimentation or compute them systematically utilizing further developed direct variable based math, we can gauge them straightforwardly from our information.

We can get going by assessing the incentive for B1 as:

B1 = sum((xi-mean(x)) * (yi-mean(y)))/sum((xi – mean(x))^2)

Where mean() is the normal incentive for the variable in our data set. The xi and yi allude to the way that we need to rehash these computations across all qualities in our data set and I allude to the i'th estimation of x or y.

We can compute B0 utilizing B1 and a few insights from our data set, as follows:

B0 = mean(y) – B1 * mean(x)

## How to use this tool simple linear regression?

To use this tool you don’t need to worry about so many things because it's a very simple calculator that anyone can understand and use this tool. And as you know this tool is a free and web-based tool that will help you solve your problem and this tool will also help you do it really quickly.

Now you just have to follow some very simple steps and then you are good to go.

As you can see in this tool you have given some text boxes to enter a value here

so enter your value in here and then you have to also double-check the value in here so that you won't make any mistake.

After entering your value here you just have to simply click on the calculate button which will show you the result of your problem.

You can also bookmark this tool so that you don’t have to worry about the problem and you don’t have to worry about searching for it again and again.

### Q. What Is Simple Linear Regression?

A. This Simple Linear Regression Is A Nice Calculator And Tool That Will Solve Your Problem Really Fast