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In this video, we are going to run a simple linear regression, that is we will use only one predictor

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variable to predict the value of price, the predictor variable.

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We are going to use is room number, that is number of rooms in the house and using the value of room

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number.

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We will try to establish a relationship between room number and price.

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We did this earlier also where we created this trend line.

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And so the equation and the R-squared against this trend line.

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Now we will run the proper regression analysis and see its results.

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So we will go to data analysis options here.

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We will select the regression option and click on talk.

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The input lighting is the response variable, which is in the column to select this whole column.

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The input X range is our room number column.

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Our data has labeled that as the first rule of the column contains the variable name.

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Next option is where we want our output.

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We want an output and a new sheet.

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And we do not need to look at these attitudes for not.

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So we'll just click on OK?

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And we get the result of linear regression between these two variables and this new shit, you can see

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that the Oscar value is coming out two point forty for it.

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This is the same as.

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The one we got when we plotted this train line.

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The values is in this column, so this is Bedazzle against InterCivic, and this particular cell is

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containing beta one value, which is the coalition of room no evil.

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So a value of nine point zero nine is telling us that if I increase the room number, they will buy

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one unit, it will increase the house price by nine point zero nine units.

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So using these to be the values we can estimate, the equation between Price and Ruman will understand

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the meanings of all these other parameters in the coming reduce.