About lineer regression

I have a project. And within this project, i make basic lineer regression analysis. My response variable is preference between Apple and Huawei; explanatory variable is people's age who makes a choice.
But there is a problem. One of the variables of data is not numerical(It's character: Apple and Huawei). How can I make the lineer regression ?

Linear regression is designed for response variables that are continuous, they can take on a large, if not infinite, number of values. Binary response variables, either/or can only take one one of two, coded as 0/1.

Logistic regression is designed for discrete variables. The most common type, and the one you are using is binomial. The R syntax to deal with this class of problem is

fit <- glm(choice  ~  age, type = 'binomial')

You get somewhat different diagnostics. High p-values will tell you right away if there is a lack of association, but low p-values require goodness-of-fit analysis.

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Firstly, thanks for your help. But it doesn't work.
Now i'm gonna try to explain the probem by using some screenshots.

This is my dataset.
Here, i tried to make a lineer model.
And it has failed. It says "Tercih"(preference) not found. I wonder, one of the variables of data is not numerical and that's why it has failed ? If the reason is this, what can i do ? How can i solve the problem ?
And additionally, as first i changed the names of the response variable. (For Apple:0 for Huawei :1) then when i try to make regression analysis in R Commander, i got some values (like p-value, R squared,....). Are these values are reliable ? I'm loading those screenshots.

My blunder; I didn't include the name of the data frame in the argument to glm

fit <- glm(data = veritchihyas, Tercih ~ Yas., family = binomial)

(and I shouldn't have quoted the argument to family.

What the lm model is telling you is that age is a slightly worse predictor than flipping a coin.

Thank you so much for your help. But i suppose i won't be able to get the correct outputs. Because as you see : plot
The spreader plot is this. And correspondingly i cannot make the lineer regression(abline of this plot cannot be draw :pensive: ).

Yes, this is exactly what you see with linear regression applied to binary data. I put my notes from the first chapter of Applied Logistic Regression by Hosmer, Lameshow and Sturdivant at https://s3-us-west-2.amazonaws.com/tuva/PlotLogisticData.pdf Don't worry about the equations. Look at the data tables. The upper plot show linear regression and the lower plot shows logistic regression after binning the data.

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Thanks for your help!

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