Questionnaire design for linear regression I am trying to conduct a research on brand preferences and I am trying to analyze regression equation i.e. which factors affects the consumer preferences (dependent factor) and other determined factors (independent variable). So my question is how to design a questionnaire to calculate the linear regression among these factors
 A: As (standard) linear regression requires a quantitative dependent variable and quantitative explanatory variables, linear regression is not the tool you search.
Your dependent variable has a nominal scale and the explanatory variables have (as it is typical for questionaires) ordinal or nominal scale, you want to look for log-linear models.
A: I think you would probably want to plot the distribution of your response variable prior to making any decisions on modeling. 
Often times it helps to see how your data is distributed, prior to assuming a particular model choice. 
That being said if you're using R, you can use packages like DHARMa to assess whether your model choice is appropriate: https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html
A: It's probably not a good idea to "design a survey in a way that allows me to run a linear regression model on the results." You want to design your questionnaire to capture the constructs you are interested in the best way possible. Once you have done that, you then want to choose the best statistical tool for the job. It might end up being linear regression or it might not.
To be more specific:
If you are interested in what factors drive brand preference, controlling for other factors, then your questionnaire needs to include questions that do a "good job" of measuring each of those things. What "good job" means depends on the specifics of what "construct" you are trying to measure: if you think that being an introvert is a predictor of preference then you need one or more questions that you can be confident actually measure whether a person is an introvert (and just asking "are you an introvert?" is probably NOT a good solution). You should make all these decisions independently of what kind of statistical model you will eventually run. When in doubt steal questions from pre-existing surveys on similar topics that have already been validated as actually measuring the things they say they are.
Once you have developed a good set of questions, then you can ask the question of what sort of statistical model would best be suited to analyzing it. A linear regression is appropriate when your dependent variable is continuous, not censored and has a distribution that isn't massively skewed one way or the other, etc. But if you decide that the best way to analyze brand preference is to ask people "which brand do you like best: A, B, C or D?" then that variables can NOT be analyzed with a linear regression model, because it is a nominal categorical variable. You would need to use some other method (like a multinomial logit model) to analyze it. It also might be that you don't end up wanting to use a regression analysis at all.
In short: get good data first, then decide how to analyze it.
