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  • 13 Common Mistakes to Avoid When Doing Linear Regression Assignments

    April 18, 2023
    Gabriel Hogan
    Gabriel Hogan
    Gabriel has a Masters in Math and has been working with us for over eight years to help students excel in linear regression assignments.
    Linear regression is a way to use statistics to find out how a dependent variable is related to one or more independent variables. Linear regression analysis is often used in many fields, like economics, finance, and the social sciences, to predict trends, model relationships between variables, and make predictions. Statistics classes often give linear regression assignments, and students are expected to show that they understand the method and how to use it to solve real-world problems. But students often make mistakes when doing their linear regression homework, which can lead to wrong results and low grades. In this blog, we'll talk about 13 things you shouldn't do when doing linear regression homework.

    Not Coming Up With A Clear Research Question

    When it comes to linear regression assignments, defining the research question is one of the most important steps. It gives the whole analysis a direction and helps figure out what kind of data to collect, what type of regression to use, and what variables to add to the model. But some students don't clearly state their research question, which leads to confusion and wrong analysis.
    For example, if a research question isn't well-defined, it could lead to a regression model that doesn't answer the question or an analysis that includes variables that aren't important to the question. If you take the time to clearly define the research question and talk to a tutor or professor if you need to, you can avoid making this mistake. It's better to spend more time figuring out what the research question is than to waste time looking at data that doesn't answer the question.

    Picking Inappropriate Variables

    When doing linear regression assignments, students often make the mistake of picking the wrong variables. It's important to pick the variables you want to use in your analysis with care. The accuracy of the regression model can be hurt by variables that don't matter or that don't have a strong relationship with the dependent variable.
    When choosing variables, it is important to have a clear understanding of the research question and the variables that could affect the dependent variable. Also, it's important to think about how easy it is to measure each variable and how much data is available. Always talk to your instructor or supervisor to make sure that the variables you choose are right for your research question.

    Not Understanding The Data

    When doing homework on linear regression, one of the worst things students can do is not understand the data they are working with. Understanding the data is important because it helps you find trends, patterns, and relationships that can be used to build a regression model. Before you start a linear regression analysis, you should look at the data carefully to make sure it is complete, correct, and consistent.
    Also, it's important to find any outliers, missing data, or mistakes in the data set that could change the results of your regression analysis. You should also think about how the data is spread out, if it is skewed, and if it meets the requirements of linear regression. If you understand the data, you can make a better regression model that shows how the dependent variable and the independent variables are related.

    Not Cleaning and Processing The Data Ahead of Time

    When working on linear regression assignments, it is common for students to forget to clean and prepare the data. Real-world data is often messy and needs to be cleaned up and preprocessed before it can be used for analysis. This means getting rid of any missing values, figuring out what to do with outliers, and changing the data as needed.
    If you don't preprocess your data, you might get wrong results and come to the wrong conclusions. For example, outliers can have a big effect on regression analysis, causing the model to fit too well or not well enough. Also, if you don't transform skewed data, you might get wrong estimates of the regression coefficients.
    Before starting any analysis, it is important to clean and prepare the data well to make sure the results are accurate and reliable. This means finding any odd or outlier values in the data, making sure there are no missing values, and changing variables as needed to make sure they have a normal distribution. It could also mean getting rid of variables that don't matter or combining variables to make new ones with more meaning. When data is preprocessed in the right way, it can lead to more accurate results and a better understanding of how variables are related to each other.

    Ignoring Multicollinearity

    When doing linear regression assignments, students often make the mistake of not taking into account multicollinearity. When there is a strong link between two or more predictor variables in the model, this is called multicollinearity. This can make it hard to figure out the real relationship between the predictor variables and the response variable and make it hard to understand how to interpret the coefficients.
    Before building the model, it is important to check the correlation between the predictor variables. This will help you avoid multicollinearity. If two or more predictor variables have a lot in common, one of them should be taken out of the model. This will make sure that the remaining predictor variables don't have a lot of information in common and can give different information about the response variable.

    Failing to Check The Assumptions of Linear Regression

    Students also often make the mistake of not checking the assumptions of linear regression when they are doing linear regression assignments. Linear regression assumes that the relationship between the independent variables and the dependent variable is linear and that the errors are normally distributed with a constant variance. But if you don't check these assumptions, you might get biased and wrong results.
    When doing linear regression, it is common to check for linearity, normality of residuals, constant variance of residuals, independence of residuals, and lack of multicollinearity. You can check these assumptions with diagnostic plots like scatterplots, histograms, and residual plots.
    If you don't take these assumptions into account, the results may not be reliable, and it may be hard to draw valid conclusions from the analysis. Before figuring out what the results of linear regression mean, it is important to make sure that the assumptions are correct. If the assumptions aren't true, you may need to use different methods or change the data to make the assumptions true.

    Using the Wrong Functional Form

    In linear regression assignments, another mistake that is often made is to use the wrong functional form. The functional form is the equation that shows how the dependent variable and the independent variable relate to each other (s). Linear regression assumes that the variables are linked in a straight line, but this may not always be true. It's important to think carefully about how the variables relate to each other and choose the right functional form.
    For example, if the relationship between the variables is not linear but instead follows a quadratic or logarithmic pattern, a linear regression model would not accurately show the relationship. In these situations, it would be better to use a nonlinear regression model.
    If you use the wrong functional form, you might get biased and wrong results, so it's important to think carefully about the functional form before you do the analysis. Also, it's important to make sure that the assumptions of the chosen functional form are met.

    Not Considering Interactions Between Variables

    When doing a linear regression analysis, it's important to think about how the different factors affect each other. If you don't take into account how the variables affect each other, you could get wrong results and wrong conclusions. For instance, if there is a significant interaction between two predictor variables, the effect of one variable on the outcome may depend on the level of the other variable.
    If you don't take this interaction into account, you might make wrong predictions and misinterpret the results. So, it's important to include interaction terms in your regression model and check to see if they are important. A common mistake in linear regression assignments is not taking into account how the variables interact with each other. This is easy to avoid by carefully analyzing and interpreting the data.

    Using Too Many Variables

    When doing linear regression assignments, it's also easy to make the mistake of using too many variables. Even though it might be tempting to add as many variables as possible to your model, doing so can lead to overfitting, which is when the model is too well-suited to the training data and does poorly with new data.
    Too many variables can also cause multicollinearity, which is when two or more variables are strongly linked to each other. This can make the coefficients less reliable and make it hard to figure out what the model's results mean.
    To avoid this mistake, it is important to carefully think about the variables that are most important to the research question and to use methods like stepwise regression to choose the best variables for the model.

    Using Too Few Variables

    Another mistake that students often make when doing linear regression assignments is to use too few variables. Using too many variables can lead to overfitting and a complicated model, while using too few variables can lead to underfitting and a model that is too easy to understand.
    When choosing variables for a linear regression model, it's important to think about all of the factors that could affect the response variable. If you choose too few variables, your model might be missing important predictors and might not show how the variables relate to each other correctly.
    One way to make sure you don't use too few variables is to do a thorough exploratory data analysis and find all the variables that might be important. Also, knowledge of the field and an understanding of the research question can help guide the choice of variables.
    In a linear regression model, it is important to find a balance between using too many and too few variables. If a model is too simple or too hard to understand, it can lead to wrong results and bad conclusions.

    Not Interpreting The Results

    Another mistake that students often make is to not figure out what the results of a linear regression analysis mean. It's not enough to just run the regression analysis and look at the coefficients. It is important to know what the coefficients mean and how they relate to the research question being looked into.
    The R-squared value, which shows how well the model fits the data, is one way to figure out what the results of a linear regression analysis mean. A high R-squared value means that the model explains a lot of the variation in the data, while a low R-squared value means that the model doesn't explain much of the variation.
    When trying to figure out what the results of a linear regression analysis mean, it's also important to look at how important the coefficients are. One way to do this is to look at the p-values for each coefficient. A p-value of less than 0.05 means that the coefficient is statistically significant and that there is evidence to support the relationship between the predictor variable and the outcome variable.

    Failing To Report The Results Clearly

    One of the most important parts of linear regression assignments is how the results are written up. Many students don't report their results clearly, which can lead to confusion and wrong conclusions.
    To avoid making this mistake, it's important for your reporting to be clear and to the point. Start by giving a short summary of the results and pointing out the most important ones. Then, show the coefficients and how significant they are, along with any statistical tests that are important. Use tables and graphs to show the data in a way that is easy for people to understand.
    Also, you should talk about the results and explain what they mean in terms of your research question. Lastly, make sure to cite any sources you used to help you figure out what the results mean, and be ready to defend your conclusions if you need to. By giving a clear and thorough report of your results, you can make sure that your linear regression assignment is well-received and shows what you did well.

    Not Seeking Help When Needed

    When doing linear regression assignments, students often make the mistake of not asking for help when they need it. Linear regression is a complicated statistical method that requires a good grasp of both math and statistics. If you're having trouble with a linear regression assignment, you should ask your teacher, a tutor, or an expert in the field for help. If you don't get help when you need it, your assignment might not be done well, which could hurt your grades and overall academic performance. Also, asking for help can give you useful tips and information that can help you improve your linear regression analysis skills. Remember that it's not embarrassing to ask for help when you need it, and it can help you do better in school.


    Linear regression is a statistical tool that can tell you a lot about how two variables relate to each other. But it is also a complicated process that requires paying close attention to details and having a good grasp of statistical ideas. If you avoid the 13 common mistakes listed in this blog, you will do your linear regression assignment more accurately. Don't forget to write down your research question, choose and process your data carefully, check your assumptions, and clearly report and explain your results. If you follow these tips, you'll be able to make great linear regression assignments that will make a real difference in your field.

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