Regression analysis is one of the most powerful statistical techniques available to Kenyan students and researchers conducting quantitative research. Whether you are analysing the relationship between study time and academic performance, examining how income influences expenditure patterns, or investigating factors affecting small business growth in Nairobi, understanding how to run and interpret regression models in SPSS is essential. This comprehensive guide walks you through every stage of SPSS regression analysis Kenya — from preparing your dataset and checking assumptions to running the procedure, interpreting coefficients, and writing up results in APA style. By the end, you will have the confidence to conduct linear regression SPSS Kenya projects independently and to communicate your findings clearly in dissertations, theses, and journal articles.
If you are new to quantitative research, regression can seem intimidating at first. The output tables are dense, the terminology is technical, and the assumptions are strict. However, with a structured approach and a clear understanding of what each statistic means, regression becomes one of the most rewarding tools in your analytical toolkit. This guide is designed specifically for Kenyan university students — from undergraduates at the University of Nairobi to postgraduates at Kenyatta University, JKUAT, and Moi University — who need practical, step-by-step guidance on regression analysis help Kenya researchers can rely on.
1. What Is Regression Analysis?
Regression analysis is a statistical method used to examine the relationship between one dependent variable and one or more independent variables. At its core, regression answers a simple question: how much does the dependent variable change when the independent variable changes by one unit, holding all other factors constant? In linear regression SPSS Kenya applications, this relationship is modelled as a straight line, hence the name linear regression. The line represents the best fit through your data points, minimising the distance between observed and predicted values.
There are several types of regression, but the two most common in social sciences, business, and education research are simple linear regression and multiple linear regression. Simple linear regression involves one independent variable, while multiple linear regression includes two or more. Both are widely used in Kenyan academic research because they are relatively easy to implement in SPSS and produce interpretable results that examiners expect to see. If you are conducting quantitative research regression Kenya for a thesis or dissertation, regression allows you to move beyond describing relationships to quantifying their strength and direction.
The origins of regression trace back to Sir Francis Galton in the 19th century, who studied heredity and the tendency of extreme traits to regress toward the mean. Today, regression is a foundational tool in economics, psychology, education, health sciences, and business management. In Kenya, researchers use regression to analyse everything from student performance determinants to agricultural yield predictors, from hospital patient outcomes to mobile money adoption rates. Understanding SPSS regression interpretation is therefore not just an academic exercise — it is a practical skill that translates directly into evidence-based decision-making.
Before you run any regression, you must clarify your research question and identify your variables. The dependent variable is the outcome you are trying to predict or explain. The independent variables are the predictors or factors you believe influence that outcome. For example, if you are studying factors influencing undergraduate examination performance at Kenyatta University, your dependent variable might be final examination score, while your independent variables could include attendance rate, study hours per week, prior academic performance, and socioeconomic status. Getting this variable mapping right is the first and most important step in any regression analysis help Kenya project.
2. When Should You Use Regression?
Regression is not appropriate for every research question. It works best when you have a continuous dependent variable — such as exam scores, income, weight, or time — and you want to understand how changes in one or more predictors affect that outcome. It is also appropriate when you need to control for confounding variables, compare the relative influence of multiple factors, or make predictions based on historical data. If your dependent variable is categorical — such as pass/fail, employed/unemployed, or satisfied/dissatisfied — you may need logistic regression instead, which is a related but distinct technique.
Common scenarios where regression analysis is the right choice include: examining the relationship between advertising expenditure and sales revenue in a business study; investigating how class size affects student achievement in an education thesis; analysing the impact of interest rates on housing prices in an economics project; or exploring how patient satisfaction relates to waiting time in a health sciences dissertation. In each case, SPSS regression analysis Kenya provides a framework for testing hypotheses, estimating effect sizes, and drawing conclusions that go beyond simple correlation.
You should also consider regression when you need to answer questions about causality, though it is important to remember that regression alone cannot prove causation. It can show that two variables are related and quantify that relationship, but establishing causality requires experimental design, temporal precedence, and control of confounding variables. For many Kenyan students conducting observational research, regression is the strongest tool available for making causal inferences, as long as you acknowledge its limitations in your discussion chapter.
Another key consideration is sample size. Regression requires enough observations to produce stable estimates. A common rule of thumb is at least 10 to 20 observations per predictor variable. If you have three independent variables, you would ideally want at least 30 to 60 cases. For complex models with many predictors, larger samples are necessary. If your dataset is small, consider reducing the number of predictors or using simpler techniques. When in doubt, consult a supervisor or seek professional statistical support to ensure your analysis is methodologically sound.
3. Simple vs Multiple Regression
Understanding the distinction between simple and multiple regression is fundamental to designing your analysis. Simple linear regression uses a single independent variable to predict the dependent variable. The equation is straightforward: Y equals beta-zero plus beta-one times X, plus an error term. Here, Y is the dependent variable, X is the independent variable, beta-zero is the intercept, and beta-one is the slope coefficient that tells you how much Y changes for a one-unit change in X.
Multiple linear regression extends this by adding more independent variables: Y equals beta-zero plus beta-one X1 plus beta-two X2 plus beta-three X3, plus an error term. This allows you to examine the unique contribution of each predictor while controlling for the others. For example, in a study of Kenyan university student performance, multiple regression could reveal that study hours have a significant positive effect on exam scores even after controlling for prior GPA, attendance, and socioeconomic status. Without multiple regression, you might incorrectly attribute all the effect to study hours alone.
The choice between simple and multiple regression depends on your research question and theoretical framework. Simple regression is useful for initial exploration and for situations where you genuinely believe only one factor matters. However, most real-world phenomena are influenced by multiple factors simultaneously. Multiple regression is therefore the standard approach in published quantitative research and the one most Kenyan examiners expect to see. If your thesis or dissertation includes quantitative analysis, you should almost certainly use multiple regression unless you have a compelling reason to use a simpler model.
Another variant worth knowing is hierarchical regression, where you enter predictors in blocks based on theoretical priority. This is common in psychology and education research, where you might first enter demographic control variables, then add psychological predictors, and finally add interaction terms. Hierarchical regression allows you to assess how much additional variance each block explains beyond the previous one. SPSS handles hierarchical regression through the Block feature in the Linear Regression dialog box.
4. Preparing Data in SPSS
Before you can run any regression, your data must be properly prepared in SPSS. Garbage in, garbage out is a fundamental principle of data analysis, and regression is no exception. Start by entering your data into the SPSS Data View, with each row representing a case and each column representing a variable. Ensure that your variable names are meaningful and that you have assigned appropriate measurement levels: scale for continuous variables like age or income, ordinal for ranked categories, and nominal for categorical variables like gender or region.
Data cleaning is the next critical step. Check for missing values, outliers, and data entry errors. In SPSS, use the Frequencies and Descriptive Statistics procedures to scan each variable for impossible values. For example, if your variable is age and you see a value of 150, that is clearly a data entry error. Use the Missing Value Analysis procedure if you have substantial missing data, and consider whether to use listwise deletion, mean substitution, or multiple imputation depending on the pattern and amount of missingness. For SPSS data analysis Kenya projects, thorough data cleaning can take as long as the actual analysis, but it prevents misleading results.
You should also check the distribution of your dependent variable. While regression is relatively robust to mild violations of normality in the dependent variable, severe skewness or kurtosis can affect your results. Use histograms, Q-Q plots, and the Explore procedure in SPSS to assess normality. If your dependent variable is heavily skewed — for example, income data with a long right tail — consider a transformation such as logarithmic or square root transformation. Alternatively, if your dependent variable is categorical, logistic regression may be more appropriate.
Coding categorical independent variables correctly is another common source of error. If you have a categorical predictor with three or more categories, you need to create dummy variables or use SPSS automatic dummy coding. For two-category variables, ensure they are coded as 0 and 1. SPSS can handle this automatically if you specify the variable as categorical in the regression dialog. If you need guidance on variable coding and preparation, our guide on SPSS data analysis Kenya offers detailed step-by-step instructions.
5. Checking Regression Assumptions
Linear regression relies on several assumptions about your data. Violating these assumptions does not necessarily invalidate your results, but it does affect the accuracy of your standard errors, confidence intervals, and p-values. Checking assumptions is therefore a non-negotiable part of rigorous regression assumptions SPSS analysis. Below, we examine each assumption and how to test it in SPSS.
Linearity
The relationship between each independent variable and the dependent variable should be linear. You can check this by examining scatterplots of each predictor against the outcome. In SPSS, use Graphs Chart Builder to create scatterplots with a fitted linear trend line. If the points cluster around a straight line, linearity is satisfied. If the relationship is curved, you may need to add a polynomial term or transform the variable. Curvilinear relationships are common in educational and psychological data, so always visualise your data before assuming linearity.
Independence
Observations must be independent of each other. This assumption is primarily about study design. If your data includes repeated measures from the same individuals, clustered data from multiple schools, or time-series observations, the independence assumption is violated. In SPSS, the Durbin-Watson statistic tests for autocorrelation in time-series data, with values between 1.5 and 2.5 indicating independence. For clustered data, consider multilevel modelling instead of standard regression.
Normality of Residuals
While the dependent variable does not need to be perfectly normal, the residuals — the differences between observed and predicted values — should be approximately normally distributed. After running your regression in SPSS, save the unstandardised residuals and create a histogram with a normal curve. A P-P plot is also useful. Mild deviations from normality are usually acceptable, especially with larger samples, but severe skewness or outliers in the residuals can distort your results.
Multicollinearity
Multicollinearity occurs when two or more independent variables are highly correlated with each other. This makes it difficult to isolate the unique effect of each predictor and inflates standard errors. In SPSS, check the Variance Inflation Factor, or VIF, in the Coefficients table. A VIF above 5 or 10 suggests problematic multicollinearity. You can also examine the Tolerance statistic, where values below 0.1 indicate concern. To address multicollinearity, consider removing one of the correlated variables, combining them into a composite score, or using ridge regression.
Homoscedasticity
Homoscedasticity means that the variance of residuals is constant across all levels of the predicted values. Heteroscedasticity, the opposite condition, occurs when residuals fan out or cluster in a pattern. To check this in SPSS, save the standardised residuals and standardised predicted values, then create a scatterplot. If the points form a random cloud around zero, homoscedasticity is satisfied. If there is a clear pattern — such as a cone shape — heteroscedasticity is present. You can correct this using robust standard errors or weighted least squares.
Outliers and Influential Cases
Outliers are cases with unusually large or small values that can distort the regression line. Influential cases are outliers that also have a disproportionate impact on the coefficients. In SPSS, examine Cook's Distance, Leverage values, and Standardised Residuals. A common threshold for Cook's Distance is values greater than 4 divided by the sample size. Cases with high leverage and large residuals warrant closer inspection. Decide whether to retain, transform, or remove each case based on whether the outlier represents a genuine observation or a data error. Document all decisions transparently in your methodology chapter.
6. Running Regression in SPSS
Once your data is clean and your assumptions are checked, running the actual regression in SPSS is straightforward. Navigate to Analyze Regression Linear. In the dialog box, move your dependent variable into the Dependent field and your independent variables into the Independent field. If you have categorical predictors, click the Categorical button to specify dummy coding. Under the Statistics button, request Estimates, Confidence intervals, and Model fit. Under Plots, request a histogram of residuals and a scatterplot of residuals against predicted values to check normality and homoscedasticity.
If you are conducting hierarchical regression, click the Next button to create blocks. Enter your control variables in Block 1 and your main predictors in Block 2. This allows you to see how much additional variance the second block explains beyond the first. Under the Options button, set the criteria for entry and removal. For standard enter method, set both to .05. For stepwise regression, SPSS will automatically add or remove variables based on statistical criteria, though this approach is less common in academic research because it can capitalise on chance.
After clicking OK, SPSS generates several tables in the Output Viewer. The most important are the Model Summary table, which shows R, R Square, and Adjusted R Square; the ANOVA table, which tests the overall significance of the model; and the Coefficients table, which shows the unstandardised and standardised coefficients, standard errors, t-values, and significance levels. Familiarise yourself with the layout of these tables now, because interpreting them correctly is the focus of the next section. If you encounter errors or warnings during execution, review your data for missing values, categorical coding issues, or singularities caused by perfect multicollinearity.
Many students find it helpful to save the regression output as an SPSS file or export it to Word or Excel for annotation. SPSS also allows you to save predicted values, residuals, and diagnostic statistics directly into the Data View. To do this, click Save in the Linear Regression dialog and select the options you need — Predicted Values, Residuals, and statistics like Cook's Distance. These saved variables are invaluable for assumption checking and for creating publication-ready diagnostic plots. For more detailed SPSS procedures, see our guide on SPSS ANOVA analysis and related statistical techniques.
7. Understanding SPSS Output
Interpreting SPSS regression output is where many students struggle, yet it is the most critical skill for writing a strong results chapter. The output contains rich information about your model, but only if you know what each statistic means. Let us walk through the key tables and statistics you will encounter.
R and R Square
R is the correlation coefficient between the observed and predicted values of the dependent variable. It ranges from 0 to 1, with higher values indicating a stronger linear relationship. R Square, also called the coefficient of determination, represents the proportion of variance in the dependent variable explained by the independent variables. For example, an R Square of 0.65 means that 65 percent of the variation in your outcome is accounted for by your predictors. The remaining 35 percent is unexplained and may be due to other variables not in your model or random error.
Adjusted R Square
Adjusted R Square is a modified version of R Square that penalises the addition of unnecessary predictors. It is always slightly lower than R Square and is a more honest estimate of the model's explanatory power when you have multiple predictors. When comparing models with different numbers of independent variables, use Adjusted R Square rather than R Square. A model that adds a weak predictor may increase R Square slightly while decreasing Adjusted R Square, signalling that the new variable does not contribute meaningfully to the model. In SPSS output interpretation, Adjusted R Square is often the figure you report in your results chapter.
ANOVA Table and F-Statistic
The ANOVA table tests whether the regression model as a whole is statistically significant. The F-statistic compares the variance explained by your model to the variance left unexplained. A significant F-statistic — typically with a p-value less than .05 — tells you that your independent variables, taken together, predict the dependent variable better than a model with no predictors. However, the F-statistic does not tell you which individual predictors are significant. A model can have a significant F-statistic even if some of its predictors are not significant individually. For quantitative research regression Kenya projects, always report both the overall model significance and the individual predictor significance.
Coefficients Table, Beta, and Significance
The Coefficients table is the heart of your SPSS regression output. It contains two versions of each coefficient: the unstandardised coefficient, or B, and the standardised coefficient, or Beta. The unstandardised coefficient represents the raw change in the dependent variable for a one-unit change in the predictor, measured in the original units of your variables. The standardised coefficient is the same relationship expressed in standardised units, allowing you to compare the relative strength of predictors measured on different scales.
The Significance column, or Sig., shows the p-value for each coefficient. A p-value below .05 indicates that the predictor is statistically significant at the conventional level, meaning you can reject the null hypothesis that the coefficient equals zero. The Standard Error column reflects the precision of the estimate; smaller standard errors indicate more precise estimates. The 95 Percent Confidence Interval provides a range of plausible values for the true population coefficient. If the interval does not include zero, the effect is significant at the .05 level. Mastery of SPSS regression interpretation requires fluency in reading and explaining these columns.
8. How to Interpret Regression Results
Interpreting regression results is more than reporting numbers. It requires translating statistical output into meaningful statements about your research topic. Start with the overall model fit: report R Square or Adjusted R Square and explain what it means in plain language. For example, if Adjusted R Square is 0.58, you might write: “The model explains 58 percent of the variance in final examination scores, indicating that study habits, attendance, and prior performance together account for just over half of the differences observed among students.”
Next, examine the F-statistic and its p-value. A significant result confirms that the model as a whole is meaningful. Then, move to the individual coefficients. For each significant predictor, describe the direction of the relationship — positive or negative — and its practical magnitude. A positive unstandardised coefficient for study hours means that each additional hour of study is associated with an increase in exam score equal to the coefficient value. If the coefficient is 2.5, then each extra hour corresponds to a 2.5-point increase in the final examination score, on average.
Standardised Betas are useful for comparing the relative importance of predictors. A Beta of 0.45 for prior GPA and 0.20 for study hours suggests that prior academic performance has more than twice the influence of study time on the final score. However, be cautious about overinterpreting standardised coefficients when your predictors are on very different scales or when multicollinearity is present. Always interpret coefficients in the context of your research question and existing literature. For example, if prior research suggests that attendance should be the strongest predictor, but your analysis shows otherwise, discuss possible explanations in your interpretation chapter.
Non-significant predictors are also informative. A non-significant coefficient does not mean the variable has no relationship with the outcome; it means you do not have sufficient evidence to conclude that a relationship exists in the population. Consider whether the non-significance is due to small sample size, measurement error, or suppressor effects. Reporting non-significant results honestly is a hallmark of good research and is expected in APA regression reporting. Avoid the temptation to omit non-significant predictors to make your model look stronger — examiners and reviewers will notice.
9. Writing Regression Results in an Academic Report
Presenting regression results in an academic report requires a balance of detail, clarity, and adherence to formatting conventions. Most Kenyan universities require APA 7th edition formatting, which has specific guidelines for reporting statistical results. Start by presenting the overall model fit: R Square, Adjusted R Square, the F-statistic, and its p-value. Then, present a table of coefficients that includes the unstandardised coefficient, standard error, standardised Beta, t-value, and p-value for each predictor. Label the table clearly with a note explaining all abbreviations.
In the text, describe the model first, then the individual predictors. For example: “A multiple linear regression was conducted to examine the predictors of final examination scores among undergraduate students. The model was statistically significant, F(4, 195) equals 28.47, p is less than .001, and accounted for 37 percent of the variance in examination scores, Adjusted R Square equals 0.37. As shown in Table 1, prior GPA was a significant positive predictor, Beta equals 0.52, p is less than .001, as was attendance rate, Beta equals 0.31, p equals .002. Study hours per week was not a significant predictor, Beta equals 0.08, p equals .312.”
Always round p-values appropriately. Report exact p-values to three decimal places when they are above .001, and report them as p less than .001 when they are smaller. Do not report p equals .000, because SPSS can display this for very small values that are not actually zero. Also, include effect sizes. In regression, the standardised Beta coefficient and R Square serve as effect size measures. For individual predictors, you may also report partial eta squared if your supervisor prefers it. Consistency in rounding and notation is essential for professional presentation.
Create a regression table that follows APA style: horizontal lines at the top and bottom, a note beneath the table explaining variables, and aligned decimal places. If you have many predictors, consider splitting the table into two columns or using landscape orientation. Clearly indicate which coefficients are significant using asterisks: one asterisk for p less than .05, two for p less than .01, and three for p less than .001. Ensure that the table number and title appear above the table, and that the table is referenced in the text before it appears. For additional guidance on formatting and presentation, see our resource on academic editing versus proofreading, which explains how professional editors can help you polish your results chapter.
Discuss the practical significance of your findings alongside statistical significance. A predictor might be statistically significant due to a large sample size while having a tiny practical effect. Conversely, a non-significant result might still be meaningful in context. Addressing both dimensions demonstrates critical thinking and prevents your results chapter from becoming a mere list of p-values. Link your findings back to your research questions, hypotheses, and the existing literature reviewed in Chapter Two. If your results contradict previous studies, offer plausible explanations grounded in your methodology and the Kenyan context.
10. Common SPSS Regression Mistakes
Even experienced researchers make mistakes when running regression in SPSS. Awareness of common pitfalls helps you avoid them and produce more credible results. One frequent error is failing to check assumptions before interpreting the output. Students often run the regression, look at the p-values, and write up their results without examining residual plots, VIF values, or Cook's Distance. This can lead to false conclusions, especially when assumptions are severely violated.
Another common mistake is confusing correlation with causation. Just because two variables are related in a regression model does not mean one causes the other. In Kenyan social science research, many variables are correlated due to underlying confounding factors. For example, a regression showing that mobile phone ownership is related to income does not prove that phones cause higher income; the relationship could run in the opposite direction or be driven by a third variable such as education level. Always frame your conclusions carefully and acknowledge alternative explanations.
Overfitting is another risk, especially with small samples and many predictors. A model that explains 90 percent of the variance in your sample is likely capturing noise rather than signal. This phenomenon, known as overfitting, means the model will perform poorly when applied to new data. To guard against it, keep your model parsimonious, use cross-validation when possible, and prioritise theory over statistical significance when selecting predictors. A simpler model with fewer, well-justified variables is almost always preferable to a complex model with dozens of weakly related predictors.
Finally, avoid the mistake of ignoring missing data patterns. If missing values are not random — for example, if high-performing students are more likely to skip certain survey questions — listwise deletion can bias your results. Always report how you handled missing data and why. If you used mean substitution or multiple imputation, describe the method and its limitations transparently. Examiners appreciate methodological honesty and will penalise hidden or unexplained decisions. For students who need extra support navigating these technical challenges, professional SPSS analysis services can provide expert guidance and quality assurance.
11. When to Get Professional Statistical Support
Not every student needs professional help, but there are situations where expert statistical support can make the difference between a passing grade and an outstanding one. If you are struggling with data cleaning, assumption checking, or interpretation of complex output, a specialist can save you hours of frustration and ensure your analysis meets academic standards. Similarly, if your supervisor has returned your results chapter with comments about inappropriate statistical techniques, weak argumentation, or poor APA formatting, professional support can help you revise and strengthen your work.
Professional support is also valuable when you are working with advanced techniques beyond standard linear regression. Hierarchical regression, logistic regression, moderated mediation, and multilevel modelling each have specific requirements and common pitfalls. A qualified analyst can help you choose the right technique, prepare your data correctly, run the analysis, and write up the results in a way that satisfies your department's expectations. For students in business, education, and health sciences, where data analysis is a major component of the thesis, this investment often pays for itself in reduced stress and higher grades.
When selecting a provider, look for specialists with documented experience in quantitative research and SPSS. Ask about their academic background, previous projects, and understanding of Kenyan university requirements. A good analyst will ask you about your research questions, sampling method, and measurement instruments before touching your data. They will also explain their work clearly so that you can defend it during a viva or seminar presentation. Platforms like TaskLynk connect students with verified statistical analysts who understand the specific needs of SPSS data analysis Kenya projects and can deliver clean, defensible output.
Even if you ultimately run the analysis yourself, a professional review of your results chapter can catch errors you might have missed. This is similar to hiring an editor for a literature review or research proposal. Our guide on research proposal writing Kenya explains how structured expert input improves the quality of academic work at every stage. Whether you need full analysis support or a final quality check, knowing when to ask for help is a sign of academic maturity, not weakness.
Conclusion
SPSS regression analysis is a cornerstone of quantitative research in Kenyan universities. By mastering the steps outlined in this guide — defining your variables, preparing clean data, checking assumptions, running the regression, interpreting R Square and coefficients, and reporting results in APA style — you can conduct rigorous and convincing analyses that meet the standards of your department and external examiners. Whether you are working on a simple linear model for an undergraduate project or a complex multiple regression for a Master's thesis, the principles remain the same: clarity, transparency, and methodological honesty.
Remember that regression is a tool, not a magic wand. Its value depends on the quality of your data, the appropriateness of your model, and the care you take in interpretation. If you find yourself struggling at any stage, resources are available. From literature review support to thesis writing guidance and academic integrity advice, platforms like TaskLynk offer comprehensive assistance tailored to Kenyan students. You can register for an account, explore pricing, or contact our team via /contact to discuss your project. For more information about our approach, visit our about page or review our terms and privacy policy.
Frequently Asked Questions
1. What is the difference between linear and multiple regression in SPSS?
Linear regression typically refers to simple linear regression, which uses one independent variable to predict a continuous dependent variable. Multiple regression uses two or more independent variables simultaneously. In SPSS, both are run through the same Linear Regression procedure; the difference is simply the number of predictors you enter. For most Kenyan university research projects, multiple regression is preferred because it provides a more realistic picture of how multiple factors influence an outcome. If you need help choosing the right model, our SPSS analysis services include personalised consultations.
2. How do I know if my regression assumptions are violated in SPSS?
You can check assumptions using SPSS diagnostic tools. For linearity, examine scatterplots with fitted lines. For normality of residuals, save the residuals and inspect histograms and P-P plots. For multicollinearity, check the VIF values in the Coefficients table — values above 5 or 10 indicate concern. For homoscedasticity, create a scatterplot of standardised residuals against standardised predicted values. For independence, use the Durbin-Watson statistic if you have time-series data. For outliers, examine Cook's Distance and leverage values. Our guide on SPSS regression analysis Kenya provides detailed instructions for each diagnostic step.
3. Can I use regression if my data is not perfectly normal?
Yes. Linear regression is fairly robust to mild violations of normality, especially when your sample size is large — typically above 30 or 50 cases. The Central Limit Theorem ensures that coefficient estimates remain approximately normal even when residuals are not perfectly normal. However, severe skewness, heavy tails, or influential outliers can distort your standard errors and p-values. In such cases, consider transforming your dependent variable, removing or adjusting outliers, or using robust regression techniques available in SPSS. If you are unsure whether your data meets the assumptions, seek professional statistical support before finalising your analysis.
Dr. Jane Otieno
Senior Statistics Editor
Verified TaskLynk editorial contributor with years of experience in academic writing, statistical analysis, and editorial quality assurance across Kenyan and international institutions.
