How to Test Linearity in SPSS – Complete Step-by-Step Guide #statistics #ethiopia #fyp

🔍 Is your regression model actually valid? Before you look at any p-values or R-squared, you MUST test the linearity assumption! In this video, I walk you through a complete step-by-step guide to testing the linearity assumption in SPSS using a real-world example. We are predicting Customer Satisfaction using Transaction Speed and Mobile Banking as our independent variables. If you skip this step, your entire regression model could be completely unreliable. Watch this short tutorial to make sure your data is truly linear! ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⏱️ TIMESTAMPS (Video Length: 2:36) 0:00 – Introduction – Why Linearity Matters 0:30 – Step-by-Step: How to Get the Scatterplot in SPSS 1:45 – How to Interpret the Scatterplot (With Real Data) 2:15 – Quick Recap & Conclusion ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📌 In This Video, You Will Learn: ✅ What the linearity assumption actually means in regression ✅ Exactly how to generate the scatterplot in SPSS (Analyze → Regression → Linear → Plots) ✅ How to set ZRESID as the Y-axis and ZPRED as the X-axis ✅ How to read the scatterplot to check for non-linear patterns (U-shapes, curves, or bends) ✅ Whether our Customer Satisfaction data passes the linearity test ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 Our Data Example Dependent Variable: Customer Satisfaction Independent Variables: Transaction Speed, Mobile Banking Sample Size (N): 348 cases ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🛠️ SPSS Menu Path Summarized 1. Analyze → Regression → Linear 2. Put Customer_Satisfaction in the Dependent box 3. Put Transaction_Speed and Mobile_Banking in the Independent(s) box 4. Click Plots 5. Set Y-axis = ZRESID (Standardized Residuals) 6. Set X-axis = ZPRED (Standardized Predicted Values) 7. Click Add → Continue → OK ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ✅ Key Interpretation Rule Linearity PASSED: Data points are randomly scattered above and below the zero line with no clear curve (no U-shape, inverted U-shape, or bending). Linearity FAILED: Data points show a systematic curved pattern, meaning the relationship is non-linear. Our Result: The scatterplot showed a random cloud with no obvious curve. The linearity assumption is satisfied for our model. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔔 Stay Tuned for More! This is just the first step! In future videos, I will cover the remaining assumptions: · Normality of Residuals · Homoscedasticity (Equal Variance) · Independence (Durbin-Watson) · No Multicollinearity (VIF) Make sure to subscribe so you don't miss them! ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💬 Have Questions? Drop a comment below with your own SPSS output questions! I read all comments and try to help where I can. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📢 Connect & Support 👍 Like this video if it helped you 🔔 Subscribe for more SPSS tutorials 💬 Comment with your own regression questions ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🏷️ HASHTAGS #SPSS #LinearRegression #Linearity #DataAnalysis #Statistics #CustomerSatisfaction #SPSSRegression #ResearchMethods #DataScience #AssumptionTesting #YouTubeTutorial