#Dependent Variable

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#Dependent Variable Reel by @chithappens.co - In statistics, regression is a technique used to analyze the relationship between one dependent variable (the outcome or variable you're trying to pre
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@chithappens.co
In statistics, regression is a technique used to analyze the relationship between one dependent variable (the outcome or variable you’re trying to predict) and one or more independent variables (the predictors or factors that might influence the outcome). Regression helps us understand and quantify how changes in the independent variables are associated with changes in the dependent variable. #simplystatistics #psychology #chithappens #dissertation #research #regression #correlationdoesnotequalcausation #correlation #cuet #cuetpg #statisticsclass #psychologyfacts #psychmajor
#Dependent Variable Reel by @insightforge.ai - Linear regression is a statistical technique used to describe the relationship between a dependent variable and one or more independent variables. I
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@insightforge.ai
Linear regression is a statistical technique used to describe the relationship between a dependent variable and one or more independent variables. It works by finding the straight line that best fits the data, represented by an equation with a slope (or multiple slopes) and an intercept. To fit this line, the algorithm estimates the model parameters in a way that minimizes the gap between the actual data points and the model’s predictions. These gaps are called residuals, which represent the difference between the true values and the predicted values. A common way to measure how well the model fits is the sum of squared errors (SSE), which is the total of all squared residuals. Linear regression typically uses SSE or mean squared error (MSE) as its loss function and adjusts the parameters to minimize this value during training. By reducing SSE, the model finds the most accurate line through the data, improving its ability to make reliable predictions on new inputs. C: 3 Minute Data Science #linearregression #machinelearning #ml #datascience #math #mathematics #computerscience #programming #coding #education #visualization
#Dependent Variable Reel by @gladys.choque_ulloa - 📊 𝗣𝗿𝘂𝗲𝗯𝗮𝘀 𝗘𝘀𝘁𝗮𝗱𝗶́𝘀𝘁𝗶𝗰𝗮𝘀 𝗖𝗹𝗮𝘃𝗲 𝗲𝗻 𝗖𝗶𝗲𝗻𝗰𝗶𝗮 𝗱𝗲 𝗗𝗮𝘁𝗼𝘀: Inferencia, validación y decisiones basadas en datos 📊 E
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@gladys.choque_ulloa
📊 𝗣𝗿𝘂𝗲𝗯𝗮𝘀 𝗘𝘀𝘁𝗮𝗱𝗶́𝘀𝘁𝗶𝗰𝗮𝘀 𝗖𝗹𝗮𝘃𝗲 𝗲𝗻 𝗖𝗶𝗲𝗻𝗰𝗶𝗮 𝗱𝗲 𝗗𝗮𝘁𝗼𝘀: Inferencia, validación y decisiones basadas en datos 📊 En Ciencia de Datos no solo construimos modelos: inferimos, validamos y tomamos decisiones basadas en evidencia. Cada vez que comparamos algoritmos, evaluamos un A/B test o verificamos si una variable tiene un efecto significativo, estamos aplicando pruebas estadísticas. 1️⃣ Fundamentos: hipótesis y significancia. 🟧 H₀ (nula): No hay efecto / diferencia. 🟧 H₁ (alternativa): Existe efecto / diferencia. 🟧 α (nivel de significancia): comúnmente 0.05. 🟧 p-value: Probabilidad de observar los datos si H₀ es cierta. ⚠ Rechazar H₀ ≠ efecto grande; solo indica significancia estadística. 2️⃣ Comparación de medias. 🟧 t-test (Student): Una muestra, dos muestras independientes o pareadas. 🟧 ANOVA: Más de dos grupos; si es significativo, aplicamos post-hoc (Tukey, Bonferroni). 3️⃣ Pruebas no paramétricas. Cuando los datos no son normales o son ordinales: 🟧 Mann-Whitney U → t-test independiente. 🟧 Wilcoxon → muestras pareadas. 🟧 Kruskal-Wallis → alternativa a ANOVA. 4️⃣ Variables categóricas. 🟧 Chi-cuadrado (χ²): Evalúa asociación entre categorías. 🟧 Ej.: ¿El tipo de usuario influye en la conversión? 5️⃣ Correlación. 🟧 Pearson: lineal, continua, sensible a outliers. 🟧 Spearman: monótona, basada en rangos. 🟧 Kendall: concordancia de pares, robusta en muestras pequeñas. Se usa para selección de variables, multicolinealidad y análisis exploratorio. 6️⃣ Normalidad. 🟧 Pruebas: Shapiro-Wilk, Kolmogorov-Smirnov, Anderson-Darling. 🟧 Complementar con histogramas, Q-Q plots y análisis del impacto práctico. 7️⃣ Validación de modelos. 🟧 Comparar métricas, evaluar estabilidad, validar cross-validation. 🟧 Ej.: t-test sobre scores k-fold, Test de McNemar, Bootstrap para intervalos de confianza. 8️⃣ Más allá del p-value. 🟧 Tamaño del efecto (Cohen’s d). 🟧 Intervalos de confianza. 🟧 Análisis de potencia. 🟧 Validación cruzada. Un resultado estadísticamente significativo puede no ser relevante en la práctica. ¡Nos vemos en el siguiente post! . . #datascience #machinelearning #datoscongladys
#Dependent Variable Reel by @ahyderabadiinusa (verified account) - POV: You suddenly wake up in the middle of the night because you forgot the difference between the independent variable and the dependent variable. At
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@ahyderabadiinusa
POV: You suddenly wake up in the middle of the night because you forgot the difference between the independent variable and the dependent variable. At 2:13 a.m. your brain decides this is the perfect time to revisit research methodology, research design, statistical concepts, and data analysis from your graduate school coursework. Somewhere between literature reviews, journal articles, theoretical frameworks, and academic writing, your mind is constantly thinking about research questions, variables, and methodology. This is the real PhD life — when research concepts follow you even into your sleep. Graduate school slowly rewires your brain to think in terms of independent variables, dependent variables, data interpretation, and scholarly research in higher education. Just another night in the doctoral journey, navigating academia, university research, graduate coursework, and research life as a woman in STEM and an international PhD student. PhD life. Graduate school. Doctoral journey. Research life. Women in STEM. PhD Life | Graduate School | Women in STEM 🕊️🧕🏻 study abroad, international student, PhD journey, master’s abroad, student life in the USA, academic journey, research life, women in education, chasing dreams, growth phase, learning and growing, Keywords, becoming her, student journey, work, goals, memories, PhD life, PhD student, study, grad life, master’s abroad, study abroad life, international student journey, academic goals, growth, mindset, resilience, becoming her, dream life, that girl, studying, hard work, women in stem, university, grad school, #studystudystudy #phdlife #studyroutine #gradstudent #usa
#Dependent Variable Reel by @stewie.cs - Determinant in linear algebra #familyguy #computerscience #maths
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@stewie.cs
Determinant in linear algebra #familyguy #computerscience #maths
#Dependent Variable Reel by @findx_bysm - Determinants ki most important properties #reels #maths #love #trend #jee #physics #instagram #kota #chemistry #viral #Student #students #cbse #findx
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@findx_bysm
Determinants ki most important properties #reels #maths #love #trend #jee #physics #instagram #kota #chemistry #viral #Student #students #cbse #findxbysm
#Dependent Variable Reel by @professorgovalla - Using Nodal Analysis to Solve for Voltages and Currents Follow me on: YouTube: www.youtube.com/@ProfessorGovalla TikTok: www.tiktok.com/@professorg
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@professorgovalla
Using Nodal Analysis to Solve for Voltages and Currents Follow me on: YouTube: www.youtube.com/@ProfessorGovalla TikTok: www.tiktok.com/@professorgovalla Instagram: www.instagram.com/professorgovalla
#Dependent Variable Reel by @petal.byte (verified account) - Reviewing some fundamentals across probability and random variables today 📚 References and resources: - Deisenroth at al, "Mathematics for Machine L
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@petal.byte
Reviewing some fundamentals across probability and random variables today 📚 References and resources: - Deisenroth at al, “Mathematics for Machine Learning”, 2020 - Casella and Berger, “Statistical Inferences”, 2nd ed., 2002 - CM Biship, “Pattern Recognition and Machine Learning”, 2006 - “Lecture 12: Discrete vs. Continuous, the Uniform | Statistics 110” on Harvard University’s YouTube channel - The course I’m following: “Mathematics for Machine Learning” by MathAcademy
#Dependent Variable Reel by @learn_with_at - Key features of relational model #foryou #dbms #computerlanguages #trending #students
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@learn_with_at
Key features of relational model #foryou #dbms #computerlanguages #trending #students
#Dependent Variable Reel by @datascience.interview - Missing values is one of the most fundamental Data Science interview questions - and also one of the most misunderstood. Most candidates jump straigh
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@datascience.interview
Missing values is one of the most fundamental Data Science interview questions — and also one of the most misunderstood. Most candidates jump straight to “mean / median / mode”. That’s where they lose points. As a hiring manager, this is what I’m actually looking for 👇 ⸻ 1️⃣ First question I expect you to ask WHY is the value missing? Missing data is rarely random in real systems. It usually tells a story. Before touching any imputation technique, understand: • how the data was collected • which users / events are affected • whether missing itself carries meaning If you don’t ask this, you’re guessing. ⸻ 2️⃣ How to handle missing values (with intent) ✅ Random missing (MCAR) Example: age missing for a few users due to form skip ✔ median / mode is acceptable ✔ low bias, minimal impact ✅ Systematic missing (NOT random) Example: income missing for unpaid users ✔ keep the missing values ✔ add a missing flag ✔ let the model learn the pattern This is often strong signal, not noise. ✅ Time-based data Example: daily revenue, sensor readings ✔ forward fill / backward fill ✔ only when it makes temporal sense ✅ Categorical features Example: city, device, source ✔ create a “missing” bucket ❌ don’t invent categories ⸻ 3️⃣ When dropping rows is OK Dropping rows is NOT wrong — but only when: ✔ missing rows are very few ✔ missing is random ✔ feature is not business-critical Example: 0.5% rows missing a non-core field ⸻ 4️⃣ When dropping features is the RIGHT decision Sometimes the best fix is deletion. Drop a feature when: • 60–80%+ values are missing • no clear business meaning • adds noise or instability • hurts model generalization Keeping bad features often hurts more than removing them. ⸻ 5️⃣ Senior-level takeaway (this matters in interviews) The goal is NOT: ❌ filling all missing values ❌ maximizing data completeness The goal IS: ✅ preserving signal ✅ avoiding bias ✅ making defensible modeling decisions This mindset is what separates: Junior answers → Senior answers 📌 Save this — you’ll need it #datascienceinterview #datascientist #machinelearning #interviewprep #datasciencejobs
#Dependent Variable Reel by @bsumathdept - Some continuous distributions. #math #manim #statistics #probability #datascience #bridgewaterstateuniversity
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@bsumathdept
Some continuous distributions. #math #manim #statistics #probability #datascience #bridgewaterstateuniversity
#Dependent Variable Reel by @futurern_prep (verified account) - A student is testing the effect of 25 mg of drug X on the growth of a plant. Which of the following is the dependent variable? Remember to share your
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@futurern_prep
A student is testing the effect of 25 mg of drug X on the growth of a plant. Which of the following is the dependent variable? Remember to share your choice below! 💡 • • • #teas7 #sciencesection #sciencequestion #controlledvariables #futurern #futurernprep #nursingprep #sciencequestionoftheday #questionoftheday #teasprep

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