Unlock the Power of #XLSTAT 2024.3 The new XLSTAT 2024.3 release brings groundbreaking features to streamline your team’s statistical analysis and help you deliver insights faster and more effectively. Whether you run complex regressions or need clearer visuals for complex results, this version has you covered. Key features: • MaxDiff Analysis – 150x Faster No more waiting for results! MaxDiff analysis now delivers real-time insights, even with large datasets. The new ErrVarNorm index helps filter out atypical respondents, ensuring more reliable results for your team. • Funnel Chart Visualisation Quickly create funnel charts to visualize processes like sales pipelines. Seamlessly pinpoint bottlenecks and successes with this new feature, making your data flow more comprehensible. • Enhanced Data Management Simplify data handling with more flexible join options and column selection. Merging complex datasets is now faster, allowing your team to focus on analysis, not data wrangling. • Scatter Plot Enhancements Incorporate trend lines that reveal linear or complex relationships, helping you and your team quickly identify patterns and trends across multiple groups. • RATA Feature Improvements Build clearer clusters of assessors using advanced algorithms that automatically group people with similar preferences, perfect for understanding consumer data. With XLSTAT, your team generates insights faster, visualises data more effectively, and manages complex datasets smoothly. Try it now and see how it can transform your analysis processes. https://lnkd.in/d83RhRtj
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3rd Year Data Enthusiast | Data Analyst | SQL, Python, Power BI & Excel | Passionate About Descriptive Statistics & Supervised ML
𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝘄𝗶𝘁𝗵 𝗢𝘂𝘁𝗹𝗶𝗲𝗿 𝗥𝗲𝗺𝗼𝘃𝗮𝗹 ! Ever wondered how we turn messy data into meaningful insights? One key step is removing outliers—those odd data points that don’t fit in. Recently, I worked on Normal Distribution and used two simple techniques: 1️⃣ Standard Deviation (STD): To spot values far from the average. 2️⃣ Z-Score: To measure how far a value is from the average in standard deviations. By removing outliers, the data became cleaner and formed the classic bell curve, making analysis accurate and insightful. It’s like clearing the fog to see the bigger picture! 🌟 #DataAnalysis #NormalDistribution #Outliers #ZScore #DataCleaning #LearningJourney
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📊Exploring Machine Status: Turning Data into Insights! Machine status dashboards provide a broad and integrated view of machines and are the basis for rating performance and #productivity. When studied dynamically, they support #data cross-referencing processes, which result in several productive analyses. 🔗Learn more in our Blog https://bit.ly/3VabAuR and #explore new possibilities for your industry! #ST1 #UnlockResults
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SMAR AnalyticsView solutions drive improvement in productivity, efficiency, quality, and sustainability. The solutions can be applied to solve common business intelligence (BI) challenges, enabling users to move rapidly and easily from data to information, without help from IT or from data scientists. Access AnalyticsView: https://lnkd.in/g346iqe
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SMAR AnalyticsView solutions drive improvement in productivity, efficiency, quality, and sustainability. The solutions can be applied to solve common business intelligence (BI) challenges, enabling users to move rapidly and easily from data to information, without help from IT or from data scientists. Access AnalyticsView: https://lnkd.in/g346iqe
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SMAR AnalyticsView solutions drive improvement in productivity, efficiency, quality, and sustainability. The solutions can be applied to solve common business intelligence (BI) challenges, enabling users to move rapidly and easily from data to information, without help from IT or from data scientists. Access AnalyticsView: https://lnkd.in/g346iqe
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#Concept_of_overfitting_and_underfitting The goal of the model is to extract the relationship and meaning between dependent and independent variables #Underfitting: When the model doesn’t learn the data well enough. It has high bias and low variance, and its generalization ability is weak. #Overfitting: When the model memorizes the data. It has high variance and low bias. Its accuracy decreases when faced with different data in the test set, as the predictions function closely represents the actual values #Detecting_overfitting We can identify overfitting by jointly evaluating the training and test sets in terms of model complexity and prediction error. Analyze the change in error in both sets #preventing_overfitting - Regularization - Bagging (bootstrap aggregating) - Data Augmentation - Cross-validation
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#Removing_anomalies Removing anomalies isn’t just about cleaning data. It’s about building a strong foundation for accurate, reliable, and actionable forecasts. By addressing anomalies before forecasting, you empower your models to uncover meaningful insights and make predictions that drive better decision-making. In Excel, the step I used to follow for removing outlier and neutralize the data is First fig out, Average, Standard deviation, upper and lower limit. Remove Outliers: The logic is, if the number is greater than max value then put the same number and if number is less than lower value, put the same value, or else null. Normalizing the value: Then, start normalize the value, and the logic will be if the number is bigger than max limit and lower than min value then put the mean value, if not then keep the real value. Then, I got to know about the min-max normalization and standardization, still I have not used in this, just remove the outliers here. What steps do you take to handle outliers in your data? We may connect on DM or comment for discussing about this more. #Forecasting #WFM #CallCenter #DataAnalysis
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Business Analyst | 11k+ LinkedIn Family | Top Data Analysis and Data Science Voice Badge | MBA Candidate | Data-Driven Decision Maker
Hypothesis Testing: Making Data-Driven Business Decisions Hypothesis testing allows businesses to validate assumptions using data. Common methods include t-tests, chi-square tests, and ANOVA. Hypothesis testing helps in verifying whether the observed data supports a particular claim or hypothesis. "Test your assumptions with data—let hypothesis testing lead the way." #HypothesisTesting #BusinessDecisions #DataValidation
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