Introduction to International Business Analytics-intermediate CHOOLS No Comment 04Sep Share Welcome to your Introduction to International Business Analytics-intermediate What is the primary purpose of 'exploratory data analysis'? A) To understand data characteristics and uncover patterns B) To develop predictive models C) To design marketing strategies D) To create financial reports What is a major challenge when performing business analytics on international data? A) Data consistency across different regions B) Employee performance tracking C) Local office management D) Product development Which method is used to combine data from various international sources into a single database? A) Data integration B) Data cleaning C) Data transformation D) Data mining What technique is used to handle missing values in a dataset? A) Imputation B) Normalization C) Standardization D) Feature scaling Which model is commonly used to forecast sales in international markets? A) ARIMA (AutoRegressive Integrated Moving Average) B) K-Means clustering C) Decision Trees D) Naive Bayes What is the purpose of 'dimension reduction' in international business analytics? A) To reduce the number of features in a dataset B) To increase the size of the dataset C) To combine multiple datasets D) To normalize data Which of the following is an example of a supervised learning technique used in business analytics? A) Linear Regression B) K-Means Clustering C) Principal Component Analysis D) Hierarchical Clustering What is the primary use of 'factor analysis' in international business analytics? A) To identify underlying relationships between variables B) To classify data into categories C) To predict future trends D) To analyze time series data What does 'data triangulation' refer to in international business analytics? A) Using multiple data sources to validate findings B) Combining data from different time periods C) Analyzing data from a single source in detail D) Standardizing data across regions Which statistical test is used to compare the means of two independent groups? A) T-Test B) Chi-Square Test C) ANOVA (Analysis of Variance) D) Pearson Correlation What is 'cluster analysis' primarily used for in business analytics? A) Grouping similar data points into clusters B) Identifying trends over time C) Predicting future sales D) Measuring customer satisfaction Which technique is used to identify patterns in categorical data? A) Association Rule Mining B) Time Series Analysis C) Regression Analysis D) Factor Analysis What does 'cross-validation' help with in predictive modeling? A) Assessing the model’s performance on unseen data B) Normalizing the dataset C) Reducing the dimensionality of the data D) Handling missing values Which type of analysis is used to understand the factors affecting customer churn? A) Survival Analysis B) Time Series Analysis C) Cluster Analysis D) Regression Analysis What is 'data normalization' used for in international business analytics? A) Scaling data to a standard range B) Removing duplicate data entries C) Combining multiple datasets D) Handling missing data Which metric would you use to assess the performance of a classification model? A) F1 Score B) Mean Absolute Error C) R-Squared D) Log-Likelihood What does 'hierarchical clustering' aim to achieve in business analytics? A) Creating a hierarchy of clusters by grouping similar data points B) Predicting future sales trends C) Analyzing time series data D) Measuring customer satisfaction Which data visualization technique is useful for showing relationships between two continuous variables? A) Scatter Plot B) Bar Chart C) Histogram D) Pie Chart What is 'regression analysis' used to predict? A) The relationship between dependent and independent variables B) The distribution of categorical data C) The clustering of data points D) The frequency of events Which of the following metrics helps in evaluating the goodness of fit for a regression model? A) R-Squared B) Mean Squared Error C) Accuracy D) Precision What does 'principal component analysis' (PCA) do? A) Reduces the dimensionality of the dataset while retaining most variance B) Classifies data into categories C) Predicts future data points D) Clusters similar data points Which technique is used to detect anomalies in a dataset? A) Outlier Detection B) Cluster Analysis C) Regression Analysis D) Association Rule Mining What does 'data wrangling' involve in business analytics? A) Cleaning and transforming raw data into a usable format B) Creating predictive models C) Designing marketing campaigns D) Generating financial reports Which technique is used to assess the relationship between multiple independent variables and a dependent variable? A) Multiple Regression Analysis B) Simple Linear Regression C) Cluster Analysis D) Principal Component Analysis Which method is used to validate the performance of a predictive model? A) K-Fold Cross-Validation B) Data Normalization C) Feature Selection D) Data Transformation What does 'sentiment analysis' help businesses understand? A) The sentiment and opinions of customers from textual data B) Sales trends over time C) Operational efficiency D) Financial performance Which technique is used to combine several models to improve prediction accuracy? A) Ensemble Learning B) Data Normalization C) Dimensionality Reduction D) Outlier Detection What does 'time series forecasting' help in predicting? A) Future values based on historical time-stamped data B) Relationships between variables C) Clusters of similar data points D) Sentiments from customer reviews Which method is used to measure the strength of the relationship between two variables? A) Correlation Coefficient B) ANOVA C) Chi-Square Test D) T-Test What does 'data enrichment' involve? A) Adding external data to enhance the existing dataset B) Normalizing data C) Cleaning data D) Reducing data dimensionality What does 'outlier analysis' help identify? A) Data points that differ significantly from the rest of the dataset B) Clusters of similar data points C) The distribution of data D) Correlations between variables Which technique is used to group similar data points together based on their characteristics? A) Clustering B) Regression C) Classification D) Association Rule Mining What does 'feature engineering' involve in machine learning? A) Creating new features from existing data to improve model performance B) Cleaning and preprocessing data C) Building predictive models D) Analyzing data distributions Which model is typically used for binary classification problems? A) Logistic Regression B) K-Means Clustering C) Principal Component Analysis D) ARIMA Which method is used to measure the accuracy of a classification model? A) Confusion Matrix B) ROC Curve C) Precision-Recall Curve D) All of the above What does 'data segmentation' involve? A) Dividing data into subsets based on certain criteria B) Combining multiple datasets C) Normalizing data D) Predicting future trends Which statistical measure indicates the spread or dispersion of a dataset? A) Standard Deviation B) Mean C) Median D) Mode What is the primary objective of 'prescriptive analytics'? A) To recommend actions based on predictive analysis B) To describe past events C) To analyze current performance D) To visualize data trends What does 'factor analysis' help to identify in business analytics? A) Underlying factors that explain the data variance B) Clusters of similar data points C) Predictive trends in data D) Relationships between dependent and independent variables Which of the following is a key component of a successful data analytics strategy? A) Clear objectives and goals B) Complex algorithms C) High volume of data D) Large team of analysts What does 'sensitivity analysis' help assess? A) The impact of changes in input variables on model outcomes B) The accuracy of classification models C) The clustering of similar data points D) The distribution of numerical data Which method is used to deal with multicollinearity in regression analysis? A) Principal Component Analysis (PCA) B) Logistic Regression C) Decision Trees D) K-Means Clustering What does 'data storytelling' involve in business analytics? A) Using data visualizations and narratives to communicate insights effectively B) Analyzing numerical data trends C) Creating predictive models D) Cleaning and preprocessing data Time is Up! Previous Introduction to International Business Analytics September 4, 2024 Next Introduction to International Business Analytics-expert September 4, 2024 You Might Also Like Hello world CHOOLS No Comment How to Disable Avast Antivirus CHOOLS No Comment AVG Review — Is the Absolutely free Version As effective as the Premium Version? CHOOLS No Comment Understanding the Limitations of Models of Managing CHOOLS No Comment Careers Similar To Teaching CHOOLS No Comment
