You are cleaning the data for data modeling. Which of the following is NOT a data preparation step ? Group of answer choices Changing the variable type of outcome variable (Defaulted) Removing variable Index Replacing any missing value by the mean of that variable Computing the confusion matrix
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You are cleaning the data for data modeling. Which of the following is NOT a data preparation step ?
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- In the sampling, hypothesis testing and analysis of business research data, which of the following is true? Group of answer choices 1. We make inferences about the population parameters based on sample statistics 2. The sample should be representative of the population 3. A larger sample size is always better 4. Answers1 and 2 only 5. Answers 1, 2 and 3What is the group of information representing the qualitative or quantitative attributes of a variable or set of variables?a. Datab. Numbersc. Populationd. VariablesUnder what circumstances would the Mood's Median non-parametric test be applied in business research? Group of answer choices a. The data are not normally distributed b. Outliers in the data exist c. Multiple medians are to be compared d. Answers 1 and 2 only e. Answers 1, 2 and 3
- A technique for the study of interrelationships among variables, usually for the purposes of data reduction and the discovery of underlying constructs or latent dimensions is known as: a. Factor Analysis b. Structural Model Analysis c. Multiple linear regression d. Reliability AnalysisYour participants are chosen for a nonprobability snowball sample due to their extreme views on a parameter of interest. You are likely to have _____. Multiple Choice limited sampling error difficulty building a sampling frame significant nonsampling error significant systematic variance None of these choices is correct.4.1 TYPES OF RANDOM VARIABLES. Which of the following describe continuous random variables? Which describe discrete random variables? The number of newspapers sold by the New York Times each month The amount of ink used in printing a Sunday edition of the New York Times The actual number of ounces in a 1-gallon bottle of laundry detergent The number of defective parts in a shipment of nuts and bolts The number of people collecting unemployment insurance each month
- Which best describes the null hypothesis associated with an Analysis of Variance (ANOVA)? Group of answer choices a. Ho: Variance 1 = Variance 2 = Variance 3 b. Ho: Standard Deviation 1 = Standard Deviation 2 = Standard Deviation 3 c. Ho: Proportion 1 = Proportion 2 = Proportion 3 d. Ho: Median 1 = Median 2 = Median 3 e. Ho: Mean 1 = Mean 2 = Mean 33) The major variable is the central idea of the entire literature. Its sub-variables help specify which particular aspect of the major variable is being referred to. The indicators, on the other hand, are specific information that describes the sub-variables. Examples: Major variable: Study habits Sub-variable: Time required for studying, methods of studying, and place for studying Indicators: Employing patterned time intervals for effective studying: using mnemonics in memorizing the terms; and studying in one's ideal environment Instructions: Identify the major variables, sub-variables, and indicators of the examples of literature review. Example 3: On leadership Competencies and Management Skills (refer to the photo for the details)Which of the following is a probability sampling method? a.Cluster sampling b.Quota sampling c.Snowball sampling d.Convenience sampling e.Judgement sampling
- Which of these terms refers to data that already exists? Primary data Secondary data Tertiary data Quaternary data Original dataWhich modeling techniques are suitable to build a predictive model ? (Multiple answer may be correct) Group of answer choices Multiple linear regression Classification tree Logistic regression ClusteringWhat is the recommended sequence of building predictive model ? which answer choice? Model Evaluation > Data Preparation > Model Training > Data Sampling Model Evaluation > Data Preparation > Data Sampling > Model Training Data Sampling > Data Preparation > Model Training > Model Evaluation Data Sampling > Data Preparation > Model > Model Evaluation > Model Training