Power Query MCQS 11

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Online Power Query MCQs with Answers

1. Suppose we created 2 queries, one for Sydney and one for Other Instructors. We did not load these into the worksheet; and we only created a connection. Due to this setup, when choosing to append these queries, the result cannot be loaded into the worksheet – we can only create a connection.

 
 

2. Which sources does Power Query allow us to Get Data from?

 
 
 
 
 

3. When getting data from a folder, the preview panel only shows a preview of the first file. Suppose there is an Australian company with an office in each of the 8 states and territories, where the parent office is in the state of New South Wales. If the 8 files are named as below, which file will appear in the preview pane, and why?

Western Australia
South Australia
Northern Territory
Tasmania
Victoria
Australian Capital Territory
New South Wales
Queensland

 
 
 
 

4. When getting data from another workbook, it is essential to transform the data within that workbook first.

 
 

5. If you created a table in Excel after getting data from a database, changing the data in the new table will update the original database if you click Refresh.

 
 

6. When using an Append Query, the two tables must:

 
 
 
 

7. There are currently three columns in Power Query: Street Address, City, and State, with data such as “42 Wallaby Way” (Street Address), “Sydney” (City), and “NSW” (State).
What could we do to create a new column that displayed the full address as a single string, such as “42 Wallaby Way, Sydney, NSW”?

 
 
 
 

8. Which aspect of getting data from a folder is similar to the result of an Append Query?

 
 
 
 

9. When getting data from a PDF that contains a table in each of the 5 pages, what will we see in the preview panel?

 
 
 
 

10. Suppose that we have created a new table by getting data from a folder that contains data from each of 5 branches of a company, each with its file. What should we do if we open two new branches – that maintains the existing structure and also gives unique information for each branch in its file?

 
 
 

11. What are some differences between Power Query and standard Excel?

 
 
 
 
 

12. In Australia, the first two digits represent the area code for a phone number, such as 0223789456. Consider a field that contains phone numbers in this format. What would be the appropriate option under Split Column to extract the area code?

 
 
 

13. If you are utilising Power Query primarily as a ‘working space’ without viewing the data in your spreadsheet within Close & Load to, you should choose the option:

 
 
 
 

14. When getting data from a database, unlike getting data from a spreadsheet, you have to transform the data at the source first.

 
 

15. After creating a new Table via Power Query, what would happen when the original data is edited or changed?

 
 

16. An American company has 50 offices, one in each state, which all use their own Excel spreadsheet for their human resources data, but the parent company wants to maintain a separate spreadsheet that gets the data from all these files. What would be an efficient solution to this problem?

 
 
 
 

17. Imdad was working in Power Query and loaded the data into a new worksheet. He notices that he has made an error and needs to undo one of his steps. What should he do?

 
 
 
 

18. What would happen if you tried to create a query from data in the current workbook that is not part of a table or a named range?

 
 
 
 

19. When getting data from a PDF that contains a table in each of 5 pages and selecting multiple items, this will create as many queries as the number of items you have selected.

 
 

Quiz Design of Experiments 11

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Challenge your understanding of experimental design with this comprehensive Quiz Design of Experiments covering non-parametric alternatives to ANOVA, Friedman and Kruskal-Wallis tests, rank transformations, treatment contrasts, orthogonal contrasts, and multiple comparison methods. Sharpen your skills and test your knowledge with our Quiz Design of Experiments is a perfect resource for mastering the principles of experimental design and statistical testing. The Quiz is ideal for students, data scientists, analysts, and researchers. Let us start with the Quiz Design of Experiments now.

Online Quiz Design of Experiments with Answers
Please go to Quiz Design of Experiments 11 to view the test

Online Quiz Design of Experiments with Answers

  • Which one is not a non-parametric alternate to ANOVA?
  • Friedman two-way analysis of variance test is used to determine whether the M samples have been drawn from:
  • Non-parametric tests make no assumptions about the —————— of the variables being assessed.
  • Kruskal-Wallis test is also called:
  • For only two groups Kruskal-Wallis test extends to:
  • Kruskal-Wallis test uses:
  • The ANOVA on ranks has never been recommended when the underlying assumption of —————— has been violated.
  • A variant of rank-transformation is:
  • ANOVA on ranks is a statistic designed for situations when the underlying assumption of homogeneous variances has been violated:
  • ANOVA does not tell us which treatments are —————- to/from each other.
  • A linear combination of treatment means is:
  • One method to examine treatment effects is called:
  • The kinds of inference we work with contrast are:
  • Does the resulting F-test of contrast involving four means use degrees of freedom?
  • A contrast is tested by comparing its mean squares to the ————— using ANOVA techniques.
  • Number of orthogonal contrasts which are always possible with $a$ treatments are:
  • Orthogonal contrasts are always:
  • The choice of contrasts is based on the:
  • Multiple comparison tests are used when:
  • Multiple comparison tests are also called what?

Data Analysis in R Language

SAS STAT Procedures

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Explore essential SAS STAT procedures in a question-and-answer format, covering topics like model selection, ANOVA, regression, and distance metrics. This blog post provides clear explanations, practical applications, and key features of PROC REG, PROC GLM, PROC LOGISTIC, PROC MIXED, PROC DISTANCE, and more. SAS STAT Procedures are perfect for data analysts, statisticians, and SAS users looking to enhance their statistical analysis skills!

What are SAS STAT Software and SAS STAT Procedures?

SAS STAT is a statistical analysis software within the SAS (Statistical Analysis System) suite. The SAS STAT software provides advanced statistical procedures for data analysis, such as regression analysis, ANOVA, survival analysis, multivariate analysis, predictive modeling, statistical visualization, and many more. It is widely used in research, business, and healthcare for data-driven decision-making.

SAS STAT Procedures

What are the Features of SAS STAT?

The Key features of SAS STAT are:

  • Data Management & Manipulation: It handles large datasets with ease, including data cleaning and transformation.
  • Advanced Statistical Procedures: Supports regression, ANOVA, survival analysis, multivariate analysis, and more.
  • Predictive Modeling: It offers machine learning and forecasting capabilities.
  • High-Performance Computing: It is optimized for parallel processing and big data analytics.
  • Graphical & Reporting Tools: It is capable of generating detailed visualizations and reports.
  • Integration with Other Tools: It can work with databases, Excel, R, Python, and Hadoop.
  • Automated Analysis & Customization: It allows scripting and automation for repetitive tasks.
  • Compliance & Security: It ensures data privacy and regulatory compliance for industries like healthcare and finance.

What are the Uses of SAS STAT Software?

SAS STAT software offers tools for an extensive kind of packages in commercial enterprise, authorities, and academia. The foremost uses of SAS are financial evaluation, forecasting, economic and financial modeling, time series analysis, economic reporting, and manipulation of time collection facts.

  • Data Analysis & Visualization: Processes large datasets and generates reports.
  • Business & Financial Analytics: Supports risk analysis, fraud detection, investment analysis, and market research.
  • Predictive Analytics: Helps in forecasting trends, outcomes using statistical models and making data-driven decisions.
  • Academic & Scientific Research: Used for statistical modeling and hypothesis testing.
  • Machine Learning & AI: Integrates with modern AI techniques for data-driven decision-making.
  • Healthcare & Clinical Research: Analyses medical data for drug trials and epidemiological studies.
  • Government & Policy Making: Aids in census analysis, economic forecasting, and social research.
  • Social & Environmental Studies: Supports research in public policy, climate change, and demographics.
  • Marketing & Customer Analytics: Analyses customer behavior, segmentation, and campaign effectiveness.
  • Quality Control & Manufacturing: Ensures process optimization and defect reduction.

What are the SAS STAT Procedures Offered for Performing ANOVA?

There are several SAS STAT procedures for performing ANOVA, depending on the complexity and type of analysis required:

  • PROC ANOVA: It is used for classical one-way and two-way ANOVA, primarily for balanced designs.
  • PROC GLM (General Linear Model): It can handle unbalanced and multifactor ANOVA, including interactions and covariates (ANCOVA).
  • PROC MIXED: It is used for ANOVA with random effects and mixed models, often applied in hierarchical and longitudinal data analysis.
  • PROC GLIMMIX (Generalized Linear Mixed Models): It extends mixed models to non-normal data and generalized linear models (GLMs).
  • PROC NESTED: It is used for hierarchical or nested ANOVA designs where factors are nested within each other.
  • PROC VARCOMP: It estimates variance components in random effects models, useful in certain ANOVA applications.
  • PROC LATTICE: It is used for analyzing lattice designs in agricultural and experimental research.

Each procedure in SAS STAT allows flexibility for different experimental designs and statistical modeling requirements.

How Can One Fit Statistical Models in SAS STAT?

There are several SAS STAT procedures to fit statistical models depending on the data type and analysis:

  • PROC REG: It fits linear regression models for continuous outcomes.
  • PROC GLM: It fits general linear models (GLMs), including ANOVA and ANCOVA.
  • PROC MIXED: It fits mixed-effects models for hierarchical or repeated measures data.
  • PROC LOGISTIC: It fits logistic regression models for binary and categorical outcomes.
  • PROC GENMOD: It fits generalized linear models (GLMs), including Poisson and negative binomial models.
  • PROC PHREG: It fits Cox proportional hazards models for survival analysis.
  • PROC GLIMMIX: It fits generalized linear mixed models (GLMMs) for complex data structures.

Each procedure allows customization using model statements, selection criteria, and diagnostics for better model fitting.

What does the PROC DISTANCE in SAS STAT do?

PROC DISTANCE computes distance and dissimilarity measures between observations in a dataset. It is commonly used for cluster analysis, nearest neighbor searches, and multivariate analysis.

The key features of PROC DISTANCE are:

  • Supports Euclidean, Manhattan, Minkowski, and Mahalanobis distances.
  • Computes similarity measures like Pearson correlation and cosine similarity.
  • Handles both numeric and categorical data.
  • Generates distance matrices for further analysis in clustering or classification tasks.

The PROC DISTACE procedure is useful in data mining, machine learning, and pattern recognition applications.