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Statistics 101 Cognitive Class Exam Quiz Answers

Statistics 101 Cognitive Class Certification Answers

Statistics 101 Cognitive Class Exam Quiz Answers

Question 1: Which one of the following is not an example of statistics?

  • The sweet smell of success
  • Monthly housing prices in a city
  • Traffic noise at a busy intersection
  • Annual unemployment rate in a country

Question 2: Which of the following statements is true? One can estimate the votes for a presidential candidate in a forthcoming election by:

  • Asking your barber
  • Conducting a poll of a random sample of the voting age population
  • Asking your favourite university professor about who is going to win
  • Asking the cab drivers in a city of their vote preference

Question 3: Which of the following is not a type of data visualization? (Pick the most appropriate answer)

  • An organization chart
  • A pie chart
  • A time series plot
  • A bar chart

Question 1: Which of the following is not a cross-sectional data set?

  • Monthly survey of consumer confidence
  • National Census conducted every 5 or 10 years
  • Weekly data on average temperature
  • A survey of student satisfaction conducted at the end of the course

Question 2: Which of the following is an example of time series data?

  • Number of dolphins in the Pacific Ocean
  • Average batting average of a baseball player
  • Number of trees in Jardin du Luxemburg in Paris
  • Annual average housing price in New York

Question 3: Which of the following is an example of multivariate data?

  • Vital signs recorded for a new born baby
  • Number of songs played in a day by your favourite radio station
  • Daily temperature recorded by a monitoring station in Antarctica
  • Number of words spoken by President Donald Trump in his inaugural speech

Question 1: What is a suitable way to display the average income earned by men and women in a city?

  • A scatter plot
  • A pie chart
  • A histogram
  • A bar chart

Question 2: What is a suitable way to display relationship between two continuous variables?

  • A scatter plot
  • A pie chart
  • A histogram
  • A bar chart

Question 3: What’s the best way to display median and outliers?

  • A bubble chart
  • A time series plot
  • A box plot
  • A scatter plot

Question 1: What is the best way to display daily temperature for a city?

  • A histogram
  • A pie chart
  • A Box plot
  • A line plot

Question 2: What extra step is needed to display two related time series variables that differ greatly in magnitude?

  • Use two axes to display the lines
  • Plot them by colouring the lines with different colours
  • Plot the lines with different thickness
  • Plot them separately in two charts

Question 3: When the sum of two or more categories equals 100, what chart type is ideally suited for displaying data?

  • A line chart
  • A pie chart
  • A box plot
  • A histogram

Question 1: When using sample data with weights, it is important to compute statistics by:

  • Filtering the data with the weight variable
  • Weighting the data with the appropriate variable
  • Ignoring the weights
  • None of the above

Question 2: When multiple observations are reported for each respondent in the data set, to compute statistics for variables about the respondents, one must:

  • Ignore the presence of duplicates and compute statistics as usual
  • Weight data by duplicates
  • Remove duplicates before running analysis
  • None of the above

Question 3: To be able to trace one’s steps, one must:

  • Generate and record syntax for every command executed for the analysis
  • Note steps taken for the analyses in a notebook
  • Use mouse for point and click to undertake the analysis
  • None of the above

Question 1: What is meta data?

  • Data about metal fatigue
  • The metabolism data in a clinical trial
  • The data about metamorphism
  • It’s the data about data

Question 2: Which of the following is not an example of big data?

  • Number of photographs uploaded to the internet every day
  • The emails sent daily from your email provider
  • The number of big basketball players in NBA (National Basketball Association)
  • Weekly data about individual credit card transactions registered for your local credit card company

Question 3: SPSS is ideally suited to analyze data stored in:

  • Books as words and paragraphs
  • Digital video files of Hollywood movies
  • Tables as rows and columns
  • Digital audio files of music records

Question 4: Reproducibility in statistical analysis requires one to use statistical software that supports:

  • Free usage for analysis
  • Syntax (script) based analysis
  • Tabular output of results
  • A point and click environment

Question 5: Which of the following is an example of categorical data?

  • Number of fire hydrants in a city
  • Number of children at a kindergarten
  • Length of the river Nile
  • Mode of travel to work

Question 6: Which of the following is not an example of ordinal data?

  • Ranking of athletes in an Olympic competition
  • Number of trees in a park
  • Level of happiness on a scale of 1 to 5
  • Street numbers

Question 7: Which of the following is an example of interval data?

  • The ethnicity of a person
  • “None”, “Some”, “Frequent” – representing the frequency of exercise
  • First, second and third rankings in a sports competition
  • Weight

Question 8: For a survey of student satisfaction in a course, the population comprises:

  • All students enrolled in the course
  • All male students registered in the department
  • All A+ students enrolled in the course
  • All students registered at the university

Question 9: A mean is meaningful for the following type of data

  • Audio data
  • Ordinal data
  • Ratio data
  • Categorical data

Question 10: Median represents a value in the data set where:

  • Half of the observations are above the median and the other half below it
  • Most observations are negative
  • Half of the observations are known and the other half not known
  • Most observations are positive

Question 11: If the standard deviation of a variable is larger than the mean, the variable depicts:

  • Fluidity
  • Low variance
  • Smoothness
  • High variance

Question 12: A histogram is a graphical display of how a variable is

  • Observed
  • Displayed
  • Distributed
  • Recorded

Question 13: The following type of computation is suited for categorical data:

  • Proportions
  • Standard deviations
  • Histogram
  • Averages

Question 14: The relationship between two categorical variables can be captured by:

  • Standard deviation
  • A crosstabulation
  • A bar chart
  • A histogram

Question 15: The probability of getting a 2 by rolling TWO six-sided dice (with sides labeled as 1, 2, 3, 4, 5, 6) is

  • 1/36
  • 1/18
  • 2
  • 2/36

Question 16: What is the best way to determine the significance of relationship between two categorical variables?

  • A regression model
  • A Pearson Correlation test
  • A Chi-square test
  • A t-test

Question 17: If two continuous variables are positively correlated, their scatter plot will depict:

  • A flat line
  • A downward sloping curve
  • An upward sloping curve
  • None of the above

Question 18: What is the best way to determine the significance of relationship between two continuous variables?

  • A regression model
  • A Pearson Correlation test
  • A Chi-square test
  • A t-test

Question 19: A good chart should not be missing the following:

  • A self-explanatory variable title
  • Thick borders
  • A dark background colour
  • Bright colours

Question 20: What is the best practice to display axes labels?

  • Use self-explanatory variables
  • Use variable names
  • Use bold font to highlight labels
  • Don’t use any labels

Introduction to Statistics 101

Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It plays a crucial role in various fields such as science, business, economics, social sciences, and more. Here are some key concepts and terms related to statistics:

  1. Descriptive Statistics:
    • Measures of Central Tendency: Mean, Median, Mode
    • Measures of Dispersion: Range, Variance, Standard Deviation
  2. Inferential Statistics:
    • Hypothesis Testing: A method for making inferences about a population based on a sample of data.
    • Confidence Intervals: A range of values used to estimate the true value of a population parameter.
  3. Probability:
    • Probability Distributions: Describes the likelihood of obtaining different values in a population.
    • Random Variables: Variables whose values are determined by chance.
  4. Regression Analysis:
    • Linear Regression: Examines the relationship between two or more variables.
    • Multiple Regression: Examines the relationship between multiple independent variables and a dependent variable.
  5. ANOVA (Analysis of Variance):
    • A statistical method used to compare means among different groups.
  6. Statistical Software:
    • Tools like R, Python (with libraries like NumPy, Pandas, and Statsmodels), SAS, SPSS, and others are commonly used for statistical analysis.
  7. Sampling Techniques:
    • Random Sampling, Stratified Sampling, Cluster Sampling, Convenience Sampling, etc.
  8. Statistical Tests:
    • t-test: Compares the means of two groups to determine if there is a significant difference.
    • Chi-squared test: Tests the independence of categorical variables.
    • ANOVA (Analysis of Variance): Tests the equality of means across multiple groups.
  9. Bayesian Statistics:
    • A statistical paradigm that involves updating probabilities based on new evidence.
  10. Data Types:
    • Qualitative Data: Categorical data without a numerical value.
    • Quantitative Data: Numerical data that can be measured and counted.
  11. Population vs. Sample:
    • Population: The entire set of individuals or instances about whom information is desired.
    • Sample: A subset of the population used to make inferences about the entire population.

Statistics is a powerful tool for making informed decisions, drawing conclusions from data, and testing hypotheses. It provides a framework for understanding variability and uncertainty in various phenomena.

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