**Enroll Here: Statistics 101 Cognitive Class Exam Quiz Answers**

**Statistics 101 Cognitive Class Certification Answers**

**Module 1 – Welcome to Statistics Quiz Answers – Cognitive Class**

**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

**Module 2 – Descriptive Statistics Quiz Answers – Cognitive Class**

**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

**Module 3 – Advanced Descriptive Statistics Quiz Answers – Cognitive Class**

**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

**Module 4 – Visualization Quiz Answers – Cognitive Class**

**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

**Module 5 – “From Start to Finish: Beauty Pays Data” Quiz Answers – Cognitive Class**

**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

**Statistics 101 Final Exam Answers – Cognitive Class**

**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 the science of collecting, analyzing, interpreting, and presenting data. It’s a fundamental tool used in various fields such as science, business, economics, engineering, and social sciences. In this introductory overview, let’s cover some key concepts:

**Descriptive Statistics**: This branch focuses on summarizing and describing the characteristics of a dataset. It includes measures like mean, median, mode, variance, and standard deviation.**Inferential Statistics**: This branch involves making inferences or predictions about a population based on a sample of data. It includes techniques like hypothesis testing, confidence intervals, and regression analysis.**Population and Sample**: A population is the entire group of individuals or objects that we’re interested in studying, while a sample is a subset of the population that we actually observe and collect data from.**Variables**: In statistics, a variable is any characteristic or quantity that can take different values. Variables can be classified as either categorical (e.g., gender, color) or numerical (e.g., height, weight).**Types of Data**: Data can be classified into two main types: qualitative data, which describes qualities or characteristics, and quantitative data, which consists of numerical measurements.**Probability**: Probability theory is essential in statistics for quantifying uncertainty. It helps us understand the likelihood of various outcomes and events.**Statistical Distributions**: Distributions describe how the values of a variable are spread out. Common distributions include the normal distribution, binomial distribution, and Poisson distribution.**Statistical Tests**: These are procedures used to make decisions about a population based on sample data. Examples include t-tests, chi-square tests, and ANOVA.**Data Visualization**: Visualizing data is crucial for understanding patterns, trends, and relationships. Graphs and charts like histograms, scatter plots, and bar charts are commonly used for this purpose.**Ethical Considerations**: Statistics is not just about crunching numbers; it also involves ethical considerations, such as ensuring data privacy, avoiding bias, and accurately representing findings.