Descriptive Data Analysis & Inferential Data Analysis Quiz

24 Questions

Match the following statistical terms with their descriptions:

Mean = Average value of a set of numbers Variance = Measurement of how spread out the values in a data set are Percentiles = Values that divide a data set into 100 equal parts Standard Deviation = Measure of the amount of variation or dispersion of a set of values

Match the following variable types with their definitions:

Independent Variable = Variable systematically varied by the researcher Dependent Variable = Variable whose values depend on the effects of the independent variables Discrete Variable = Variable that includes a finite set of values Continuous Variable = Variable that can take on any value on a continuous scale

Match the following data visualization techniques with their functions:

Histograms = Display distribution of numerical data Box Plots = Show distribution of data based on five-number summary Scatter Plots = Visualize relationship between two variables Bar Charts = Compare different categories of data

Match the following terms related to central tendency with their meanings:

Central Tendency = Statistics describing the center of a distribution Typical Score = Value representing what is considered normal or average in a dataset Grouping of Numbers = Characterization of how numbers are clustered in a distribution Level of Measurement = Determines the type and scale of data being analyzed

Match the following statistical concepts with their descriptions:

Null hypothesis = Represents no effect or no difference Alternative hypothesis = Suggests there is an effect or difference P-Value = Probability of obtaining test results at least as extreme as the observed results Standard deviation = Measure of the dispersion or variability of a set of values

Match the following terms with their meanings in inferential data analysis:

Hypotheses formulation = Process of stating null and alternative hypotheses Statistical test selection = Choosing an appropriate test based on research question and data type P-Value calculation = Determining the probability of obtaining test results Regression analysis = Statistical technique to analyze the relationship between variables

Match the following salary statistics with their meanings:

Mean salary = Average salary calculated from a set of values Median salary = Middle value in a sorted list of salaries Standard deviation of salaries = Measure of how spread out salaries are from the average Annual salary distribution = Spread of salaries across different amounts in a given year

Match the following terms with their correct definitions on normal distributions:

Normal curve = Symmetrical bell-shaped curve representing a normal distribution Percentages on normal curve = Indicate the proportion of data within specific standard deviations from the mean 68-95-99.7 rule = Rule describing the percentage of data within 1, 2, and 3 standard deviations from the mean Significance levels = Thresholds used to determine whether a result is statistically significant

Match the following concepts related to variability with their definitions:

Variability in data = Extent to which data points in a set differ from each other Frequency polygon = Graphical representation showing how often values occur in a dataset Bell curve standard deviation = Measure indicating how tightly or loosely data points are clustered around the mean Normal distribution percentage areas = Segments representing proportions of data within certain standard deviations from the mean

Match the following salary classes with their characteristics:

Class A salaries = Higher variability in salary amounts with a mean close to 75.5K Class B salaries = Lower variability in salary amounts with a mean close to 75.5K Salary sample data statistics = Include average, median, and standard deviation for understanding salary distribution Annual salary figures = Represent individual salary amounts for employees in a given year

Match the following terms with their definitions:

Null Hypothesis = There is no significant difference/relationship between groups Alternative Hypothesis = There is a significant difference/relationship between groups Significance Level = A critical probability associated with a statistical hypothesis test p-value = Probability value, or the observed or computed significance level

Match the following statements with the correct interpretation:

If the value is consistent with the hypothesis = The hypothesis is supported If the value is not consistent with the hypothesis = The hypothesis is not supported Object of the research is to reject or accept the Null Hypothesis/es = The aim is to determine if there is a significant difference/relationship between groups Interpretation in statistical analysis = The process of drawing inferences from the analysis results

Match the following terms regarding Type I error:

Significance Level = The acceptable level of Type I error p-value comparison with Significance Level = Testing hypotheses for Type I error Importance of controlling Type I error = Ensuring that false positives are minimized Definition of Type I error = Incorrectly rejecting a true null hypothesis

Match the following pairs related to hypothesis testing:

Acceptable Significance Level = Critical probability for supporting a difference between observed and expected values Hypothesis Testing Objective = Determining if there is evidence to reject the Null Hypothesis Role of p-values in hypothesis testing = Comparing the observed significance level with the acceptable threshold Relationship between Null Hypothesis and Alternative Hypothesis = Contrasting positions regarding differences or relationships between groups

Match the statistical technique with its description:

Univariate Statistical Analysis = Involves analysis of a single variable at a time Bivariate Statistical Analysis = Focuses on the relationship between two variables Measures of Central Tendency = Include mean, median, and mode Measures of Dispersion = Include variance and standard deviation

Match the statistical test with its purpose:

Test for Difference = To determine if a significant difference exists between groups Tests for Relationship = To assess if a significant relationship exists between variables Hypothesis Testing Using Basic Statistics = Utilizes statistical analysis to test hypotheses Frequency Distributions = Display the distribution of values in a dataset

Match the concept with its definition:

Inferential Statistics = Draws conclusions and makes decisions based on data interpretations Hypothesis Testing = Process of using statistics to determine if a hypothesis is supported or rejected Central Tendency = Represents the center or average value of a dataset Correlation Analysis = Examines the relationship between two variables

Match the analysis type with its focus:

Qualitative Meaning of Data = Important aspect from a management perspective Predictive Relationship = Relationship between variables that can predict outcomes Association Analysis = Analyzing how changes in one variable affect another Probability Distributions = Show the likelihood of different outcomes

Match the statistical analysis type with its description:

Univariate Statistical Analysis = Tests of hypotheses involving only one variable Bivariate Statistical Analysis = Tests of hypotheses involving two variables Multivariate Statistical Analysis = Statistical analysis involving three or more variables or sets of variables Inferential Statistics = Provide two environments: Test for Difference and Tests for relationship

Match the hypothesis type with its description:

Null Hypothesis = There is no significant difference in quiz one performance regarding grouping (group 1 and group 2) Alternative Hypothesis = There is a significant difference in quiz one performance regarding grouping (group 1 and group 2) Predictive Relationship = Relationship may also be predictive Stated Hypothesis = The specifically stated hypothesis is derived from the research objectives

Match the variable type with its definition:

Independent Variable = Group (2 categories) Dependent Variable = Quiz one performance Y Variable = Dependent variable X Variable = Independent variable

Match the significance level with its interpretation:

p-value less than 0.05 = Strong evidence against the null hypothesis p-value greater than 0.05 = Weak evidence against the null hypothesis p-value = 0.00 = Represents the probability of obtaining the observed results if the null hypothesis were true p-value = 0.04 = Represents the probability of obtaining the observed results (or more extreme results) if the null hypothesis were true

Match the hypothesis testing step with its description:

Sample Collection = A sample is obtained and the relevant variable is measured Comparison Stage = The measured sample value is compared to the value either stated explicitly or implied in the hypothesis Hypothesis Derivation = The specifically stated hypothesis is derived from the research objectives Research Objective Alignment = The specifically stated hypothesis is aligned with the research objectives

Match the test environment with its purpose:

Test for Difference = To test whether a significant difference exists between groups Tests for Relationship = To test whether a significant relationship exists between a dependent (Y) and independent (X) variable/s

Study Notes