Descriptive Data Analysis & Inferential Data Analysis Quiz

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Questions and Answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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Study Notes

Descriptive Data Analysis

Descriptive data analysis involves exploring, summarizing, and presenting data to understand its key characteristics.
Involves calculating and examining summary statistics such as:
- Mean
- Median
- Mode
- Standard deviation
- Variance
- Range
- Percentiles
Data visualization is used to create visual representations of data, including:
- Charts
- Graphs
- Histograms
- Box plots
- Scatter plots
Types of variables:
- Independent variables: systematically varied by the researcher
- Dependent variables: observed and their values depend on the effects of the independent variables
Forms of variables:
- Discrete variables: only include a finite set of values (e.g., yes/no, republican/democrat)
- Continuous variables: take on any value on a continuous scale (e.g., height, weight, length, time)

Central Tendency

Measures that answer the question: "What is a typical score?"
Provide information about the grouping of numbers in a distribution
Examples of central tendency measures:
- Mean
- Median
- Mode
Frequency polygon: a graphical representation of a distribution showing the frequency of each value

Inferential Data Analysis

Involves making inferences or predictions about a population based on a sample of data
Steps in inferential data analysis:
- Formulate hypotheses
- Select a statistical test
- Calculate p-value
- Interpret results
Hypothesis testing procedure:
- Null hypothesis (H0): typically represents no effect or no difference
- Alternative hypothesis (H1): suggests there is an effect or difference
Significance levels and p-values:
- Significance level: a critical probability associated with a statistical hypothesis test
- p-value: probability value, or observed or computed significance level
Interpretation: the process of drawing inferences from the analysis results

Hypothesis Testing

Types of hypothesis testing:
- Test for difference: tests whether a significant difference exists between groups
- Test for relationship: tests whether a significant relationship exists between a dependent and independent variable
Univariate statistical analysis:
- Examines a single variable at a time
- Aims to understand the distribution, central tendency, and variability of a single variable
Bivariate statistical analysis:
- Focuses on the relationship between two variables
- Analyzes the association, correlation, or dependency between two variables