Bivariate Analysis

Multiple Select

Which of the following questions can be answered using correlations, t-tests, or chi-square tests?

• Does one thing affect another?

• What is the capital of France?

• Are groups different?

• Is there a pattern?

Open Ended

Why is it important to use statistical tests like correlations, t-tests, and chi-square tests when analyzing data?

Multiple Choice

Which of the following best describes a correlation of +1 between two variables?

• A perfect positive relationship where both variables increase together

• A perfect negative relationship where one variable increases as the other decreases

• No relationship between the variables

• A weak positive relationship

Open Ended

Why else would weight decrease as age increases in elderly populations?

Multiple Choice

Which of the following graphs represents a strong negative correlation?

• A graph where points trend upward from left to right

• A graph where points trend downward from left to right

• A graph where points are scattered randomly

• A graph where points form a horizontal line

Multiple Choice

Which of the following situations would be most appropriate for a Chi-Square Test?

• Comparing the average height of men and women

• Testing if favorite snack type differs by grade level

• Measuring the relationship between hours studied and test scores

• Comparing the mean hospital stay between two neighborhoods

Multiple Choice

Match the following statistical tests to the type of data or groups they are best suited for: Correlation, T-Test, Chi-Square.

• Correlation: Two numbers; T-Test: One number, two groups; Chi-Square: Two groups, both categories

• Correlation: Two groups, both categories; T-Test: Two numbers; Chi-Square: One number, two groups

• Correlation: One number, two groups; T-Test: Two numbers; Chi-Square: Two groups, both categories

• Correlation: Two groups, both categories; T-Test: One number, two groups; Chi-Square: Two numbers

Multiple Choice

Which of the following best describes the role of the equation Y = mx + b in regression analysis?

• It is used to calculate averages.

• It is the equation for a line used to predict outcomes based on variables.

• It is only used in geometry, not statistics.

• It is the formula for probability.

Multiple Choice

What does the 'm' represent in the equation Y = mx + b, and how does it affect the value of Y?

• The y-intercept; it shifts the line up or down.

• The dependent variable; it is the outcome.

• The slope; it tells how much Y changes for each 1-unit change in X.

• The independent variable; it is the predictor.

Open Ended

Explain the meaning of the intercept in the regression equation Blood Sugar = 0.8·Weight + 90. Why might this value not be meaningful in real life?

Multiple Choice

In the linear regression formula for predicting blood sugar, which variables are considered predictors, and what type of variable is 'neighborhood'?

• Weight, calories consumed, and neighborhood are predictors; neighborhood is a categorical variable.

• Only weight is a predictor; neighborhood is a numeric variable.

• Calories consumed is the only predictor; neighborhood is a numeric variable.

• Weight and calories consumed are predictors; neighborhood is not included.

Multiple Choice

Which statistical test would you use to examine the effect of neighborhood on blood sugar if you only had two variables?

• Correlation

• Chi-Square

• T-Test

• Regression

Multiple Choice

Which of the following is NOT a question that correlations, t-tests, and chi-square tests help us answer?

• Does one thing affect another?

• Are groups different?

• Is there a pattern?

• What is the weather today?

Open Ended

How do correlations, t-tests, and chi-square tests help us understand relationships in data?

Multiple Choice

What is the purpose of using JAMOVIA in the context of linear regression analysis as described in the slide?

• To provide a front end software package for 'R' to perform in-depth comparisons of equations for a line and linear regression using real world data

• To create visualizations for data analysis only

• To replace the need for statistical software like 'R'

• To collect real world data automatically