Chi-Square Test Concepts and Applications

What type of data is typically analyzed using a Chi-square model?

• Continuous variables like height and IQ.

• Categorical variables like hair color or academic degree.

• Numerical data with a normal distribution.

• Time-series data showing trends over time.

A video game developer claims that 15% of players choose healers, 20% choose tanks, 20% choose assassins, and 45% choose fighters. What is the null hypothesis for testing this claim using a Chi-square goodness of fit test?

• The observed player distribution is significantly different from the claimed percentages.

• At least one of the claimed percentages for player choices is incorrect.

• The actual player distribution matches the claimed percentages.

• The game's popularity is declining.

In a Chi-square test, why is the difference between observed and expected values squared before summing?

• To simplify the calculation process.

• To ensure all differences are positive, preventing them from canceling each other out.

• To give more weight to smaller differences.

• To convert the values into percentages.

If a Chi-square goodness of fit test is performed on data with 5 distinct categories, what are the degrees of freedom for this test?

• 5

• 4

• 6

• 1

Which statement accurately describes a Goodness of Fit Chi-Square test?

• It analyzes the relationship between two or more variables.

• It determines if observed frequencies fit an expected distribution for a single variable.

• It requires all expected frequencies to be exactly equal.

• It is used exclusively for continuous data.

For the results of a Chi-Square test to be considered reliable, what is the minimum expected frequency for each cell?

• 1

• 5

• 10

• 50

What is the primary purpose of a Chi-Square Test of Independence?

• To compare the means of two different groups.

• To determine if a sample's distribution matches a known population distribution.

• To assess whether two categorical variables are related or independent.

• To predict the value of one variable based on another.

In a Chi-Square test involving a contingency table with 'r' rows and 'c' columns, how are the degrees of freedom calculated?

• r * c

• r + c - 1

• (r - 1) * (c - 1)

• (r + c) / 2