Module 3

Question 1

What does a T-Test help us compare?

Answer 1

Average of 2 groups

The test gives us t-Statistic

Question 2

What is the Null Hypothesis in a T-Test?

Answer 2

We haven’t found anything significant

Question 3

What is the P-Value?

Answer 3

Probability results are by chance.

P-Value is less than 0.05

Small (< 0.05): Significant, reject null.
Large (> 0.05): Not significant, fail to reject.

Question 4

What does ANOVA compare?

Answer 4

Average of 3 groups

The ANOVA gives us F-Statistics

Question 5

What does F-Statistics measure?

Answer 5

Compares variation between groups to within groups.

Question 6

What does a Chi Squared Test, test

Answer 6

Checks if there’s a relationship between categorical variables.
KEYWORD: frequency

Question 7

What does Linear Regression analyze and predict?

Answer 7

The relationship between 2 variables (dependent and independent). KEYWORD: relationship

Question 8

What does Multiple Regression predict?

Answer 8

One outcome using two or more influencing factors.

Equation: y = mx1 + mx2 + b
mx2 is the coefficient Int.

Question 9

What is Autocorrelation in time series analysis?

Answer 9

Current data points are affected by previous ones.

Question 10

What is the purpose of Cluster Analysis?

Answer 10

Groups data points based on similarities for easier analysis and decision-making. KEYWORD:

Groups form naturally

Question 11

What does Break-Even Analysis identify?

Answer 11

How much needs to be sold to cover all costs, with no profit or loss.

Question 12

What is Cross Over Analysis?

Answer 12

compare 2, 3, 4 or more options for the least cost at which volume. KEYWORD: Optimal, Volume.

Question 13

What does Linear Programming optimize?

Answer 13

Outcomes (profit/cost) under constraints like limited resources. KEYWORD: Product Mix

Question 14

What must the P-value be to To reject the Null Hypothesis?

Answer 14

Less than 0.05

Question 15

Confidence Level is…

Answer 15

always 95%

Question 16

Significant Level is…

Answer 16

0.05%

Question 17

Homoscedasticity -

Answer 17

Constant error variance across data points in regression.

Question 18

Homoscedasticity -

Answer 18

Constant error variance across data points in regression.

Question 19

Heteroscedasticity -

Answer 19

Varying error variance across data points in regression

Question 20

Regression Equation:

Answer 20

y = mx + b.
* “y” is what we are predicting
* “m” is the coefficient completion
* “x” is whatever the completions are
* “b” is the coefficient intercept

Question 21

Multiple Regression Equation:

Answer 21

y = mx1 + mx2 + b
mx2 is the coefficient Int.