WGU C784 Statistics Module 5 (Latest 2024/ 2025) Actual Exam Questions and Correct Answers

1. When one variable causes change in another, we call the first variable the variable*. The affected variable is called the variable*. Answer: When one variable causes change in another, we call the firstvariable the explanatory variable*. The affected variable is called the response variable*. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The different values of the explanatory variable are called treatments. An experimental unit is a single object or individual to be measured. 2. A two-way table, also known as a two-way frequency table or contingency table, is used to show the relationship between two variables ( C’C ); the rows show the categories of one variable, and the columns show the categories of the othervariable. Answer: -categorical variables ( C’C ) 3. . These represent the total number of instances that fall in both the corresponding row and header. The data in the green cells show. These are equal to the sum of the number of individuals in the corresponding row or column. Answer: The cells in yellow show joint frequencies*. These represent the total number of instances that fall in both the corresponding row and header. For example, data in the "Male" row and "With Autism" column counts the number of males with autism. The data in the green cells show marginal frequencies*. These are equal to the sum of the number of individuals in the corresponding row or column. For example, data in the "Totals" column and "Female" row shows the total number of females in the study. It may be helpful to remember that marginal frequencies appear in the margins of the table. The bottom, right cell (in both the "Totals" column and the "Totals" row) measures the total number of individuals in the study. 4. The relationship between two variables that are both quantitative can be displayed in a . Answer: scatterplot 5. As we've seen earlier, every point on a coordinate plane can be rep- resented by an ordered pair*, ( x , y ). Here, the x -value is typically the variable's value for a piece of data, and the y -value is the corresponding value for the variable. A simple way to remember this fact is that the term "explanatory" has an " x " in it. Answer: explanatory variable; response variable 6. Side-by-side box plots are a good choice for two-variable data where the explanatory variable is data and the response variable is data. Answer: Categorical Quantitative 7. Which variable, explanatory or response, is displayed on the x -axis on side- by-side boxplots? Answer: Side-by-side boxplots can be horizontal or vertical, soeither variable (explanatory or response) can be displayed on the x -axis. 8. A scatterplot is a good choice to display two-variable data that are both variables. Answer: Quantitative 9. The relationship between the x -variable and the y -variable is called . Answer: Correlation 10. What determines the location of a dot on a scatterplot? Answer: A dot is placed on a scatterplot according to its x - and y -value. 11. When analyzing a possible relationship for two-variable data, if both variables are categorical, what is the most appropriate choice to display the data? a) Side-by-side boxplots b) Scatterplot c) Bar chart d) Two-way frequency table e) Histogram Answer: D A two-way frequency table is the most appropriate way to graphically display apossible relationship for two-variable data, when both variables are categorical. 12. A hospital hires an independent consulting firm to perform a study about patients with high blood pressure, and the medicine they are being pre- scribed. The study is examining the relationship between a patient's starting blood pressurewhen they entered the treatment program and the dosage of blood pressure medicine they are prescribed during their treatment. For this study: What is the explanatory variable? Is the explanatory variable categorical or quantitative? What is the response variable? Is it categorical or quantitative? What graphical display should be used to show the results of the study? Answer: The explanatory variable is patient's starting blood pressure. The explanatory variable is a quantitative variable. The response variable is the dosage of blood pressure medicine they are pre-scribed. The response variable is also a quantitative variable. As both the explanatory and response variables are quantitative (Q’Q) , a scatter- plot would be an appropriate graphical display. 13. When working with two-variable data, if the explanatory variable is categorical and the response variable is quantitative, what is the most appropriate choice to display the data? a) Side-by-side boxplots b) Scatterplot c) Bar chart d) Two-way frequency table e) Histogram Answer: A When working with two-variable data, if one variable is categorical and the other is quantitative, a side-by-side boxplot is the most appropriate way to display the data. 14. 13. When working with two-variable data, if both variables are quantitative, what is the most appropriate choice to display the data? a) Side-by-side boxplots b) Scatterplot c) Bar chart d) Two-way frequency table e) Histogram Answer: B When working with two-variable data, if both variables are quantitative, a scatter- plot is the most appropriate choice to display the data. 15. In a two-way table, what does the sum of the joint frequencies in one row equal? a) The quantitative variable b) A marginal frequency c) The correlation coefficient d) The number of individuals in the placebo group Answer: Correct. The correct answer is b. In a two-way table, the sum of the joint frequencies in one row equals a marginal frequency. 16. If both variables are categorical, a - is used to display the data. Answer: two-way frequency table 17. There are several ways we can analyze the data presented in this table. If we calculate the percentage that each cell is of the total, the results are called relative frequencies. When the relative frequencies are calculated from the row total or the column total, they are called . Answer: conditional percentages. 18. Each row is a different gender. If we are trying to see if gender influences the choice of exercise program, then gender is the explanatory variable. In this case, we are calculating the relative frequency by rows; that is, we are calculating the relative frequency by gender. To determine relative frequency for women, we divide the data in the top row by the total number of women. To determine the relative frequency for men, we divide the data in the second row by the total number of men. The percentages obtained are called . Answer: conditional row percentages. 19. Each column is a different exercise program. If we are trying to deter- mine how each exercise program is appealing to different genders, then the exercise program becomes the explanatory variable. In this case, the explanatory variable is in the columns, so we will be calculating the relative frequency by columns, thatis, we are calculating the relative frequency by exercise program. To determine relative frequency for each cell, we divide the data by the corresponding column's total number of individuals. The percentages obtained are called . Answer: conditional column percentages 20. If the explanatory variable is categorical and the response vari- able is quantitative, we can use descriptive statistics, namely the for the quantitative variable, and compare the statistics for each of the categories Answer: five-number summary 21. for the majority of the data points, there is a linear relationship indicating a *positive correlation*, meaning: Answer: When two quantitative variables move in the same direction; meaning that as one variable (response variable ) increases, the other variable (explanatoryvariable) increases. 22. If the relationship is linear, the strength of the correlation (linear relation-ship) can be measured using a statistic called the Answer: correlation coefficient*. A correlation coefficient is a number that falls somewhere from1 to 1 . A measure of the linear relationship between two attributes. The numerical value demonstrates how closely the attributes vary together.Correlation coefficients near -1 and +1 have strong linear correlation, while a correlation coefficient near 0 has weak (or no) linear correlation.