Designing, Running, and Analyzing Experiments(Interaction Design Specialization)

Understanding the Basics :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026

Question 1 Why can’t we confidently compare only means? ❌ We can, provided the means are really big❌ We can, provided the means are really different❌ We can, but we need to know every score✅ We can, but we need to know the spread of scores around those means as well❌ None of the above… <a href="https://codeshala.io/platform/coursera/course/designing-running-and-analyzing-experimentsinteraction-design-specialization/assignment/understanding-the-basics-designing-running-and-analyzing-experimentsinteraction-design-specialization-answers-2026/" rel="bookmark">Understanding the Basics :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026</a>

Understanding Tests of Proportions :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026

Question 1 Why recode Subject column using factor()? ❌ Nominal, not numeric❌ Categorical, not numeric❌ Nominal, not scalar❌ Categorical, not scalar✅ All of the above Explanation:Subject IDs are categorical identifiers, not quantities, even if encoded as numbers. Question 2 Variable type names that are synonyms (Select all that apply) ✅ Categorical, nominal, factor✅ Ordinal, ordered✅… <a href="https://codeshala.io/platform/coursera/course/designing-running-and-analyzing-experimentsinteraction-design-specialization/assignment/understanding-tests-of-proportions-designing-running-and-analyzing-experimentsinteraction-design-specialization-answers-2026/" rel="bookmark">Understanding Tests of Proportions :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026</a>

Doing Tests of Proportions :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026

Question 1 Why recode Subject column using factor()? ❌ Nominal, not numeric❌ Categorical, not numeric❌ Nominal, not scalar❌ Categorical, not scalar✅ All of the above Explanation:Subject IDs are categorical identifiers, not quantities, even if encoded as numbers. Question 2 Variable type names that are synonyms (Select all that apply) ✅ Categorical, nominal, factor✅ Ordinal, ordered✅… <a href="https://codeshala.io/platform/coursera/course/designing-running-and-analyzing-experimentsinteraction-design-specialization/assignment/doing-tests-of-proportions-designing-running-and-analyzing-experimentsinteraction-design-specialization-answers-2026/" rel="bookmark">Doing Tests of Proportions :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026</a>

Understanding Experiment Designs :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026

Question 1 What might account for random error in an experimental measure? ✅ Natural variation among and within subjects❌ A systematic flaw in the logging software❌ A pattern of dropped data for every fifth subject❌ Biased observations❌ None of the above Explanation:Random error comes from unpredictable natural variability, not from consistent or biased problems (those… <a href="https://codeshala.io/platform/coursera/course/designing-running-and-analyzing-experimentsinteraction-design-specialization/assignment/understanding-experiment-designs-designing-running-and-analyzing-experimentsinteraction-design-specialization-answers-2026/" rel="bookmark">Understanding Experiment Designs :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026</a>

Understanding Validity :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026

Question 1 What is experimental control? ❌ Ensuring nothing happens without the experimenter knowing❌ Ensuring every subject experiences every condition❌ Ensuring measures are made correctly and precisely✅ Ensuring that systematic differences in observed responses can be attributed to systematic changes in manipulated factors❌ None of the above Explanation:Experimental control is about isolating cause and effect… <a href="https://codeshala.io/platform/coursera/course/designing-running-and-analyzing-experimentsinteraction-design-specialization/assignment/understanding-validity-designing-running-and-analyzing-experimentsinteraction-design-specialization-answers-2026/" rel="bookmark">Understanding Validity :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026</a>

Doing Tests of Assumptions :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026

Q1. Number of subjects data <- read.csv("designtime.csv") nrow(data) 👉 Enter the total number of rows. Q2. Boxplot & visual comparison boxplot(Time ~ Tool, data = data, xlab = "Tool", ylab = "Time (minutes)", main = "Task Time by Design Tool") 👉 Look at: Which box has a higher median line Whether the spread (IQR +… <a href="https://codeshala.io/platform/coursera/course/designing-running-and-analyzing-experimentsinteraction-design-specialization/assignment/doing-tests-of-assumptions-designing-running-and-analyzing-experimentsinteraction-design-specialization-answers-2026/" rel="bookmark">Doing Tests of Assumptions :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026</a>

Understanding Oneway Designs :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026

Question 1 The issue that requires an experimenter to use a oneway ANOVA instead of a t-test is when there are more than two response categories available. ✅ True❌ False Explanation:A t-test compares only two groups. When there are more than two groups (levels), a oneway ANOVA is required to control Type I error. Question… <a href="https://codeshala.io/platform/coursera/course/designing-running-and-analyzing-experimentsinteraction-design-specialization/assignment/understanding-oneway-designs-designing-running-and-analyzing-experimentsinteraction-design-specialization-answers-2026/" rel="bookmark">Understanding Oneway Designs :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026</a>

Doing Oneway ANOVAs :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026

Q1. Number of stroke alphabets Command: length(unique(data$Alphabet)) 👉 Look at the single number printed in console👉 Enter that number👉 This equals the total number of stroke alphabets compared Q2. Average WPM for EdgeWrite Command: mean(data$WPM[data$Alphabet == "EdgeWrite"]) 👉 R prints one numeric value👉 Round to 2 decimal places👉 Enter that rounded value Example: 12.3467 →… <a href="https://codeshala.io/platform/coursera/course/designing-running-and-analyzing-experimentsinteraction-design-specialization/assignment/doing-oneway-anovas-designing-running-and-analyzing-experimentsinteraction-design-specialization-answers-2026/" rel="bookmark">Doing Oneway ANOVAs :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026</a>

Understanding Oneway Repeated Measures Designs :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026

Question 1 What primarily distinguishes a oneway repeated measures ANOVA from a oneway ANOVA? ❌ The presence of multiple factors.❌ The presence of a between-subjects factor.✅ The presence of a within-subjects factor.❌ The presence of both between- and within-subjects factors.❌ None of the above. Explanation:A oneway repeated measures ANOVA is used when the same subjects… <a href="https://codeshala.io/platform/coursera/course/designing-running-and-analyzing-experimentsinteraction-design-specialization/assignment/understanding-oneway-repeated-measures-designs-designing-running-and-analyzing-experimentsinteraction-design-specialization-answers-2026/" rel="bookmark">Understanding Oneway Repeated Measures Designs :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026</a>

Doing Oneway Repeated Measures ANOVAs :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026

Q1. Number of participants data2 <- read.csv("websearch2.csv") length(unique(data2$Participant)) 👉 Enter the printed number Q2. Highest average number of searches aggregate(Searches ~ Engine, data2, mean) 👉 Identify the largest mean👉 Round to 2 decimals Q3. Order effect on Searches (paired t-test) Convert to wide format: wide_order <- dcast(data2, Participant ~ Order, value.var = "Searches") t.test(wide_order[,2], wide_order[,3],… <a href="https://codeshala.io/platform/coursera/course/designing-running-and-analyzing-experimentsinteraction-design-specialization/assignment/doing-oneway-repeated-measures-anovas-designing-running-and-analyzing-experimentsinteraction-design-specialization-answers-2026/" rel="bookmark">Doing Oneway Repeated Measures ANOVAs :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026</a>

Understanding Factorial Designs :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026

Question 1 Interaction effects explore which of the following? ❌ How the response changes as factors change❌ How the response is affected by levels of a factor✅ How the response is differentially affected by levels of one factor based on levels of another factor❌ How the response is differentially affected by levels of one factor… <a href="https://codeshala.io/platform/coursera/course/designing-running-and-analyzing-experimentsinteraction-design-specialization/assignment/understanding-factorial-designs-designing-running-and-analyzing-experimentsinteraction-design-specialization-answers-2026/" rel="bookmark">Understanding Factorial Designs :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026</a>

Doing Factorial ANOVAs :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026

Q1. Identify experimental design Check: table(data$Height, data$Avatar) Decision logic: Each participant appears once only → between-subjects Appears multiple times → within / mixed Q2. Number of subjects length(unique(data$Subject)) Q3. Most positive Height × Avatar mean aggregate(Positives ~ Height + Avatar, data, mean) 👉 Take largest mean, round to 2 decimals Q4–Q5. Interaction plots interaction.plot(data$Height, data$Avatar,… <a href="https://codeshala.io/platform/coursera/course/designing-running-and-analyzing-experimentsinteraction-design-specialization/assignment/doing-factorial-anovas-designing-running-and-analyzing-experimentsinteraction-design-specialization-answers-2026/" rel="bookmark">Doing Factorial ANOVAs :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026</a>

Understanding Generalized Linear Models :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026

Question 1 What do generalized linear models (GLMs) generalize? ✅ The linear model, which encompasses the ANOVA❌ The linear model, which is a subset of the ANOVA❌ The general model, which supersedes the ANOVA❌ The general model, which is a subset of the ANOVA❌ None of the above Explanation:GLMs extend the linear model (LM), which… <a href="https://codeshala.io/platform/coursera/course/designing-running-and-analyzing-experimentsinteraction-design-specialization/assignment/understanding-generalized-linear-models-designing-running-and-analyzing-experimentsinteraction-design-specialization-answers-2026/" rel="bookmark">Understanding Generalized Linear Models :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026</a>